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## Publications of Guillermo Sapiro    :chronological  alphabetical  combined listing:

%% Papers Published
@article{fds335963,
Author = {Campbell, K and Carpenter, KL and Hashemi, J and Espinosa, S and Marsan,
S and Borg, JS and Chang, Z and Qiu, Q and Vermeer, S and Adler, E and Tepper,
M and Egger, HL and Baker, JP and Sapiro, G and Dawson,
G},
Title = {Computer vision analysis captures atypical attention in
toddlers with autism.},
Journal = {Autism},
Volume = {23},
Number = {3},
Pages = {619-628},
Year = {2019},
Month = {April},
url = {http://dx.doi.org/10.1177/1362361318766247},
Abstract = {To demonstrate the capability of computer vision analysis to
detect atypical orienting and attention behaviors in
toddlers with autism spectrum disorder. One hundered and
four toddlers of 16-31 months old (mean = 22)
participated in this study. Twenty-two of the toddlers had
autism spectrum disorder and 82 had typical development or
developmental delay. Toddlers watched video stimuli on a
tablet while the built-in camera recorded their head
movement. Computer vision analysis measured participants'
attention and orienting in response to name calls.
Reliability of the computer vision analysis algorithm was
tested against a human rater. Differences in behavior were
analyzed between the autism spectrum disorder group and the
comparison group. Reliability between computer vision
analysis and human coding for orienting to name was
excellent (intra-class coefficient 0.84, 95% confidence
interval 0.67-0.91). Only 8% of toddlers with autism
spectrum disorder oriented to name calling on >1 trial,
compared to 63% of toddlers in the comparison group
(p = 0.002). Mean latency to orient was significantly
longer for toddlers with autism spectrum disorder (2.02 vs
1.06 s, p = 0.04). Sensitivity for autism spectrum
disorder of atypical orienting was 96% and specificity was
38%. Older toddlers with autism spectrum disorder showed
less attention to the videos overall (p = 0.03).
Automated coding offers a reliable, quantitative method for
detecting atypical social orienting and reduced sustained
attention in toddlers with autism spectrum
disorder.},
Doi = {10.1177/1362361318766247},
Key = {fds335963}
}

@article{fds341551,
Author = {Shamir, RR and Duchin, Y and Kim, J and Patriat, R and Marmor, O and Bergman, H and Vitek, JL and Sapiro, G and Bick, A and Eliahou, R and Eitan, R and Israel, Z and Harel, N},
Title = {Microelectrode Recordings Validate the Clinical
Visualization of Subthalamic-Nucleus Based on 7T Magnetic
Resonance Imaging and Machine Learning for Deep Brain
Stimulation Surgery.},
Journal = {Neurosurgery},
Volume = {84},
Number = {3},
Pages = {749-757},
Year = {2019},
Month = {March},
url = {http://dx.doi.org/10.1093/neuros/nyy212},
Abstract = {BACKGROUND:Deep brain stimulation (DBS) of the subthalamic
nucleus (STN) is a proven and effective therapy for the
management of the motor symptoms of Parkinson's disease
(PD). While accurate positioning of the stimulating
electrode is critical for success of this therapy, precise
identification of the STN based on imaging can be
challenging. We developed a method to accurately visualize
the STN on a standard clinical magnetic resonance imaging
(MRI). The method incorporates a database of 7-Tesla (T)
MRIs of PD patients together with machine-learning methods
(hereafter 7 T-ML). OBJECTIVE:To validate the clinical
application accuracy of the 7 T-ML method by comparing it
with identification of the STN based on intraoperative
microelectrode recordings. METHODS:Sixteen PD patients who
underwent microelectrode-recordings guided STN DBS were
included in this study (30 implanted leads and electrode
trajectories). The length of the STN along the electrode
trajectory and the position of its contacts to dorsal,
inside, or ventral to the STN were compared using
microelectrode-recordings and the 7 T-ML method computed
based on the patient's clinical 3T MRI. RESULTS:All 30
electrode trajectories that intersected the STN based on
microelectrode-recordings, also intersected it when
visualized with the 7 T-ML method. STN trajectory average
length was 6.2 ± 0.7 mm based on microelectrode
recordings and 5.8 ± 0.9 mm for the 7 T-ML method. We
observed a 93% agreement regarding contact location between
the microelectrode-recordings and the 7 T-ML method.
CONCLUSION:The 7 T-ML method is highly consistent with
microelectrode-recordings data. This method provides a
reliable and accurate patient-specific prediction for
targeting the STN.},
Doi = {10.1093/neuros/nyy212},
Key = {fds341551}
}

@article{fds341351,
Author = {Dawson, G and Sapiro, G},
Title = {Potential for Digital Behavioral Measurement Tools to
Transform the Detection and Diagnosis of Autism Spectrum
Disorder.},
Journal = {Jama Pediatr},
Year = {2019},
Month = {February},
url = {http://dx.doi.org/10.1001/jamapediatrics.2018.5269},
Doi = {10.1001/jamapediatrics.2018.5269},
Key = {fds341351}
}

@article{fds339597,
Author = {Kim, J and Duchin, Y and Shamir, RR and Patriat, R and Vitek, J and Harel,
N and Sapiro, G},
Title = {Automatic localization of the subthalamic nucleus on
patient-specific clinical MRI by incorporating 7 T MRI and
machine learning: Application in deep brain
stimulation.},
Journal = {Human Brain Mapping},
Volume = {40},
Number = {2},
Pages = {679-698},
Year = {2019},
Month = {February},
url = {http://dx.doi.org/10.1002/hbm.24404},
Abstract = {Deep brain stimulation (DBS) of the subthalamic nucleus
(STN) has shown clinical potential for relieving the motor
symptoms of advanced Parkinson's disease. While accurate
localization of the STN is critical for consistent
across-patients effective DBS, clear visualization of the
STN under standard clinical MR protocols is still
challenging. Therefore, intraoperative microelectrode
recordings (MER) are incorporated to accurately localize the
STN. However, MER require significant neurosurgical
expertise and lengthen the surgery time. Recent advances in
7 T MR technology facilitate the ability to clearly
visualize the STN. The vast majority of centers, however,
still do not have 7 T MRI systems, and fewer have the
ability to collect and analyze the data. This work
introduces an automatic STN localization framework based on
standard clinical MRIs without additional cost in the
current DBS planning protocol. Our approach benefits from a
large database of 7 T MRI and its clinical MRI pairs. We
first model in the 7 T database, using efficient machine
learning algorithms, the spatial and geometric dependency
between the STN and its adjacent structures (predictors).
Given a standard clinical MRI, our method automatically
computes the predictors and uses the learned information to
predict the patient-specific STN. We validate our proposed
method on clinical T2 W MRI of 80 subjects, comparing with
experts-segmented STNs from the corresponding 7 T MRI pairs.
The experimental results show that our framework provides
more accurate and robust patient-specific STN localization
than using state-of-the-art atlases. We also demonstrate the
clinical feasibility of the proposed technique assessing the
post-operative electrode active contact locations.},
Doi = {10.1002/hbm.24404},
Key = {fds339597}
}

@article{fds342170,
Author = {Lezama, J and Qiu, Q and Musé, P and Sapiro, G},
Title = {OLE: Orthogonal Low-rank Embedding, A Plug and Play
Geometric Loss for Deep Learning},
Journal = {Proceedings of the Ieee Computer Society Conference on
Computer Vision and Pattern Recognition},
Pages = {8109-8118},
Year = {2018},
Month = {December},
url = {http://dx.doi.org/10.1109/CVPR.2018.00846},
Abstract = {© 2018 IEEE. Deep neural networks trained using a softmax
layer at the top and the cross-entropy loss are ubiquitous
tools for image classification. Yet, this does not naturally
enforce intra-class similarity nor inter-class margin of the
learned deep representations. To simultaneously achieve
these two goals, different solutions have been proposed in
the literature, such as the pairwise or triplet losses.
However, these carry the extra task of selecting pairs or
triplets, and the extra computational burden of computing
and learning for many combinations of them. In this paper,
we propose a plug-and-play loss term for deep networks that
explicitly reduces intra-class variance and enforces
inter-class margin simultaneously, in a simple and elegant
geometric manner. For each class, the deep features are
collapsed into a learned linear subspace, or union of them,
and inter-class subspaces are pushed to be as orthogonal as
possible. Our proposed Orthogonal Low-rank Embedding
(OLÃ‰) does not require carefully crafting pairs or
triplets of samples for training, and works standalone as a
classification loss, being the first reported deep metric
learning framework of its kind. Because of the improved
margin between features of different classes, the resulting
deep networks generalize better, are more discriminative,
and more robust. We demonstrate improved classification
performance in general object recognition, plugging the
proposed loss term into existing off-the-shelf
architectures. In particular, we show the advantage of the
proposed loss in the small data/model scenario, and we
significantly advance the state-of-the-art on the Stanford
STL-10 benchmark.},
Doi = {10.1109/CVPR.2018.00846},
Key = {fds342170}
}

@article{fds342169,
Author = {Zhu, W and Qiu, Q and Huang, J and Calderbank, R and Sapiro, G and Daubechies, I},
Title = {LDMNet: Low Dimensional Manifold Regularized Neural
Networks},
Journal = {Proceedings of the Ieee Computer Society Conference on
Computer Vision and Pattern Recognition},
Pages = {2743-2751},
Year = {2018},
Month = {December},
url = {http://dx.doi.org/10.1109/CVPR.2018.00290},
Abstract = {© 2018 IEEE. Deep neural networks have proved very
successful on archetypal tasks for which large training sets
are available, but when the training data are scarce, their
performance suffers from overfitting. Many existing methods
of reducing overfitting are data-independent. Data-dependent
regularizations are mostly motivated by the observation that
data of interest lie close to a manifold, which is typically
hard to parametrize explicitly. These methods usually only
focus on the geometry of the input data, and do not
necessarily encourage the networks to produce geometrically
meaningful features. To resolve this, we propose the
Low-Dimensional-Manifold-regularized neural Network
(LDMNet), which incorporates a feature regularization method
that focuses on the geometry of both the input data and the
output features. In LDMNet, we regularize the network by
encouraging the combination of the input data and the output
features to sample a collection of low dimensional
manifolds, which are searched efficiently without explicit
parametrization. To achieve this, we directly use the
manifold dimension as a regularization term in a variational
functional. The resulting Euler-Lagrange equation is a
Laplace-Beltrami equation over a point cloud, which is
solved by the point integral method without increasing the
computational complexity. In the experiments, we show that
LDMNet significantly outperforms widely-used regularizers.
Moreover, LDMNet can extract common features of an object
imaged via different modalities, which is very useful in
real-world applications such as cross-spectral face
recognition.},
Doi = {10.1109/CVPR.2018.00290},
Key = {fds342169}
}

@article{fds339768,
Author = {Dawson, G and Campbell, K and Hashemi, J and Lippmann, SJ and Smith, V and Carpenter, K and Egger, H and Espinosa, S and Vermeer, S and Baker, J and Sapiro, G},
Title = {Atypical postural control can be detected via computer
vision analysis in toddlers with autism spectrum
disorder.},
Journal = {Scientific Reports},
Volume = {8},
Number = {1},
Pages = {17008},
Year = {2018},
Month = {November},
url = {http://dx.doi.org/10.1038/s41598-018-35215-8},
Abstract = {Evidence suggests that differences in motor function are an
early feature of autism spectrum disorder (ASD). One aspect
of motor ability that develops during childhood is postural
and body position without excessive sway. Observational
studies have documented differences in postural control in
older children with ASD. The present study used computer
vision analysis to assess midline head postural control, as
reflected in the rate of spontaneous head movements during
states of active attention, in 104 toddlers between 16-31
months of age (Mean = 22 months), 22 of whom were
diagnosed with ASD. Time-series data revealed robust group
differences in the rate of head movements while the toddlers
watched movies depicting social and nonsocial stimuli.
Toddlers with ASD exhibited a significantly higher rate of
head movement as compared to non-ASD toddlers, suggesting
difficulties in maintaining midline position of the head
while engaging attentional systems. The use of digital
phenotyping approaches, such as computer vision analysis, to
quantify variation in early motor behaviors will allow for
more precise, objective, and quantitative characterization
of early motor signatures and potentially provide new
automated methods for early autism risk identification.},
Doi = {10.1038/s41598-018-35215-8},
Key = {fds339768}
}

@article{fds339259,
Author = {Aguerrebere, C and Delbracio, M and Bartesaghi, A and Sapiro,
G},
Title = {A Practical Guide to Multi-Image Alignment},
Journal = {2015 Ieee International Conference on Acoustics, Speech, and
Signal Processing (Icassp)},
Volume = {2018-April},
Pages = {1927-1931},
Publisher = {IEEE},
Year = {2018},
Month = {September},
url = {http://dx.doi.org/10.1109/ICASSP.2018.8461588},
Abstract = {© 2018 IEEE. Multi-image alignment, bringing a group of
images into common register, is an ubiquitous problem and
the first step of many applications in a wide variety of
domains. As a result, a great amount of effort is being
invested in developing efficient multi-image alignment
algorithms. Little has been done, however, to answer
fundamental practical questions such as: what is the
comparative performance of existing methods? is there still
room for improvement? under which conditions should one
technique be preferred over another? does adding more images
or prior image information improve the registration results?
In this work, we present a thorough analysis and evaluation
of the main multi-image alignment methods which, combined
with theoretical limits in multi-image alignment
performance, allows us to organize them under a common
framework and provide practical answers to these essential
questions.},
Doi = {10.1109/ICASSP.2018.8461588},
Key = {fds339259}
}

@article{fds339260,
Author = {Ahn, HK and Qiu, Q and Bosch, E and Thompson, A and Robles, FE and Sapiro,
G and Warren, WS and Calderbank, R},
Title = {Classifying Pump-Probe Images of Melanocytic Lesions Using
the WEYL Transform},
Journal = {2015 Ieee International Conference on Acoustics, Speech, and
Signal Processing (Icassp)},
Volume = {2018-April},
Pages = {4209-4213},
Publisher = {IEEE},
Year = {2018},
Month = {September},
ISBN = {9781538646588},
url = {http://dx.doi.org/10.1109/ICASSP.2018.8461298},
Abstract = {© 2018 IEEE. Diagnosis of melanoma is fraught with
uncertainty, and discordance rates among physicians remain
high because of the lack of a definitive criterion.
Motivated by this challenge, this paper first introduces the
Patch Weyl transform (PWT), a 2-dimensional variant of the
Weyl transform. It then presents a method for classifying
pump-probe images of melanocytic lesions based on the PWT
coefficients. Performance of the PWT coefficients is shown
to be superior to classification based on baseline
intensity, on standard descriptors such as the Histogram of
Oriented Gradients (HOG) and Local Binary Patterns (LBP),
and on coefficients derived from PCA and Fourier
representations of the data.},
Doi = {10.1109/ICASSP.2018.8461298},
Key = {fds339260}
}

@article{fds339261,
Author = {Giryes, R and Eldar, YC and Bronstein, AM and Sapiro,
G},
Title = {The Learned Inexact Project Gradient Descent
Algorithm},
Journal = {2015 Ieee International Conference on Acoustics, Speech, and
Signal Processing (Icassp)},
Volume = {2018-April},
Pages = {6767-6771},
Publisher = {IEEE},
Year = {2018},
Month = {September},
ISBN = {9781538646588},
url = {http://dx.doi.org/10.1109/ICASSP.2018.8462136},
Abstract = {© 2018 IEEE. Accelerating iterative algorithms for solving
inverse problems using neural networks have become a very
popular strategy in the recent years. In this work, we
propose a theoretical analysis that may provide an
explanation for its success. Our theory relies on the usage
of inexact projections with the projected gradient descent
(PGD) method. It is demonstrated in various problems
including image super-resolution.},
Doi = {10.1109/ICASSP.2018.8462136},
Key = {fds339261}
}

@article{fds338014,
Author = {Hashemi, J and Dawson, G and Carpenter, KLH and Campbell, K and Qiu, Q and Espinosa, S and Marsan, S and Baker, JP and Egger, HL and Sapiro,
G},
Title = {Computer Vision Analysis for Quantification of Autism Risk
Behaviors},
Journal = {Ieee Transactions on Affective Computing},
Pages = {1-1},
Publisher = {Institute of Electrical and Electronics Engineers
(IEEE)},
Year = {2018},
Month = {August},
url = {http://dx.doi.org/10.1109/TAFFC.2018.2868196},
Abstract = {IEEE Observational behavior analysis plays a key role for
the discovery and evaluation of risk markers for many
neurodevelopmental disorders. Research on autism spectrum
disorder (ASD) suggests that behavioral risk markers can be
observed at 12 months of age, with diagnosis possible at 18
months. To date, studies and evaluations involving
observational analysis tend to rely heavily on clinical
practitioners and specialists who have undergone intensive
training to be able to reliably administer carefully
designed behavioral-eliciting tasks, code the resulting
behaviors, and interpret them. These methods are therefore
extremely expensive, time-intensive, and are not easily
scalable for large or longitudinal observational analysis.
We developed a self-contained, closed-loop, mobile
application with movie stimuli designed to engage the
child&#x0027;s attention and elicit specific behavioral and
social responses, which are recorded with the mobile
device&#x0027;s camera and analyzed via computer vision
we validate the system to measure engagement, name-call, and
emotional responses of toddlers with and without ASD who
were presented with the application. Additionally, we
demonstrate how the proposed framework can further risk
marker research with fine-grained quantification of
behaviors. The results suggest these objective and automatic
methods can be considered to aid behavioral
analysis.},
Doi = {10.1109/TAFFC.2018.2868196},
Key = {fds338014}
}

@article{fds335962,
Author = {Bartesaghi, A and Aguerrebere, C and Falconieri, V and Banerjee, S and Earl, LA and Zhu, X and Grigorieff, N and Milne, JLS and Sapiro, G and Wu,
X and Subramaniam, S},
Title = {Atomic Resolution Cryo-EM Structure of β-Galactosidase.},
Journal = {Structure (London, England : 1993)},
Volume = {26},
Number = {6},
Pages = {848-856.e3},
Year = {2018},
Month = {June},
url = {http://dx.doi.org/10.1016/j.str.2018.04.004},
Abstract = {The advent of direct electron detectors has enabled the
routine use of single-particle cryo-electron microscopy (EM)
approaches to determine structures of a variety of protein
complexes at near-atomic resolution. Here, we report the
development of methods to account for local variations in
defocus and beam-induced drift, and the implementation of a
data-driven dose compensation scheme that significantly
improves the extraction of high-resolution information
recorded during exposure of the specimen to the electron
beam. These advances enable determination of a cryo-EM
density map for β-galactosidase bound to the inhibitor
phenylethyl β-D-thiogalactopyranoside where the ordered
regions are resolved at a level of detail seen in X-ray maps
at ∼ 1.5 Å resolution. Using this density map in
conjunction with constrained molecular dynamics simulations
provides a measure of the local flexibility of the
non-covalently bound inhibitor and offers further
opportunities for structure-guided inhibitor
design.},
Doi = {10.1016/j.str.2018.04.004},
Key = {fds335962}
}

@article{fds332366,
Author = {Giryes, R and Eldar, YC and Bronstein, AM and Sapiro,
G},
Title = {Tradeoffs between convergence speed and reconstruction
accuracy in inverse problems},
Journal = {Ieee Transactions on Signal Processing},
Volume = {66},
Number = {7},
Pages = {1676-1690},
Publisher = {Institute of Electrical and Electronics Engineers
(IEEE)},
Year = {2018},
Month = {April},
url = {http://dx.doi.org/10.1109/TSP.2018.2791945},
Abstract = {© 2018 IEEE. Solving inverse problems with iterative
algorithms is popular, especially for large data. Due to
time constraints, the number of possible iterations is
usually limited, potentially affecting the achievable
accuracy. Given an error one is willing to tolerate, an
important question is whether it is possible to modify the
original iterations to obtain faster convergence to a
minimizer achieving the allowed error without increasing the
computational cost of each iteration considerably. Relying
on recent recovery techniques developed for settings in
which the desired signal belongs to some low-dimensional
set, we show that using a coarse estimate of this set may
reconstruction error related to the accuracy of the set
approximation. Our theory ties to recent advances in sparse
recovery, compressed sensing, and deep learning.
Particularly, it may provide a possible explanation to the
successful approximation of the 1 -minimization solution by
neural networks with layers representing iterations, as
practiced in the learned iterative shrinkage-thresholding
algorithm.},
Doi = {10.1109/TSP.2018.2791945},
Key = {fds332366}
}

@article{fds332805,
Author = {Vu, M-AT and Adalı, T and Ba, D and Buzsáki, G and Carlson, D and Heller,
K and Liston, C and Rudin, C and Sohal, VS and Widge, AS and Mayberg, HS and Sapiro, G and Dzirasa, K},
Title = {A Shared Vision for Machine Learning in Neuroscience.},
Journal = {Journal of Neuroscience},
Volume = {38},
Number = {7},
Pages = {1601-1607},
Year = {2018},
Month = {February},
url = {http://dx.doi.org/10.1523/JNEUROSCI.0508-17.2018},
Abstract = {With ever-increasing advancements in technology,
neuroscientists are able to collect data in greater volumes
and with finer resolution. The bottleneck in understanding
how the brain works is consequently shifting away from the
amount and type of data we can collect and toward what we
actually do with the data. There has been a growing interest
in leveraging this vast volume of data across levels of
analysis, measurement techniques, and experimental paradigms
to gain more insight into brain function. Such efforts are
visible at an international scale, with the emergence of big
data neuroscience initiatives, such as the BRAIN initiative
(Bargmann et al., 2014), the Human Brain Project, the Human
Connectome Project, and the National Institute of Mental
Health's Research Domain Criteria initiative. With these
large-scale projects, much thought has been given to
data-sharing across groups (Poldrack and Gorgolewski, 2014;
Sejnowski et al., 2014); however, even with such
data-sharing initiatives, funding mechanisms, and
infrastructure, there still exists the challenge of how to
cohesively integrate all the data. At multiple stages and
levels of neuroscience investigation, machine learning holds
great promise as an addition to the arsenal of analysis
tools for discovering how the brain works.},
Doi = {10.1523/JNEUROSCI.0508-17.2018},
Key = {fds332805}
}

@article{fds327666,
Author = {Pisharady, PK and Sotiropoulos, SN and Duarte-Carvajalino, JM and Sapiro, G and Lenglet, C},
Title = {Estimation of white matter fiber parameters from compressed
multiresolution diffusion MRI using sparse Bayesian
learning.},
Journal = {Neuroimage},
Volume = {167},
Pages = {488-503},
Year = {2018},
Month = {February},
url = {http://dx.doi.org/10.1016/j.neuroimage.2017.06.052},
Abstract = {We present a sparse Bayesian unmixing algorithm BusineX:
Bayesian Unmixing for Sparse Inference-based Estimation of
Fiber Crossings (X), for estimation of white matter fiber
parameters from compressed (under-sampled) diffusion MRI
(dMRI) data. BusineX combines compressive sensing with
linear unmixing and introduces sparsity to the previously
proposed multiresolution data fusion algorithm RubiX,
resulting in a method for improved reconstruction,
especially from data with lower number of diffusion
gradients. We formulate the estimation of fiber parameters
as a sparse signal recovery problem and propose a linear
unmixing framework with sparse Bayesian learning for the
recovery of sparse signals, the fiber orientations and
volume fractions. The data is modeled using a parametric
spherical deconvolution approach and represented using a
dictionary created with the exponential decay components
along different possible diffusion directions. Volume
fractions of fibers along these directions define the
dictionary weights. The proposed sparse inference, which is
based on the dictionary representation, considers the
sparsity of fiber populations and exploits the spatial
redundancy in data representation, thereby facilitating
inference from under-sampled q-space. The algorithm improves
parameter estimation from dMRI through data-dependent local
learning of hyperparameters, at each voxel and for each
possible fiber orientation, that moderate the strength of
priors governing the parameter variances. Experimental
results on synthetic and in-vivo data show improved accuracy
with a lower uncertainty in fiber parameter estimates.
BusineX resolves a higher number of second and third fiber
crossings. For under-sampled data, the algorithm is also
shown to produce more reliable estimates.},
Doi = {10.1016/j.neuroimage.2017.06.052},
Key = {fds327666}
}

@article{fds335964,
Author = {Qiu, Q and Hashemi, J and Sapiro, G},
Title = {Intelligent synthesis driven model calibration: framework
and face recognition application},
Journal = {Proceedings 2017 Ieee International Conference on Computer
Vision Workshops, Iccvw 2017},
Volume = {2018-January},
Pages = {2564-2572},
Publisher = {IEEE},
Year = {2018},
Month = {January},
ISBN = {9781538610343},
url = {http://dx.doi.org/10.1109/ICCVW.2017.301},
Abstract = {© 2017 IEEE. Deep Neural Networks (DNNs) that achieve
state-of-the-art results are still prone to suffer
performance degradation when deployed in many real-world
scenarios due to shifts between the training and deployment
domains. Limited data from a given setting can be enriched
through synthesis, then used to calibrate a pre-trained DNN
to improve the performance in the setting. Most enrichment
approaches try to generate as much data as possible;
however, this blind approach is computationally expensive
and can lead to generating redundant data. Contrary to this,
we develop synthesis, here exemplified for faces, methods
and propose information-driven approaches to exploit and
optimally select face synthesis types both at training and
testing. We show that our approaches, without re-designing a
new DNN, lead to more efficient training and improved
performance. We demonstrate the effectiveness of our
approaches by calibrating a state-of-the-art DNN to two
challenging face recognition datasets.},
Doi = {10.1109/ICCVW.2017.301},
Key = {fds335964}
}

@article{fds335965,
Author = {Sokolić, J and Qiu, Q and Rodrigues, MRD and Sapiro,
G},
Title = {Learning to identify while failing to discriminate},
Journal = {Proceedings 2017 Ieee International Conference on Computer
Vision Workshops, Iccvw 2017},
Volume = {2018-January},
Pages = {2537-2544},
Publisher = {IEEE},
Year = {2018},
Month = {January},
ISBN = {9781538610343},
url = {http://dx.doi.org/10.1109/ICCVW.2017.298},
Abstract = {© 2017 IEEE. Privacy and fairness are critical in computer
vision applications, in particular when dealing with human
identification. Achieving a universally secure, private, and
fair systems is practically impossible as the exploitation
of additional data can reveal private information in the
original one. Faced with this challenge, we propose a new
line of research, where the privacy is learned and used in a
closed environment. The goal is to ensure that a given
entity, trusted to infer certain information with our data,
is blocked from inferring protected information from it. We
design a system that learns to succeed on the positive task
while simultaneously fail at the negative one, and
illustrate this with challenging cases where the positive
task (face verification) is harder than the negative one
(gender classification). The framework opens the door to
privacy and fairness in very important closed scenarios,
ranging from private data accumulation companies to
law-enforcement and hospitals.},
Doi = {10.1109/ICCVW.2017.298},
Key = {fds335965}
}

@article{fds339262,
Author = {Simhal, AK and Gong, B and Trimmer, JS and Weinberg, RJ and Smith, SJ and Sapiro, G and Micheva, KD},
Title = {A Computational Synaptic Antibody Characterization Tool for
Array Tomography.},
Journal = {Frontiers in Neuroanatomy},
Volume = {12},
Pages = {51},
Year = {2018},
Month = {January},
url = {http://dx.doi.org/10.3389/fnana.2018.00051},
Abstract = {Application-specific validation of antibodies is a critical
prerequisite for their successful use. Here we introduce an
automated framework for characterization and screening of
antibodies against synaptic molecules for high-resolution
immunofluorescence array tomography (AT). The proposed
Synaptic Antibody Characterization Tool (SACT) is designed
to provide an automatic, robust, flexible, and efficient
tool for antibody characterization at scale. SACT
automatically detects puncta of immunofluorescence labeling
from candidate antibodies and determines whether a punctum
belongs to a synapse. The molecular composition and size of
the target synapses expected to contain the antigen is
determined by the user, based on biological knowledge.
Operationally, the presence of a synapse is defined by the
colocalization or adjacency of the candidate antibody
punctum to one or more reference antibody puncta. The
outputs of SACT are automatically computed measurements such
as target synapse density and target specificity ratio that
reflect the sensitivity and specificity of immunolabeling
with a given candidate antibody. These measurements provide
an objective way to characterize and compare the performance
of different antibodies against the same target, and can be
used to objectively select the antibodies best suited for AT
and potentially for other immunolabeling
applications.},
Doi = {10.3389/fnana.2018.00051},
Key = {fds339262}
}

@article{fds335968,
Author = {Bertrán, MA and Martínez, NL and Wang, Y and Dunson, D and Sapiro, G and Ringach, D},
Title = {Active learning of cortical connectivity from two-photon
imaging data.},
Journal = {Plos One},
Volume = {13},
Number = {5},
Pages = {e0196527},
Year = {2018},
Month = {January},
url = {http://dx.doi.org/10.1371/journal.pone.0196527},
Abstract = {Understanding how groups of neurons interact within a
network is a fundamental question in system neuroscience.
Instead of passively observing the ongoing activity of a
network, we can typically perturb its activity, either by
external sensory stimulation or directly via techniques such
as two-photon optogenetics. A natural question is how to use
such perturbations to identify the connectivity of the
network efficiently. Here we introduce a method to infer
sparse connectivity graphs from in-vivo, two-photon imaging
of population activity in response to external stimuli. A
novel aspect of the work is the introduction of a
recommended distribution, incrementally learned from the
data, to optimally refine the inferred network. Unlike
existing system identification techniques, this "active
learning" method automatically focuses its attention on key
undiscovered areas of the network, instead of targeting
global uncertainty indicators like parameter variance. We
show how active learning leads to faster inference while, at
the same time, provides confidence intervals for the network
parameters. We present simulations on artificial small-world
networks to validate the methods and apply the method to
real data. Analysis of frequency of motifs recovered show
that cortical networks are consistent with a small-world
topology model.},
Doi = {10.1371/journal.pone.0196527},
Key = {fds335968}
}

@article{fds335966,
Author = {Asiedu, MN and Simhal, A and Lam, CT and Mueller, J and Chaudhary, U and Schmitt, JW and Sapiro, G and Ramanujam, N},
Title = {Image processing and machine learning techniques to automate
diagnosis of Lugol's iodine cervigrams for a low-cost
point-of-care digital colposcope},
Journal = {Progress in Biomedical Optics and Imaging Proceedings of
Spie},
Volume = {10485},
Publisher = {SPIE},
Year = {2018},
Month = {January},
ISBN = {9781510614550},
url = {http://dx.doi.org/10.1117/12.2282792},
recommends visual inspection with acetic acid (VIA) and/or
Lugol's Iodine (VILI) for cervical cancer screening in
low-resource settings. Human interpretation of diagnostic
indicators for visual inspection is qualitative, subjective,
and has high inter-observer discordance, which could lead
both to adverse outcomes for the patient and unnecessary
follow-ups. In this work, we a simple method for automatic
feature extraction and classification for Lugol's Iodine
cervigrams acquired with a low-cost, miniature, digital
colposcope. Algorithms to preprocess expert
physician-labelled cervigrams and to extract simple but
powerful color-based features are introduced. The features
are used to train a support vector machine model to classify
cervigrams based on expert physician labels. The selected
framework achieved a sensitivity, specificity, and accuracy
of 89.2%, 66.7% and 80.6% with majority diagnosis of the
expert physicians in discriminating cervical intraepithelial
neoplasia (CIN +) relative to normal tissues. The proposed
classifier also achieved an area under the curve of 84 when
trained with majority diagnosis of the expert physicians.
The results suggest that utilizing simple color-based
features may enable unbiased automation of VILI cervigrams,
opening the door to a full system of low-cost data
acquisition complemented with automatic interpretation.},
Doi = {10.1117/12.2282792},
Key = {fds335966}
}

@article{fds337693,
Author = {Duchin, Y and Shamir, RR and Patriat, R and Kim, J and Vitek, JL and Sapiro, G and Harel, N},
Title = {Patient-specific anatomical model for deep brain stimulation
based on 7 Tesla MRI.},
Journal = {Plos One},
Volume = {13},
Number = {8},
Pages = {e0201469},
Year = {2018},
Month = {January},
url = {http://dx.doi.org/10.1371/journal.pone.0201469},
Abstract = {OBJECTIVE:Deep brain stimulation (DBS) requires accurate
localization of the anatomical target structure, and the
precise placement of the DBS electrode within it. Ultra-high
field 7 Tesla (T) MR images can be utilized to create
patient-specific anatomical 3D models of the subthalamic
nuclei (STN) to enhance pre-surgical DBS targeting as well
as post-surgical visualization of the DBS lead position and
orientation. We validated the accuracy of the 7T
imaging-based patient-specific model of the STN and measured
the variability of the location and dimensions across
movement disorder patients. METHODS:72 patients who
underwent DBS surgery were scanned preoperatively on 7T MRI.
Segmentations and 3D volume rendering of the STN were
generated for all patients. For 21 STN-DBS cases,
microelectrode recording (MER) was used to validate the
segmentation. For 12 cases, we computed the correlation
between the overlap of the STN and volume of tissue
activated (VTA) and the monopolar review for a further
validation of the model's accuracy and its clinical
relevancy. RESULTS:We successfully reconstructed and
visualized the STN in all patients. Significant variability
was found across individuals regarding the location of the
STN center of mass as well as its volume, length, depth and
width. Significant correlations were found between MER and
the 7T imaging-based model of the STN (r = 0.86) and VTA-STN
overlap and the monopolar review outcome (r = 0.61).
CONCLUSION:The results suggest that an accurate
visualization and localization of a patient-specific 3D
model of the STN can be generated based on 7T MRI. The
imaging-based 7T MRI STN model was validated using MER and
patient's clinical outcomes. The significant variability
observed in the STN location and shape based on a large
number of patients emphasizes the importance of an accurate
direct visualization of the STN for DBS targeting. An
accurate STN localization can facilitate postoperative
stimulation parameters for optimized patient
outcome.},
Doi = {10.1371/journal.pone.0201469},
Key = {fds337693}
}

@article{fds339596,
Author = {Qiu, Q and Lezama, J and Bronstein, A and Sapiro,
G},
Title = {ForestHash: Semantic Hashing with Shallow Random Forests and
Tiny Convolutional Networks},
Journal = {Lecture Notes in Computer Science (Including Subseries
Lecture Notes in Artificial Intelligence and Lecture Notes
in Bioinformatics)},
Volume = {11206 LNCS},
Pages = {442-459},
Publisher = {Springer International Publishing},
Year = {2018},
Month = {January},
url = {http://dx.doi.org/10.1007/978-3-030-01216-8_27},
Abstract = {© 2018, Springer Nature Switzerland AG. In this paper, we
introduce a random forest semantic hashing scheme that
embeds tiny convolutional neural networks (CNN) into shallow
random forests. A binary hash code for a data point is
obtained by a set of decision trees, setting ‘1’ for the
visited tree leaf, and ‘0’ for the rest. We propose to
first randomly group arriving classes at each tree split
node into two groups, obtaining a significantly simplified
two-class classification problem that can be a handled with
a light-weight CNN weak learner. Code uniqueness is achieved
via the random class grouping, whilst code consistency is
achieved using a low-rank loss in the CNN weak learners that
encourages intra-class compactness for the two random class
groups. Finally, we introduce an information-theoretic
approach for aggregating codes of individual trees into a
single hash code, producing a near-optimal unique hash for
each class. The proposed approach significantly outperforms
state-of-the-art hashing methods for image retrieval tasks
on large-scale public datasets, and is comparable to image
classification methods while utilizing a more compact,
efficient and scalable representation. This work proposes a
principled and robust procedure to train and deploy in
parallel an ensemble of light-weight CNNs, instead of simply
going deeper.},
Doi = {10.1007/978-3-030-01216-8_27},
Key = {fds339596}
}

@article{fds340082,
Author = {Qiu, Q and Cheng, X and Calderbank, R and Sapiro,
G},
Title = {DCFNet: Deep Neural Network with Decomposed Convolutional
Filters},
Journal = {35th International Conference on Machine Learning, Icml
2018},
Volume = {9},
Pages = {6687-6696},
Year = {2018},
Month = {January},
Abstract = {©35th International Conference on Machine Learning, ICML
Network (CNN) contain model parameters learned from enormous
amounts of data. In this paper, we suggest to decompose
convolutional filters in CNN as a truncated expansion with
pre-fixed bases, namely the Decomposed Convolutional Filters
network (DCFNet), where the expansion coefficients remain
learned from data. Such a structure not only reduces the
number of trainable parameters and computation, but also
imposes filter regularity by bases truncation. Through
extensive experiments, we consistently observe that DCFNet
maintains accuracy for image classification tasks with a
significant reduction of model parameters, particularly with
Fourier-Bessel (FB) bases, and even with random bases.
Theoretically, we analyze the representation stability of
DCFNet with respect to input variations, and prove
representation stability under generic assumptions on the
expansion coefficients. The analysis is consistent with the
empirical observations.},
Key = {fds340082}
}

@article{fds340484,
Author = {Asiedu, MN and Simhal, A and Chaudhary, U and Mueller, JL and Lam, CT and Schmitt, JW and Venegas, G and Sapiro, G and Ramanujam,
N},
Title = {Development of algorithms for automated detection of
cervical pre-cancers with a low-cost, point-of-care, Pocket
Colposcope},
Journal = {Ieee Transactions on Bio Medical Engineering},
Pages = {1-1},
Publisher = {Institute of Electrical and Electronics Engineers
(IEEE)},
Year = {2018},
Month = {January},
url = {http://dx.doi.org/10.1109/TBME.2018.2887208},
Abstract = {OAPA Goal: In this work, we propose methods for (1)
automatic feature extraction and classification for acetic
acid and Lugol's iodine cervigrams and (2) methods for
combining features/diagnosis of different contrasts in
cervigrams for improved performance. Methods: We developed
algorithms to pre-process pathology-labeled cervigrams and
to extract simple but powerful color and textural-based
features. The features were used to train a support vector
machine model to classify cervigrams based on corresponding
pathology for VIA, VILI, and combination of the two
contrasts. Results: The proposed framework achieved a
sensitivity, specificity, and accuracy of 81.3%, 78.6%, and
80.0%, respectively when used to distinguish cervical
intraepithelial neoplasia (CIN+) relative to normal and
benign tissues. This is superior to the average values
achieved by expert physicians on the same data set for
discriminating normal/benign from CIN+ (sensitivity=77%,
specificity=51%, accuracy=63%). Conclusion: The results
suggest that utilizing simple color- and textural-based
features from VIA and VILI images may provide unbiased
automation of cervigrams. Significance: This would enable
automated expert-level diagnosis of cervical pre-cancer at
the point-of-care.},
Doi = {10.1109/TBME.2018.2887208},
Key = {fds340484}
}

@article{fds340786,
Author = {Bovery, MDMJ and Dawson, G and Hashemi, J and Sapiro,
G},
Title = {A Scalable Off-the-Shelf Framework for Measuring Patterns of
Attention in Young Children and its Application in Autism
Spectrum Disorder},
Journal = {Ieee Transactions on Affective Computing},
Pages = {1-1},
Publisher = {Institute of Electrical and Electronics Engineers
(IEEE)},
Year = {2018},
Month = {January},
url = {http://dx.doi.org/10.1109/TAFFC.2018.2890610},
Abstract = {IEEE Autism spectrum disorder (ASD) is associated with
deficits in the processing of social information and
difficulties in social interaction, and individuals with ASD
exhibit atypical attention and gaze. Traditionally, gaze
studies have relied upon precise and constrained means of
monitoring attention using expensive equipment in
laboratories. We develop a low-cost off-the-shelf
alternative for measuring attention that can be used in
natural settings. The head and iris positions of 104 16-31
months children, 22 of them diagnosed with ASD, were
recorded using the front facing camera in an iPad while they
watched on the device screen a movie displaying dynamic
stimuli, social on the left and nonsocial on the right. The
head and iris position were then automatically analyzed via
computer vision algorithms to detect the direction of
attention. Children in the ASD group paid less attention to
the movie, showed less attention to the social as compared
to the nonsocial stimulus, and often fixated their attention
to one side of the screen. The proposed method provides a
low-cost means of monitoring attention to properly designed
stimuli, demonstrating that the integration of stimuli
design and automatic response analysis results in the
opportunity to use off-the-shelf cameras to assess
behavioral biomarkers.},
Doi = {10.1109/TAFFC.2018.2890610},
Key = {fds340786}
}

@article{fds335967,
Author = {Chiew, KS and Hashemi, J and Gans, LK and Lerebours, L and Clement, NJ and Vu, M-AT and Sapiro, G and Heller, NE and Adcock,
RA},
Title = {Motivational valence alters memory formation without
altering exploration of a real-life spatial
environment.},
Journal = {Plos One},
Volume = {13},
Number = {3},
Pages = {e0193506},
Year = {2018},
url = {http://dx.doi.org/10.1371/journal.pone.0193506},
Abstract = {Volitional exploration and learning are key to adaptive
behavior, yet their characterization remains a complex
problem for cognitive science. Exploration has been posited
as a mechanism by which motivation promotes memory, but this
relationship is not well-understood, in part because novel
stimuli that motivate exploration also reliably elicit
changes in neuromodulatory brain systems that directly alter
memory formation, via effects on neural plasticity. To
deconfound interrelationships between motivation,
exploration, and memory formation we manipulated
motivational state prior to entering a spatial context,
measured exploratory responses to the context and novel
stimuli within it, and then examined motivation and
exploration as predictors of memory outcomes. To elicit
spontaneous exploration, we used the physical space of an
art exhibit with affectively rich content; we expected
motivated exploration and memory to reflect multiple
factors, including not only motivational valence, but also
individual differences. Motivation was manipulated via an
introductory statement framing exhibit themes in terms of
Promotion- or Prevention-oriented goals. Participants
explored the exhibit while being tracked by video. They
returned 24 hours later for recall and spatial memory tests,
followed by measures of motivation, personality, and
relevant attitude variables. Promotion and Prevention
condition participants did not differ in terms of
group-level exploration time or memory metrics, suggesting
similar motivation to explore under both framing contexts.
However, exploratory behavior and memory outcomes were
significantly more closely related under Promotion than
Prevention, indicating that Prevention framing disrupted
trait measures predicted exploration similarly across
framing conditions, traits interacted with motivational
framing context and facial affect to predict memory
outcomes. This novel characterization of motivated learning
implies that dissociable behavioral and biological
mechanisms, here varying as a function of valence,
contribute to memory outcomes in complex, real-life
environments.},
Doi = {10.1371/journal.pone.0193506},
Key = {fds335967}
}

@article{fds335969,
Author = {Lezama, J and Qiu, Q and Sapiro, G},
Title = {Not afraid of the dark: NIR-VIS face recognition via
cross-spectral hallucination and low-rank
embedding},
Journal = {Proceedings 30th Ieee Conference on Computer Vision and
Pattern Recognition, Cvpr 2017},
Volume = {2017-January},
Pages = {6807-6816},
Publisher = {IEEE},
Year = {2017},
Month = {November},
url = {http://dx.doi.org/10.1109/CVPR.2017.720},
Abstract = {© 2017 IEEE. Surveillance cameras today often capture NIR
(near infrared) images in low-light environments. However,
most face datasets accessible for training and verification
are only collected in the VIS (visible light) spectrum. It
remains a challenging problem to match NIR to VIS face
images due to the different light spectrum. Recently,
breakthroughs have been made for VIS face recognition by
applying deep learning on a huge amount of labeled VIS face
samples. The same deep learning approach cannot be simply
applied to NIR face recognition for two main reasons: First,
much limited NIR face images are available for training
compared to the VIS spectrum. Second, face galleries to be
matched are mostly available only in the VIS spectrum. In
this paper, we propose an approach to extend the deep
learning breakthrough for VIS face recognition to the NIR
spectrum, without retraining the underlying deep models that
see only VIS faces. Our approach consists of two core
components, cross-spectral hallucination and low-rank
embedding, to optimize respectively input and output of a
VIS deep model for cross-spectral face recognition.
Cross-spectral hallucination produces VIS faces from NIR
images through a deep learning approach. Low-rank embedding
restores a low-rank structure for faces deep features across
both NIR and VIS spectrum. We observe that it is often
equally effective to perform hallucination to input NIR
images or low-rank embedding to output deep features for a
VIS deep model for cross-spectral recognition. When
hallucination and low-rank embedding are deployed together,
we observe significant further improvement; we obtain
state-of-the-art accuracy on the CASIA NIR-VIS v2.0
benchmark, without the need at all to re-train the
recognition system.},
Doi = {10.1109/CVPR.2017.720},
Key = {fds335969}
}

@article{fds335970,
Author = {Ye, Q and Zhang, T and Ke, W and Qiu, Q and Chen, J and Sapiro, G and Zhang,
B},
Title = {Self-learning scene-specific pedestrian detectors using a
progressive latent model},
Journal = {Proceedings 30th Ieee Conference on Computer Vision and
Pattern Recognition, Cvpr 2017},
Volume = {2017-January},
Pages = {2057-2066},
Publisher = {IEEE},
Year = {2017},
Month = {November},
url = {http://dx.doi.org/10.1109/CVPR.2017.222},
Abstract = {© 2017 IEEE. In this paper, a self-learning approach is
proposed towards solving scene-specific pedestrian detection
problem without any human' annotation involved. The
self-learning approach is deployed as progressive steps of
object discovery, object enforcement, and label propagation.
In the learning procedure, object locations in each frame
are treated as latent variables that are solved with a
progressive latent model (PLM). Compared with conventional
latent models, the proposed PLM incorporates a spatial
regularization term to reduce ambiguities in object
proposals and to enforce object localization, and also a
graph-based label propagation to discover harder instances
in adjacent frames. With the difference of convex (DC)
objective functions, PLM can be efficiently optimized with a
concave-convex programming and thus guaranteeing the
stability of self-learning. Extensive experiments
demonstrate that even without annotation the proposed
self-learning approach outperforms weakly supervised
learning approaches, while achieving comparable performance
with transfer learning and fully supervised
approaches.},
Doi = {10.1109/CVPR.2017.222},
Key = {fds335970}
}

@article{fds335971,
Author = {Su, S and Delbracio, M and Wang, J and Sapiro, G and Heidrich, W and Wang,
O},
Title = {Deep video deblurring for hand-held cameras},
Journal = {Proceedings 30th Ieee Conference on Computer Vision and
Pattern Recognition, Cvpr 2017},
Volume = {2017-January},
Pages = {237-246},
Publisher = {IEEE},
Year = {2017},
Month = {November},
ISBN = {9781538604571},
url = {http://dx.doi.org/10.1109/CVPR.2017.33},
Abstract = {© 2017 IEEE. Motion blur from camera shake is a major
problem in videos captured by hand-held devices. Unlike
single-image deblurring, video-based approaches can take
advantage of the abundant information that exists across
neighboring frames. As a result the best performing methods
rely on the alignment of nearby frames. However, aligning
images is a computationally expensive and fragile procedure,
and methods that aggregate information must therefore be
able to identify which regions have been accurately aligned
and which have not, a task that requires high level scene
understanding. In this work, we introduce a deep learning
solution to video deblurring, where a CNN is trained
end-toend to learn how to accumulate information across
frames. To train this network, we collected a dataset of
real videos recorded with a high frame rate camera, which we
use to generate synthetic motion blur for supervision. We
show that the features learned from this dataset extend to
deblurring motion blur that arises due to camera shake in a
wide range of videos, and compare the quality of results to
a number of other baselines.},
Doi = {10.1109/CVPR.2017.33},
Key = {fds335971}
}

@article{fds335972,
Author = {Tepper, M and Sapiro, G},
Title = {Nonnegative matrix underapproximation for robust multiple
model fitting},
Journal = {Proceedings 30th Ieee Conference on Computer Vision and
Pattern Recognition, Cvpr 2017},
Volume = {2017-January},
Pages = {655-663},
Publisher = {IEEE},
Year = {2017},
Month = {November},
ISBN = {9781538604571},
url = {http://dx.doi.org/10.1109/CVPR.2017.77},
Abstract = {© 2017 IEEE. In this work, we introduce a highly efficient
algorithm to address the nonnegative matrix
underapproximation (NMU) problem, i.e., nonnegative matrix
factorization (NMF) with an additional underapproximation
constraint. NMU results are interesting as, compared to
part-based behavior, explaining unique data features. To
show these features in practice, we first present an
application to the analysis of climate data. We then present
an NMU-based algorithm to robustly fit multiple parametric
models to a dataset. The proposed approach delivers
state-of-the-art results for the estimation of multiple
fundamental matrices and homographies, outperforming other
alternatives in the literature and exemplifying the use of
efficient NMU computations.},
Doi = {10.1109/CVPR.2017.77},
Key = {fds335972}
}

@article{fds329136,
Author = {Pisharady, PK and Sotiropoulos, SN and Sapiro, G and Lenglet,
C},
Title = {A Sparse Bayesian Learning Algorithm for White Matter
Parameter Estimation from Compressed Multi-shell Diffusion
MRI.},
Journal = {Medical Image Computing and Computer Assisted Intervention :
Miccai ... International Conference on Medical Image
Computing and Computer Assisted Intervention},
Volume = {10433},
Pages = {602-610},
Year = {2017},
Month = {September},
ISBN = {9783319661810},
url = {http://dx.doi.org/10.1007/978-3-319-66182-7_69},
Abstract = {We propose a sparse Bayesian learning algorithm for improved
estimation of white matter fiber parameters from compressed
(under-sampled q-space) multi-shell diffusion MRI data. The
multi-shell data is represented in a dictionary form using a
non-monoexponential decay model of diffusion, based on
continuous gamma distribution of diffusivities. The fiber
volume fractions with predefined orientations, which are the
unknown parameters, form the dictionary weights. These
unknown parameters are estimated with a linear un-mixing
framework, using a sparse Bayesian learning algorithm. A
localized learning of hyperparameters at each voxel and for
each possible fiber orientations improves the parameter
estimation. Our experiments using synthetic data from the
ISBI 2012 HARDI reconstruction challenge and in-vivo data
from the Human Connectome Project demonstrate the
improvements.},
Doi = {10.1007/978-3-319-66182-7_69},
Key = {fds329136}
}

@article{fds329481,
Author = {Sokolić, J and Giryes, R and Sapiro, G and Rodrigues,
MRD},
Title = {Generalization error of deep neural networks: Role of
classification margin and data structure},
Journal = {2017 12th International Conference on Sampling Theory and
Applications, Sampta 2017},
Pages = {147-151},
Publisher = {IEEE},
Year = {2017},
Month = {September},
ISBN = {9781538615652},
url = {http://dx.doi.org/10.1109/SAMPTA.2017.8024476},
Abstract = {© 2017 IEEE. Understanding the generalization properties of
deep learning models is critical for their successful usage
in many applications, especially in the regimes where the
number of training samples is limited. We study the
generalization properties of deep neural networks (DNNs) via
the Jacobian matrix of the network. Our analysis is general
to arbitrary network structures, types of non-linearities
and pooling operations. We show that bounding the spectral
norm of the Jacobian matrix in the network reduces the
generalization error. In addition, we tie this error to the
invariance in the data and the network. Experiments on the
MNIST and ImageNet datasets support these findings. This
short paper summarizes our generalization error theorems for
DNNs and for general invariant classifiers [1],
[2].},
Doi = {10.1109/SAMPTA.2017.8024476},
Key = {fds329481}
}

@article{fds328865,
Author = {Sokolić, J and Giryes, R and Sapiro, G and Rodrigues,
MRD},
Title = {Robust Large Margin Deep Neural Networks},
Journal = {Ieee Transactions on Signal Processing},
Volume = {65},
Number = {16},
Pages = {4265-4280},
Publisher = {Institute of Electrical and Electronics Engineers
(IEEE)},
Year = {2017},
Month = {August},
url = {http://dx.doi.org/10.1109/TSP.2017.2708039},
Abstract = {© 2017 IEEE. The generalization error of deep neural
networks via their classification margin is studied in this
paper. Our approach is based on the Jacobian matrix of a
deep neural network and can be applied to networks with
arbitrary nonlinearities and pooling layers, and to networks
with different architectures such as feed forward networks
and residual networks. Our analysis leads to the conclusion
that a bounded spectral norm of the network's Jacobian
matrix in the neighbourhood of the training samples is
crucial for a deep neural network of arbitrary depth and
width to generalize well. This is a significant improvement
over the current bounds in the literature, which imply that
the generalization error grows with either the width or the
depth of the network. Moreover, it shows that the recently
proposed batch normalization and weight normalization
reparametrizations enjoy good generalization properties, and
leads to a novel network regularizer based on the network's
Jacobian matrix. The analysis is supported with experimental
results on the MNIST, CIFAR-10, LaRED, and ImageNet
datasets.},
Doi = {10.1109/TSP.2017.2708039},
Key = {fds328865}
}

@article{fds326146,
Author = {Simhal, AK and Aguerrebere, C and Collman, F and Vogelstein, JT and Micheva, KD and Weinberg, RJ and Smith, SJ and Sapiro,
G},
Title = {Probabilistic fluorescence-based synapse
detection.},
Journal = {Plos Computational Biology},
Volume = {13},
Number = {4},
Pages = {e1005493},
Year = {2017},
Month = {April},
url = {http://dx.doi.org/10.1371/journal.pcbi.1005493},
Abstract = {Deeper exploration of the brain's vast synaptic networks
will require new tools for high-throughput structural and
molecular profiling of the diverse populations of synapses
that compose those networks. Fluorescence microscopy (FM)
and electron microscopy (EM) offer complementary advantages
and disadvantages for single-synapse analysis. FM combines
exquisite molecular discrimination capacities with high
speed and low cost, but rigorous discrimination between
synaptic and non-synaptic fluorescence signals is
challenging. In contrast, EM remains the gold standard for
reliable identification of a synapse, but offers only
limited molecular discrimination and is slow and costly. To
develop and test single-synapse image analysis methods, we
have used datasets from conjugate array tomography (cAT),
which provides voxel-conjugate FM and EM (annotated) images
of the same individual synapses. We report a novel
unsupervised probabilistic method for detection of synapses
from multiplex FM (muxFM) image data, and evaluate this
method both by comparison to EM gold standard annotated data
and by examining its capacity to reproduce known important
features of cortical synapse distributions. The proposed
probabilistic model-based synapse detector accepts
molecular-morphological synapse models as user queries, and
delivers a volumetric map of the probability that each voxel
represents part of a synapse. Taking human annotation of cAT
EM data as ground truth, we show that our algorithm detects
synapses from muxFM data alone as successfully as human
annotators seeing only the muxFM data, and accurately
reproduces known architectural features of cortical synapse
distributions. This approach opens the door to data-driven
discovery of new synapse types and their density. We suggest
that our probabilistic synapse detector will also be useful
for analysis of standard confocal and super-resolution FM
images, where EM cross-validation is not
practical.},
Doi = {10.1371/journal.pcbi.1005493},
Key = {fds326146}
}

@article{fds323853,
Author = {Campbell, K and Carpenter, KLH and Espinosa, S and Hashemi, J and Qiu,
Q and Tepper, M and Calderbank, R and Sapiro, G and Egger, HL and Baker,
JP and Dawson, G},
Title = {Use of a Digital Modified Checklist for Autism in Toddlers -
Revised with Follow-up to Improve Quality of Screening for
Autism.},
Journal = {J Pediatr},
Volume = {183},
Pages = {133-139.e1},
Year = {2017},
Month = {April},
url = {http://dx.doi.org/10.1016/j.jpeds.2017.01.021},
Abstract = {OBJECTIVES: To assess changes in quality of care for
children at risk for autism spectrum disorders (ASD) due to
process improvement and implementation of a digital
screening form. STUDY DESIGN: The process of screening for
ASD was studied in an academic primary care pediatrics
clinic before and after implementation of a digital version
of the Modified Checklist for Autism in Toddlers - Revised
with Follow-up with automated risk assessment. Quality
metrics included accuracy of documentation of screening
results and appropriate action for positive screens
(secondary screening or referral). Participating physicians
completed pre- and postintervention surveys to measure
changes in attitudes toward feasibility and value of
screening for ASD. Evidence of change was evaluated with
statistical process control charts and χ2 tests. RESULTS:
Accurate documentation in the electronic health record of
screening results increased from 54% to 92% (38% increase,
95% CI 14%-64%) and appropriate action for children
screening positive increased from 25% to 85% (60% increase,
95% CI 35%-85%). A total of 90% of participating physicians
agreed that the transition to a digital screening form
improved their clinical assessment of autism risk.
CONCLUSIONS: Implementation of a tablet-based digital
version of the Modified Checklist for Autism in Toddlers -
Revised with Follow-up led to improved quality of care for
children at risk for ASD and increased acceptability of
screening for ASD. Continued efforts towards improving the
process of screening for ASD could facilitate rapid, early
diagnosis of ASD and advance the accuracy of studies of the
impact of screening.},
Doi = {10.1016/j.jpeds.2017.01.021},
Key = {fds323853}
}

@article{fds324086,
Author = {Chen, J and Chang, Z and Qiu, Q and Li, X and Sapiro, G and Bronstein, A and Pietikäinen, M},
Title = {RealSense = real heart rate: Illumination invariant heart
rate estimation from videos},
Journal = {2016 6th International Conference on Image Processing
Theory, Tools and Applications, Ipta 2016},
Publisher = {IEEE},
Year = {2017},
Month = {January},
ISBN = {9781467389105},
url = {http://dx.doi.org/10.1109/IPTA.2016.7820970},
Abstract = {© 2016 IEEE. Recent studies validated the feasibility of
estimating heart rate from human faces in RGB video.
However, test subjects are often recorded under controlled
conditions, as illumination variations significantly affect
the RGB-based heart rate estimation accuracy. Intel
newly-announced low-cost RealSense 3D (RGBD) camera is
becoming ubiquitous in laptops and mobile devices starting
this year, opening the door to new and more robust computer
vision. RealSense cameras produce RGB images with extra
depth information inferred from a latent near-infrared (NIR)
channel. In this paper, we experimentally demonstrate, for
the first time, that heart rate can be reliably estimated
from RealSense near-infrared images. This enables
illumination invariant heart rate estimation, extending the
heart rate from video feasibility to low-light applications,
such as night driving. With the (coming) ubiquitous presence
of RealSense devices, the proposed method not only utilizes
its near-infrared channel, designed originally to be hidden
from consumers; but also exploits the associated depth
information for improved robustness to head
pose.},
Doi = {10.1109/IPTA.2016.7820970},
Key = {fds324086}
}

@article{fds326840,
Author = {Gunalan, K and Chaturvedi, A and Howell, B and Duchin, Y and Lempka, SF and Patriat, R and Sapiro, G and Harel, N and McIntyre,
CC},
Title = {Creating and parameterizing patient-specific deep brain
stimulation pathway-activation models using the hyperdirect
pathway as an example.},
Journal = {Plos One},
Volume = {12},
Number = {4},
Pages = {e0176132},
Year = {2017},
Month = {January},
url = {http://dx.doi.org/10.1371/journal.pone.0176132},
Abstract = {BACKGROUND:Deep brain stimulation (DBS) is an established
clinical therapy and computational models have played an
important role in advancing the technology. Patient-specific
DBS models are now common tools in both academic and
industrial research, as well as clinical software systems.
However, the exact methodology for creating patient-specific
DBS models can vary substantially and important technical
details are often missing from published reports.
OBJECTIVE:Provide a detailed description of the assembly
workflow and parameterization of a patient-specific DBS
pathway-activation model (PAM) and predict the response of
the hyperdirect pathway to clinical stimulation.
METHODS:Integration of multiple software tools (e.g. COMSOL,
MATLAB, FSL, NEURON, Python) enables the creation and
visualization of a DBS PAM. An example DBS PAM was developed
using 7T magnetic resonance imaging data from a single
unilaterally implanted patient with Parkinson's disease
(PD). This detailed description implements our best
computational practices and most elaborate parameterization
steps, as defined from over a decade of technical evolution.
RESULTS:Pathway recruitment curves and strength-duration
relationships highlight the non-linear response of axons to
changes in the DBS parameter settings. CONCLUSION:Parameterization
of patient-specific DBS models can be highly detailed and
constrained, thereby providing confidence in the simulation
predictions, but at the expense of time demanding technical
implementation steps. DBS PAMs represent new tools for
investigating possible correlations between brain pathway
activation patterns and clinical symptom
modulation.},
Doi = {10.1371/journal.pone.0176132},
Key = {fds326840}
}

@article{fds322212,
Author = {Lezama, J and Mukherjee, D and McNabb, RP and Sapiro, G and Kuo, AN and Farsiu, S},
Title = {Segmentation guided registration of wide field-of-view
retinal optical coherence tomography volumes.},
Journal = {Biomedical Optics Express},
Volume = {7},
Number = {12},
Pages = {4827-4846},
Year = {2016},
Month = {December},
url = {http://dx.doi.org/10.1364/BOE.7.004827},
Abstract = {Patient motion artifacts are often visible in densely
sampled or large wide field-of-view (FOV) retinal optical
coherence tomography (OCT) volumes. A popular strategy for
reducing motion artifacts is to capture two orthogonally
oriented volumetric scans. However, due to larger volume
sizes, longer acquisition times, and corresponding larger
motion artifacts, the registration of wide FOV scans remains
a challenging problem. In particular, gaps in data
acquisition due to eye motion, such as saccades, can be
significant and their modeling becomes critical for
complete computational pipeline for the automatic motion
correction and accurate registration of wide FOV
orthogonally scanned OCT images of the human retina. The
proposed framework utilizes the retinal boundary
segmentation as a guide for registration and requires only a
minimal transformation of the acquired data to produce a
successful registration. It includes saccade detection and
correction, a custom version of the optical flow algorithm
for dense lateral registration and a linear optimization
approach for axial registration. Utilizing a wide FOV swept
source OCT system, we acquired retinal volumes of 12
subjects and we provide qualitative and quantitative
experimental results to validate the state-of-the-art
effectiveness of the proposed technique. The source code
corresponding to the proposed algorithm is available
online.},
Doi = {10.1364/BOE.7.004827},
Key = {fds322212}
}

@article{fds322672,
Author = {Aguerrebere, C and Delbracio, M and Bartesaghi, A and Sapiro,
G},
Title = {Fundamental limits in multi-image alignment},
Journal = {Ieee Transactions on Signal Processing},
Volume = {64},
Number = {21},
Pages = {5707-5722},
Publisher = {Institute of Electrical and Electronics Engineers
(IEEE)},
Year = {2016},
Month = {November},
url = {http://dx.doi.org/10.1109/TSP.2016.2600517},
Abstract = {© 1991-2012 IEEE. The performance of multiimage alignment,
bringing different images into one coordinate system, is
critical in many applications with varied signal-to-noise
ratio (SNR) conditions. A great amount of effort is being
invested into developing methods to solve this problem.
Several important questions thus arise, including: Which are
the fundamental limits in multiimage alignment performance?
Theoretical bounds provide a fundamental benchmark to
compare methods and can help establish whether improvements
can be made. In this work, we tackle the problem of finding
the performance limits in image registration when multiple
shifted and noisy observations are available. We derive and
analyze the Cramér-Rao and Ziv-Zakai lower bounds under
different statistical models for the underlying image. We
show the existence of different behavior zones depending on
the difficulty level of the problem, given by the SNR
conditions of the input images. The analysis we present here
brings further insight into the fundamental limitations of
the multiimage alignment problem.},
Doi = {10.1109/TSP.2016.2600517},
Key = {fds322672}
}

@article{fds322673,
Author = {Elhamifar, E and Sapiro, G and Sastry, SS},
Title = {Dissimilarity-Based Sparse Subset Selection.},
Journal = {Ieee Transactions on Pattern Analysis and Machine
Intelligence},
Volume = {38},
Number = {11},
Pages = {2182-2197},
Year = {2016},
Month = {November},
url = {http://dx.doi.org/10.1109/tpami.2015.2511748},
Abstract = {Finding an informative subset of a large collection of data
points or models is at the center of many problems in
computer vision, recommender systems, bio/health informatics
as well as image and natural language processing. Given
pairwise dissimilarities between the elements of a 'source
set' and a 'target set,' we consider the problem of finding
a subset of the source set, called representatives or
exemplars, that can efficiently describe the target set. We
formulate the problem as a row-sparsity regularized trace
minimization problem. Since the proposed formulation is, in
general, NP-hard, we consider a convex relaxation. The
solution of our optimization finds representatives and the
assignment of each element of the target set to each
representative, hence, obtaining a clustering. We analyze
the solution of our proposed optimization as a function of
the regularization parameter. We show that when the two sets
jointly partition into multiple groups, our algorithm finds
representatives from all groups and reveals clustering of
the sets. In addition, we show that the proposed framework
can effectively deal with outliers. Our algorithm works with
arbitrary dissimilarities, which can be asymmetric or
violate the triangle inequality. To efficiently implement
our algorithm, we consider an Alternating Direction Method
complexity in the problem size. We show that the ADMM
implementation allows to parallelize the algorithm, hence
further reducing the computational time. Finally, by
experiments on real-world datasets, we show that our
proposed algorithm improves the state of the art on the two
problems of scene categorization using representative images
and time-series modeling and segmentation using
representative models.},
Doi = {10.1109/tpami.2015.2511748},
Key = {fds322673}
}

@article{fds322674,
Author = {Fiori, M and Muse, P and Tepper, M and Sapiro, G},
Title = {Tell me where you are and i tell you where you are going:
Estimation of dynamic mobility graphs},
Journal = {Proceedings of the Ieee Sensor Array and Multichannel Signal
Processing Workshop},
Volume = {2016-September},
Publisher = {IEEE},
Year = {2016},
Month = {September},
ISBN = {9781509021031},
url = {http://dx.doi.org/10.1109/SAM.2016.7569685},
Abstract = {© 2016 IEEE. The interest in problems related to graph
inference has been increasing significantly during the last
are either static, or systems where changes in one node are
immediately reflected in other nodes. In this paper we
address the problem of mobility graph estimation, when the
available dataset has an asynchronous and time-variant
nature. We present a formulation for this problem consisting
on an optimization of a cost function having a fitting term
to explain the observations with the dynamics of the system,
and a sparsity promoting penalty term, in order to select
the paths actually used. The formulation is tested on two
publicly available real datasets on US aviation and NY taxi
traffic, showing the importance of the problem and the
applicability of the proposed framework.},
Doi = {10.1109/SAM.2016.7569685},
Key = {fds322674}
}

@article{fds322675,
Author = {Giryes, R and Sapiro, G and Bronstein, AM},
Title = {Deep Neural Networks with Random Gaussian Weights: A
Universal Classification Strategy?},
Journal = {Ieee Transactions on Signal Processing},
Volume = {64},
Number = {13},
Pages = {3444-3457},
Publisher = {Institute of Electrical and Electronics Engineers
(IEEE)},
Year = {2016},
Month = {July},
url = {http://dx.doi.org/10.1109/TSP.2016.2546221},
Abstract = {© 1991-2012 IEEE. Three important properties of a
classification machinery are i) the system preserves the
core information of the input data; ii) the training
examples convey information about unseen data; and iii) the
system is able to treat differently points from different
classes. In this paper, we show that these fundamental
properties are satisfied by the architecture of deep neural
networks. We formally prove that these networks with random
Gaussian weights perform a distance-preserving embedding of
the data, with a special treatment for in-class and
out-of-class data. Similar points at the input of the
network are likely to have a similar output. The theoretical
analysis of deep networks here presented exploits tools used
in the compressed sensing and dictionary learning
literature, thereby making a formal connection between these
important topics. The derived results allow drawing
conclusions on the metric learning properties of the network
and their relation to its structure, as well as providing
bounds on the required size of the training set such that
the training examples would represent faithfully the unseen
data. The results are validated with state-of-the-art
trained networks.},
Doi = {10.1109/TSP.2016.2546221},
Key = {fds322675}
}

@article{fds322676,
Author = {Tepper, M and Sapiro, G},
Title = {A short-graph fourier transform via personalized pagerank
vectors},
Journal = {2015 Ieee International Conference on Acoustics, Speech, and
Signal Processing (Icassp)},
Volume = {2016-May},
Pages = {4806-4810},
Publisher = {IEEE},
Year = {2016},
Month = {May},
ISBN = {9781479999880},
url = {http://dx.doi.org/10.1109/ICASSP.2016.7472590},
Abstract = {© 2016 IEEE. The short-time Fourier transform (STFT) is
widely used to analyze the spectra of temporal signals that
vary through time. Signals defined over graphs, due to their
intrinsic complexity, exhibit large variations in their
patterns. In this work we propose a new formulation for an
STFT for signals defined over graphs. This formulation draws
on recent ideas from spectral graph theory, using
personalized PageRank vectors as its fundamental building
block. Furthermore, this work establishes and explores the
connection between local spectral graph theory and localized
spectral analysis of graph signals. We accompany the
presentation with synthetic and real-world examples, showing
the suitability of the proposed approach.},
Doi = {10.1109/ICASSP.2016.7472590},
Key = {fds322676}
}

@article{fds322677,
Author = {Tepper, M and Sapiro, G},
Title = {Compressed Nonnegative Matrix Factorization Is Fast and
Accurate},
Journal = {Ieee Transactions on Signal Processing},
Volume = {64},
Number = {9},
Pages = {2269-2283},
Publisher = {Institute of Electrical and Electronics Engineers
(IEEE)},
Year = {2016},
Month = {May},
url = {http://dx.doi.org/10.1109/TSP.2016.2516971},
Abstract = {© 2015 IEEE. Nonnegative matrix factorization (NMF) has an
established reputation as a useful data analysis technique
in numerous applications. However, its usage in practical
situations is undergoing challenges in recent years. The
fundamental factor to this is the increasingly growing size
of the datasets available and needed in the information
sciences. To address this, in this work we propose to use
structured random compression, that is, random projections
that exploit the data structure, for two NMF variants:
classical and separable. In separable NMF (SNMF), the left
factors are a subset of the columns of the input matrix. We
present suitable formulations for each problem, dealing with
different representative algorithms within each one. We show
that the resulting compressed techniques are faster than
their uncompressed variants, vastly reduce memory demands,
and do not encompass any significant deterioration in
performance. The proposed structured random projections for
SNMF allow to deal with arbitrarily shaped large matrices,
beyond the standard limit of tall-and-skinny matrices,
general setting. We accompany the algorithmic presentation
with theoretical foundations and numerous and diverse
examples, showing the suitability of the proposed
approaches. Our implementations are publicly
available.},
Doi = {10.1109/TSP.2016.2516971},
Key = {fds322677}
}

@article{fds322678,
Author = {Qiu, Q and Thompson, A and Calderbank, R and Sapiro,
G},
Title = {Data Representation Using the Weyl Transform},
Journal = {Ieee Transactions on Signal Processing},
Volume = {64},
Number = {7},
Pages = {1844-1853},
Publisher = {Institute of Electrical and Electronics Engineers
(IEEE)},
Year = {2016},
Month = {April},
url = {http://dx.doi.org/10.1109/TSP.2015.2505661},
Abstract = {© 2015 IEEE. The Weyl transform is introduced as a rich
framework for data representation. Transform coefficients
are connected to the Walsh-Hadamard transform of multiscale
autocorrelations, and different forms of dyadic periodicity
in a signal are shown to appear as different features in its
Weyl coefficients. The Weyl transform has a high degree of
symmetry with respect to a large group of multiscale
transformations, which allows compact yet discriminative
representations to be obtained by pooling coefficients. The
effectiveness of the Weyl transform is demonstrated through
the example of textured image classification.},
Doi = {10.1109/TSP.2015.2505661},
Key = {fds322678}
}

@article{fds322680,
Author = {Chang, Z and Qiu, Q and Sapiro, G},
Title = {Synthesis-based low-cost gaze analysis},
Journal = {Communications in Computer and Information
Science},
Volume = {618},
Pages = {95-100},
Publisher = {Springer International Publishing},
Year = {2016},
Month = {January},
ISBN = {9783319405414},
url = {http://dx.doi.org/10.1007/978-3-319-40542-1_15},
Abstract = {© Springer International Publishing Switzerland 2016. Gaze
analysis has gained much popularity over the years due to
its relevance in a wide array of applications, including
humancomputer interaction, fatigue detection, and clinical
mental health diagnosis. However, accurate gaze estimation
from low resolution images outside of the lab (in the wild)
still proves to be a challenging task. The new Intel
low-cost RealSense 3D camera, capable of acquiring
submillimeter resolution depth information, is currently
available in laptops, and such technology is expected to
become ubiquitous in other portable devices. In this paper,
we focus on low-cost, scalable and real time analysis of
human gaze using this RealSense camera. We exploit the
direct measurement of eye surface geometry captured by the
RGB-D camera, and perform gaze estimation through novel
synthesis-based training and testing. Furthermore, we
synthesize different eye movement appearances using a linear
approach. From each 3D eye training sample captured by the
RealSense camera, we synthesize multiple novel 2D views by
varying the view angle to simulate head motions expected at
testing. We then learn from the synthesized 2D eye images a
gaze regression model using regression forests. At testing,
for each captured RGB-D eye image, we first repeat the same
synthesis process. For each synthesized image, we estimate
the gaze from our gaze regression model, and factor-out the
associated camera/head motion. In this way, we obtain
multiple gaze estimations for each RGB-D eye image, and the
consensus is adopted. We show that this synthesis-based
training and testing significantly improves the precision in
gaze estimation, opening the door to true low-cost
solutions.},
Doi = {10.1007/978-3-319-40542-1_15},
Key = {fds322680}
}

@article{fds322681,
Author = {Lyzinski, V and Fishkind, DE and Fiori, M and Vogelstein, JT and Priebe,
CE and Sapiro, G},
Title = {Graph Matching: Relax at Your Own Risk.},
Journal = {Ieee Transactions on Pattern Analysis and Machine
Intelligence},
Volume = {38},
Number = {1},
Pages = {60-73},
Year = {2016},
Month = {January},
url = {http://dx.doi.org/10.1109/TPAMI.2015.2424894},
Abstract = {Graph matching-aligning a pair of graphs to minimize their
both theoretical and applied communities over the past
several decades, including combinatorics, computer vision,
and connectomics. Its attention can be partially attributed
to its computational difficulty. Although many heuristics
have previously been proposed in the literature to
approximately solve graph matching, very few have any
theoretical support for their performance. A common
technique is to relax the discrete problem to a continuous
problem, therefore enabling practitioners to bring
gradient-descent-type algorithms to bear. We prove that an
indefinite relaxation (when solved exactly) almost always
discovers the optimal permutation, while a common convex
relaxation almost always fails to discover the optimal
permutation. These theoretical results suggest that
initializing the indefinite algorithm with the convex
optimum might yield improved practical performance. Indeed,
experimental results illuminate and corroborate these
theoretical findings, demonstrating that excellent results
are achieved in both benchmark and real data problems by
amalgamating the two approaches.},
Doi = {10.1109/TPAMI.2015.2424894},
Key = {fds322681}
}

@article{fds322213,
Author = {Carpenter, KLH and Sprechmann, P and Calderbank, R and Sapiro, G and Egger, HL},
Title = {Quantifying Risk for Anxiety Disorders in Preschool
Children: A Machine Learning Approach.},
Journal = {Plos One},
Volume = {11},
Number = {11},
Pages = {e0165524},
Year = {2016},
url = {http://dx.doi.org/10.1371/journal.pone.0165524},
Abstract = {Early childhood anxiety disorders are common, impairing, and
predictive of anxiety and mood disorders later in childhood.
Epidemiological studies over the last decade find that the
prevalence of impairing anxiety disorders in preschool
children ranges from 0.3% to 6.5%. Yet, less than 15% of
young children with an impairing anxiety disorder receive a
mental health evaluation or treatment. One possible reason
for the low rate of care for anxious preschoolers is the
lack of affordable, timely, reliable and valid tools for
identifying young children with clinically significant
anxiety. Diagnostic interviews assessing psychopathology in
young children require intensive training, take hours to
administer and code, and are not available for use outside
of research settings. The Preschool Age Psychiatric
Assessment (PAPA) is a reliable and valid structured
diagnostic parent-report interview for assessing
psychopathology, including anxiety disorders, in 2 to 5 year
old children. In this paper, we apply machine-learning tools
to already collected PAPA data from two large community
studies to identify sub-sets of PAPA items that could be
developed into an efficient, reliable, and valid screening
tool to assess a young child's risk for an anxiety disorder.
Using machine learning, we were able to decrease by an order
of magnitude the number of items needed to identify a child
who is at risk for an anxiety disorder with an accuracy of
over 96% for both generalized anxiety disorder (GAD) and
present a continuous risk score representing the child's
short question-set that assesses risk for an anxiety
disorder could be a first step toward development and
validation of a relatively short screening tool feasible for
use in pediatric clinics and daycare/preschool
settings.},
Doi = {10.1371/journal.pone.0165524},
Key = {fds322213}
}

@article{fds330346,
Author = {Hashemi, J and Campbell, K and Carpenter, KLH and Harris, A and Qiu, Q and Tepper, M and Espinosa, S and Borg, JS and Marsan, S and Calderbank, R and Baker, JP and Egger, HL and Dawson, G and Sapiro,
G},
Title = {A scalable app for measuring autism risk behaviors in young
children: A technical validity and feasibility
study},
Journal = {Mobihealth 2015 5th Eai International Conference on Wireless
Mobile Communication and Healthcare Transforming Healthcare
Through Innovations in Mobile and Wireless
Technologies},
Publisher = {ICST},
Year = {2015},
Month = {December},
url = {http://dx.doi.org/10.4108/eai.14-10-2015.2261939},
genetics and neuroscience of early childhood mental health,
behavioral observation is still the gold standard in
screening, diagnosis, and outcome assessment. Unfortunately,
clinical observation is often sub-jective, needs significant
rater training, does not capture data from participants in
their natural environment, and is not scalable for use in
large populations or for longitu-dinal monitoring. To
address these challenges, we devel-oped and tested a
self-contained app designed to measure toddlers' social
communication behaviors in a primary care, school, or home
setting. Twenty 16-30 month old children with and without
autism participated in this study. Tod-dlers watched the
developmentally-Appropriate visual stim-uli on an iPad in a
pediatric clinic and in our lab while the iPad camera
simultaneously recorded video of the child's behaviors.
Automated computer vision algorithms coded emotions and
social referencing to quantify autism risk be-haviors. We
validated our automatic computer coding by comparing the
computer-generated analysis of facial expres-sion and social
referencing to human coding of these behav-iors. We report
our method and propose the development and testing of
measures of young children's behaviors as the first step
toward development of a novel, fully integrated, low-cost,
scalable screening tool for autism and other
neu-rodevelopmental disorders of early childhood.},
Doi = {10.4108/eai.14-10-2015.2261939},
Key = {fds330346}
}

@article{fds330345,
Author = {Qiu, Q and Chang, Z and Draelos, M and Chen, J and Bronstein, A and Sapiro,
G},
Title = {Low-cost gaze and pulse analysis using realsense},
Journal = {Mobihealth 2015 5th Eai International Conference on Wireless
Mobile Communication and Healthcare Transforming Healthcare
Through Innovations in Mobile and Wireless
Technologies},
Publisher = {ICST},
Year = {2015},
Month = {December},
ISBN = {9781631900884},
url = {http://dx.doi.org/10.4108/eai.14-10-2015.2261657},
high precision RealSense 3D (RGBD) camera is becoming
ubiquitous in laptops and mobile devices starting this year,
opening the door for new applications in the mobile health
arena. In this paper, we demonstrate how the Intel RealSense
3D camera can be used for low-cost gaze tracking and passive
pulse rate estimation. We develop a novel 3D gaze and
fixation tracker based on the eye surface geometry as well
as an illumination invari-ant pulse rate estimation method
using near-infrared images captured with RealSense. We
achieve a mean error of 1 cm at 20-30 cm for the gaze
tracker and 2:26 bpm (beats per minute) for pulse
estimation, which is adequate in many medical applications,
demonstrating the great potential of novel consumer-grade
RGBD technology in mobile health.},
Doi = {10.4108/eai.14-10-2015.2261657},
Key = {fds330345}
}

@article{fds322682,
Author = {Kim, J and Duchin, Y and Sapiro, G and Vitek, J and Harel,
N},
Title = {Clinical deep brain stimulation region prediction using
regression forests from high-field MRI},
Journal = {Proceedings International Conference on Image Processing,
Icip},
Volume = {2015-December},
Pages = {2480-2484},
Publisher = {IEEE},
Year = {2015},
Month = {December},
ISBN = {9781479983391},
url = {http://dx.doi.org/10.1109/ICIP.2015.7351248},
Abstract = {© 2015 IEEE. This paper presents a prediction framework of
brain subcortical structures which are invisible on clinical
low-field MRI, learning detailed information from
ultrahigh-field MR training data. Volumetric segmentation of
Deep Brain Stimulation (DBS) structures within the Basal
ganglia is a prerequisite process for reliable DBS surgery.
While ultrahigh-field MR imaging (7 Tesla) allows direct
visualization of DBS targeting structures, such
ultrahigh-fields are not always clinically available, and
therefore the relevant structures need to be predicted from
the clinical data. We address the shape prediction problem
with a regression forest, non-linearly mapping predictors to
target structures with high confidence, exploiting
ultrahigh-field MR training data. We consider an application
for the subthalamic nucleus (STN) prediction as a crucial
DBS target. Experimental results on Parkinson's patients
validate that the proposed approach enables reliable
estimation of the STN from clinical 1.5T
MRI.},
Doi = {10.1109/ICIP.2015.7351248},
Key = {fds322682}
}

@article{fds322683,
Author = {Tepper, M and Newson, A and Sprechmann, P and Sapiro,
G},
Title = {Multi-temporal foreground detection in videos},
Journal = {Proceedings International Conference on Image Processing,
Icip},
Volume = {2015-December},
Pages = {4599-4603},
Year = {2015},
Month = {December},
ISBN = {9781479983391},
url = {http://dx.doi.org/10.1109/ICIP.2015.7351678},
Abstract = {© 2015 IEEE. A common task in video processing is the
binary separation of a video's content into either
background or moving foreground. However, many situations
require a foreground analysis with a finer temporal
granularity, in particular for objects or people which
remain immobile for a certain period of time. We propose an
efficient method which detects foreground at different
timescales, by exploiting the desirable theoretical and
practical properties of Robust Principal Component Analysis.
Our algorithm can be used in a variety of scenarios such as
detecting people who have fallen in a video, or analysing
the fluidity of road traffic, while avoiding costly
computations needed for nearest neighbours searches or
optical flow analysis. Finally, our algorithm has the useful
ability to perform motion analysis without explicitly
requiring computationally expensive motion
estimation.},
Doi = {10.1109/ICIP.2015.7351678},
Key = {fds322683}
}

@article{fds322684,
Author = {Hashemi, J and Qiu, Q and Sapiro, G},
Title = {Cross-modality pose-invariant facial expression},
Journal = {Proceedings International Conference on Image Processing,
Icip},
Volume = {2015-December},
Pages = {4007-4011},
Publisher = {IEEE},
Year = {2015},
Month = {December},
ISBN = {9781479983391},
url = {http://dx.doi.org/10.1109/ICIP.2015.7351558},
Abstract = {© 2015 IEEE. In this work, we present a dictionary learning
based framework for robust, cross-modality, and
pose-invariant facial expression recognition. The proposed
framework first learns a dictionary that i) contains both 3D
shape and morphological information as well as 2D texture
and geometric information, ii) enforces coherence across
both 2D and 3D modalities and different poses, and iii) is
robust in the sense that a learned dictionary can be applied
across multiple facial expression datasets. We demonstrate
that enforcing domain specific block structures on the
dictionary, given a test expression sample, we can transform
such sample across different domains for tasks such as pose
alignment. We validate our approach on the task of
pose-invariant facial expression recognition on the standard
BU3D-FE and MultiPie datasets, achieving state of the art
performance.},
Doi = {10.1109/ICIP.2015.7351558},
Key = {fds322684}
}

@article{fds322214,
Author = {Draelos, M and Qiu, Q and Bronstein, A and Sapiro,
G},
Title = {Intel realsense = Real low cost gaze},
Journal = {Proceedings International Conference on Image Processing,
Icip},
Volume = {2015-December},
Pages = {2520-2524},
Publisher = {IEEE},
Year = {2015},
Month = {December},
ISBN = {9781479983391},
url = {http://dx.doi.org/10.1109/ICIP.2015.7351256},
Abstract = {© 2015 IEEE. Intel's newly-announced low-cost RealSense 3D
camera claims significantly better precision than other
currently available low-cost platforms and is expected to
become ubiquitous in laptops and mobile devices starting
this year. In this paper, we demonstrate for the first time
that the RealSense camera can be easily converted into a
real low-cost gaze tracker. Gaze has become increasingly
relevant as an input for human-computer interaction due to
its association with attention. It is also critical in
clinical mental health diagnosis. We present a novel 3D gaze
and fixation tracker based on the eye surface geometry
captured with the RealSense 3D camera. First, eye surface 3D
point clouds are segmented to extract the pupil center and
iris using registered infrared images. With non-ellipsoid
eye surface and single fixation point assumptions, pupil
centers and iris normal vectors are used to first estimate
gaze (for each eye), and then a single fixation point for
both eyes simultaneously using a RANSAC-based approach. With
a simple learned bias field correction model, the fixation
tracker demonstrates mean error of approximately 1 cm at
20-30 cm, which is sufficiently adequate for gaze and
fixation tracking in human-computer interaction and mental
health diagnosis applications.},
Doi = {10.1109/ICIP.2015.7351256},
Key = {fds322214}
}

@article{fds264703,
Author = {Delbracio, M and Sapiro, G},
Title = {Removing camera shake via weighted fourier burst
accumulation},
Journal = {Ieee Transactions on Image Processing : a Publication of the
Ieee Signal Processing Society},
Volume = {24},
Number = {11},
Pages = {3293-3307},
Publisher = {Institute of Electrical and Electronics Engineers
(IEEE)},
Year = {2015},
Month = {November},
ISSN = {1057-7149},
url = {http://dx.doi.org/10.1109/TIP.2015.2442914},
Abstract = {© 2015 IEEE. Numerous recent approaches attempt to remove
image blur due to camera shake, either with one or multiple
input images, by explicitly solving an inverse and
inherently ill-posed deconvolution problem. If the
photographer takes a burst of images, a modality available
in virtually all modern digital cameras, we show that it is
possible to combine them to get a clean sharp version. This
is done without explicitly solving any blur estimation and
subsequent inverse problem. The proposed algorithm is
strikingly simple: it performs a weighted average in the
Fourier domain, with weights depending on the Fourier
spectrum magnitude. The method can be seen as a
generalization of the align and average procedure, with a
weighted average, motivated by hand-shake physiology and
theoretically supported, taking place in the Fourier domain.
The method's rationale is that camera shake has a random
nature, and therefore, each image in the burst is generally
blurred differently. Experiments with real camera data, and
extensive comparisons, show that the proposed Fourier burst
accumulation algorithm achieves state-of-the-art results an
order of magnitude faster, with simplicity for on-board
implementation on camera phones. Finally, we also present
experiments in real high dynamic range (HDR) scenes, showing
how the method can be straightforwardly extended to HDR
photography.},
Doi = {10.1109/TIP.2015.2442914},
Key = {fds264703}
}

@article{fds322685,
Author = {Delbracio, M and Sapiro, G},
Title = {Burst deblurring: Removing camera shake through fourier
burst accumulation},
Journal = {Proceedings of the Ieee Computer Society Conference on
Computer Vision and Pattern Recognition},
Volume = {07-12-June-2015},
Pages = {2385-2393},
Publisher = {IEEE},
Year = {2015},
Month = {October},
ISBN = {9781467369640},
url = {http://dx.doi.org/10.1109/CVPR.2015.7298852},
Abstract = {© 2015 IEEE. Numerous recent approaches attempt to remove
image blur due to camera shake, either with one or multiple
input images, by explicitly solving an inverse and
inherently ill-posed deconvolution problem. If the
photographer takes a burst of images, a modality available
in virtually all modern digital cameras, we show that it is
possible to combine them to get a clean sharp version. This
is done without explicitly solving any blur estimation and
subsequent inverse problem. The proposed algorithm is
strikingly simple: it performs a weighted average in the
Fourier domain, with weights depending on the Fourier
spectrum magnitude. The method's rationale is that camera
shake has a random nature and therefore each image in the
burst is generally blurred differently. Experiments with
real camera data show that the proposed Fourier Burst
Accumulation algorithm achieves state-of-the-art results an
order of magnitude faster, with simplicity for on-board
implementation on camera phones.},
Doi = {10.1109/CVPR.2015.7298852},
Key = {fds322685}
}

@article{fds304066,
Author = {Sprechmann, P and Bronstein, AM and Sapiro, G},
Title = {Learning Efficient Sparse and Low Rank Models.},
Journal = {Ieee Transactions on Pattern Analysis and Machine
Intelligence},
Volume = {37},
Number = {9},
Pages = {1821-1833},
Year = {2015},
Month = {September},
url = {http://arxiv.org/abs/1212.3631v1},
Abstract = {Parsimony, including sparsity and low rank, has been shown
to successfully model data in numerous machine learning and
approaches rely on an iterative algorithm that minimizes an
objective function with parsimony-promoting terms. The
inherently sequential structure and data-dependent
complexity and latency of iterative optimization constitute
a major limitation in many applications requiring real-time
performance or involving large-scale data. Another
limitation encountered by these modeling techniques is the
difficulty of their inclusion in discriminative learning
scenarios. In this work, we propose to move the emphasis
from the model to the pursuit algorithm, and develop a
process-centric view of parsimonious modeling, in which a
learned deterministic fixed-complexity pursuit process is
used in lieu of iterative optimization. We show a principled
way to construct learnable pursuit process architectures for
structured sparse and robust low rank models, derived from
the iteration of proximal descent algorithms. These
architectures learn to approximate the exact parsimonious
representation at a fraction of the complexity of the
standard optimization methods. We also show that appropriate
training regimes allow to naturally extend parsimonious
models to discriminative settings. State-of-the-art results
are demonstrated on several challenging problems in image
and audio processing with several orders of magnitude
speed-up compared to the exact optimization
algorithms.},
Doi = {10.1109/tpami.2015.2392779},
Key = {fds304066}
}

@article{fds322679,
Author = {Huang, J and Qiu, Q and Calderbank, R and Sapiro,
G},
Title = {Geometry-aware deep transform},
Journal = {Proceedings of the Ieee International Conference on Computer
Vision},
Volume = {2015 International Conference on Compute},
Pages = {4139-4147},
Publisher = {IEEE},
Year = {2015},
Month = {February},
ISBN = {9781467383912},
url = {http://dx.doi.org/10.1109/ICCV.2015.471},
Abstract = {© 2015 IEEE. Many recent efforts have been devoted to
designing sophisticated deep learning structures, obtaining
revolutionary results on benchmark datasets. The success of
these deep learning methods mostly relies on an enormous
volume of labeled training samples to learn a huge number of
parameters in a network, therefore, understanding the
generalization ability of a learned deep network cannot be
overlooked, especially when restricted to a small training
set, which is the case for many applications. In this paper,
we propose a novel deep learning objective formulation that
unifies both the classification and metric learning
criteria. We then introduce a geometry-aware deep transform
to enable a non-linear discriminative and robust feature
transform, which shows competitive performance on small
training sets for both synthetic and real-world data. We
further support the proposed framework with a formal
(K)-robustness analysis.},
Doi = {10.1109/ICCV.2015.471},
Key = {fds322679}
}

@article{fds304057,
Author = {Qiu, Q and Sapiro, G},
Title = {Learning transformations for clustering and
classification},
Journal = {Journal of Machine Learning Research},
Volume = {16},
Pages = {187-225},
Year = {2015},
Month = {February},
url = {http://arxiv.org/abs/1309.2074v2},
Abstract = {©2015 Qiang Qiu and Guillermo Sapiro. A low-rank
transformation learning framework for subspace clustering
and classification is proposed here. Many high-dimensional
data, such as face images and motion sequences,
approximately lie in a union of low-dimensional subspaces.
The corresponding subspace clustering problem has been
extensively studied in the literature to partition such
high-dimensional data into clusters corresponding to their
underlying low-dimensional subspaces. Low-dimensional
intrinsic structures are often violated for real-world
observations, as they can be corrupted by errors or deviate
from ideal models. We propose to address this by learning a
linear transformation on subspaces using nuclear norm as the
modeling and optimization criteria. The learned linear
transformation restores a low-rank structure for data from
the same subspace, and, at the same time, forces a maximally
separated structure for data from different subspaces. In
this way, we reduce variations within the subspaces, and
increase separation between the subspaces for a more robust
subspace clustering. This proposed learned robust subspace
clustering framework significantly enhances the performance
of existing subspace clustering methods. Basic theoretical
results presented here help to further support the
underlying framework. To exploit the low-rank structures of
the transformed subspaces, we further introduce a fast
subspace clustering technique, which efficiently combines
robust PCA with sparse modeling. When class labels are
present at the training stage, we show this low-rank
transformation framework also significantly enhances
classification performance. Extensive experiments using
public data sets are presented, showing that the proposed
approach significantly outperforms state-of-the-art methods
for subspace clustering and classification. The learned low
cost transform is also applicable to other classification
frameworks.},
Key = {fds304057}
}

@article{fds264698,
Author = {Lucas, JE and Sapiro, G},
Title = {Cancer: What's luck got to do with it?},
Journal = {Significance},
Volume = {12},
Number = {2},
Pages = {40-42},
Publisher = {WILEY},
Year = {2015},
Month = {January},
ISSN = {1740-9705},
url = {http://dx.doi.org/10.1111/j.1740-9713.2015.00816.x},
Abstract = {© 2015 The Royal Statistical Society. Recent press reports
would have you believe that cancer develops randomly, and
healthy living makes little difference. But that is a gross
misinterpretation of a recent scientific paper, as Joseph E.
Lucas and Guillermo Sapiro explain.},
Doi = {10.1111/j.1740-9713.2015.00816.x},
Key = {fds264698}
}

@article{fds264706,
Author = {Yang, J and Liao, X and Yuan, X and Llull, P and Brady, DJ and Sapiro, G and Carin, L},
Title = {Compressive sensing by learning a Gaussian mixture model
from measurements.},
Journal = {Ieee Transactions on Image Processing : a Publication of the
Ieee Signal Processing Society},
Volume = {24},
Number = {1},
Pages = {106-119},
Year = {2015},
Month = {January},
ISSN = {1057-7149},
url = {http://dx.doi.org/10.1109/tip.2014.2365720},
Abstract = {Compressive sensing of signals drawn from a Gaussian mixture
model (GMM) admits closed-form minimum mean squared error
reconstruction from incomplete linear measurements. An
accurate GMM signal model is usually not available a priori,
because it is difficult to obtain training signals that
match the statistics of the signals being sensed. We propose
to solve that problem by learning the signal model in situ,
based directly on the compressive measurements of the
signals, without resorting to other signals to train a
model. A key feature of our method is that the signals being
sensed are treated as random variables and are integrated
out in the likelihood. We derive a maximum marginal
likelihood estimator (MMLE) that maximizes the likelihood of
the GMM of the underlying signals given only their linear
compressive measurements. We extend the MMLE to a GMM with
dominantly low-rank covariance matrices, to gain
computational speedup. We report extensive experimental
results on image inpainting, compressive sensing of
high-speed video, and compressive hyperspectral imaging (the
latter two based on real compressive cameras). The results
demonstrate that the proposed methods outperform
state-of-the-art methods by significant margins.},
Doi = {10.1109/tip.2014.2365720},
Key = {fds264706}
}

@article{fds291305,
Author = {Kim, J and Duchin, Y and Sapiro, G and Vitek, J and Harel,
N},
Title = {Clinical subthalamic nucleus prediction from high-field
brain MRI},
Journal = {Proceedings International Symposium on Biomedical
Imaging},
Volume = {2015-July},
Pages = {1264-1267},
Publisher = {IEEE},
Year = {2015},
Month = {January},
ISBN = {9781479923748},
ISSN = {1945-7928},
url = {http://dx.doi.org/10.1109/ISBI.2015.7164104},
Abstract = {© 2015 IEEE. The subthalamic nucleus (STN) within the
sub-cortical region of the Basal ganglia is a crucial
targeting structure for Parkinson's Deep brain stimulation
(DBS) surgery. Volumetric segmentation of such small and
complex structure, which is elusive in clinical MRI
protocols, is thereby a pre-requisite process for reliable
DBS direct targeting. While direct visualization of the STN
is facilitated with advanced ultrahigh-field MR imaging (7
Tesla), such high fields are not always clinically
available. In this paper, we aim at the automatic prediction
of the STN region on clinical low-field MRI, exploiting
dependencies between the STN and its adjacent structures,
learned from ultrahigh-field MRI. We present a framework
based on a statistical shape model to learn such shape
relationship on high quality MR data sets. This allows for
an accurate prediction and visualization of the STN
structure, given detectable predictors on the low-field MRI.
Experimental results on Parkinson's patients demonstrate
that the proposed approach enables accurate estimation of
the STN on clinical 1.5T MRI.},
Doi = {10.1109/ISBI.2015.7164104},
Key = {fds291305}
}

@article{fds335974,
Author = {Llull, P and Yuan, X and Liao, X and Yang, J and Kittle, D and Carin, L and Sapiro, G and Brady, DJ},
Title = {Temporal compressive sensing for video},
Number = {9783319160412},
Pages = {41-74},
Publisher = {Springer International Publishing},
Year = {2015},
Month = {January},
ISBN = {9783319160412},
url = {http://dx.doi.org/10.1007/978-3-319-16042-9_2},
Abstract = {© Springer International Publishing Switzerland 2015. Video
camera architects must design cameras capable of
high-quality, dynamic event capture, while adhering to power
and communications constraints. Though modern imagers are
capable of both simultaneous spatial and temporal
resolutions at micrometer and microsecond scales, the power
required to sample at these rates is undesirable. The field
of compressive sensing (CS) has recently suggested a
solution to this design challenge. By exploiting
physical-layer compression strategies, one may overlay the
original scene with a coding sequence to sample at
sub-Nyquist rates with virtually no additional power
requirement. The underlying scene may be later estimated
without significant loss of fidelity. In this chapter, we
cover a variety of such strategies taken to improve an
imager’s temporal resolution. Highlighting a new low-power
acquisition paradigm, we show how a video sequence of high
temporal resolution may be reconstructed from a single video
frame taken with a low-framerate camera.},
Doi = {10.1007/978-3-319-16042-9_2},
Key = {fds335974}
}

@article{fds322686,
Author = {Huang, J and Qiu, Q and Calderbank, R and Rodrigues, M and Sapiro,
G},
Title = {Alignment with intra-class structure can improve
classification},
Journal = {2015 Ieee International Conference on Acoustics, Speech, and
Signal Processing (Icassp)},
Volume = {2015-August},
Pages = {1921-1925},
Publisher = {IEEE},
Year = {2015},
Month = {January},
ISBN = {9781467369978},
url = {http://dx.doi.org/10.1109/ICASSP.2015.7178305},
Abstract = {© 2015 IEEE. High dimensional data is modeled using
low-rank subspaces, and the probability of misclassification
is expressed in terms of the principal angles between
subspaces. The form taken by this expression motivates the
design of a new feature extraction method that enlarges
inter-class separation, while preserving intra-class
structure. The method can be tuned to emphasize different
features shared by members within the same class.
Classification performance is compared to that of
state-of-the-art methods on synthetic data and on the real
face database. The probability of misclassification is
decreased when intra-class structure is taken into
account.},
Doi = {10.1109/ICASSP.2015.7178305},
Key = {fds322686}
}

@article{fds322688,
Author = {Kim, J and Duchin, Y and Kim, H and Vitek, J and Harel, N and Sapiro,
G},
Title = {Robust prediction of clinical deep brain stimulation target
structures via the estimation of influential high-field MR
atlases},
Journal = {Lecture Notes in Computer Science (Including Subseries
Lecture Notes in Artificial Intelligence and Lecture Notes
in Bioinformatics)},
Volume = {9350},
Pages = {587-594},
Publisher = {Springer International Publishing},
Year = {2015},
Month = {January},
ISBN = {9783319245706},
url = {http://dx.doi.org/10.1007/978-3-319-24571-3_70},
Abstract = {© Springer International Publishing Switzerland 2015. This
work introduces a robust framework for predicting Deep Brain
Stimulation (DBS) target structures which are not
identifiable on standard clinical MRI. While recent
high-field MR imaging allows clear visualization of DBS
target structures, such high-fields are not clinically
available, and therefore DBS targeting needs to be performed
on the standard clinical low contrast data. We first learn
via regression models the shape relationships between DBS
targets and their potential predictors from high-field (7
Tesla) MR training sets. A bagging procedure is utilized in
the regression model, reducing the variability of learned
dependencies. Then, given manually or automatically detected
predictors on the clinical patient data, the target
structure is predicted using the learned high quality
information. Moreover, we derive a robust way to properly
weight different training subsets, yielding higher accuracy
when using an ensemble of predictions. The subthalamic
nucleus (STN), the most common DBS target for Parkinson’s
disease, is used to exemplify within our framework.
Experimental validation from Parkinson’s patients shows
that the proposed approach enables reliable prediction of
the STN from the clinical 1.5T MR data.},
Doi = {10.1007/978-3-319-24571-3_70},
Key = {fds322688}
}

@article{fds322689,
Author = {Huang, J and Qiu, Q and Sapiro, G and Calderbank,
R},
Title = {Discriminative robust transformation learning},
Journal = {Advances in Neural Information Processing
Systems},
Volume = {2015-January},
Pages = {1333-1341},
Year = {2015},
Month = {January},
Abstract = {This paper proposes a framework for learning features that
are robust to data variation, which is particularly
important when only a limited number of training samples are
available. The framework makes it possible to tradeoff the
discriminative value of learned features against the
generalization error of the learning algorithm. Robustness
is achieved by encouraging the transform that maps data to
features to be a local isometry. This geometric property is
shown to improve (K, ∈)-robustness, thereby providing
theoretical justification for reductions in generalization
error observed in experiments. The proposed optimization
framework is used to train standard learning algorithms such
as deep neural networks. Experimental results obtained on
benchmark datasets, such as labeled faces in the wild,
demonstrate the value of being able to balance
discrimination and robustness.},
Key = {fds322689}
}

@article{fds322690,
Author = {Tepper, M and Sapiro, G},
Title = {From local to global communities in large networks through
consensus},
Journal = {Lecture Notes in Computer Science (Including Subseries
Lecture Notes in Artificial Intelligence and Lecture Notes
in Bioinformatics)},
Volume = {9423},
Pages = {659-666},
Publisher = {Springer International Publishing},
Year = {2015},
Month = {January},
ISBN = {9783319257501},
url = {http://dx.doi.org/10.1007/978-3-319-25751-8_79},
Abstract = {© Springer International Publishing Switzerland 2015. Given
a universe of local communities of a large network, we aim
at identifying the meaningful and consistent communities in
it. We address this from a new perspective as the process of
obtaining consensual community detections and formalize it
as a bi-clustering problem. We obtain the global community
structure of the given network without running expensive
global community detection algorithms. The proposed
mathematical characterization of the consensus problem and a
new biclustering algorithm to solve it render the problem
tractable for large networks. The approach is successfully
validated in experiments with synthetic and large real-world
networks, outperforming other state-ofthe-art alternatives
in terms of speed and results quality.},
Doi = {10.1007/978-3-319-25751-8_79},
Key = {fds322690}
}

@article{fds264705,
Author = {Tepper, M and Sapiro, G},
Title = {A biclustering framework for consensus problems},
Journal = {Siam Journal on Imaging Sciences},
Volume = {7},
Number = {4},
Pages = {2488-2552},
Publisher = {Society for Industrial & Applied Mathematics
(SIAM)},
Year = {2014},
Month = {November},
url = {http://dx.doi.org/10.1137/140967325},
Abstract = {© 2014 Society for Industrial and Applied Mathematics. We
consider grouping as a general characterization for problems
such as clustering, community detection in networks, and
multiple parametric model estimation. We are interested in
merging solutions from different grouping algorithms,
distilling all their good qualities into a consensus
solution. In this paper, we propose a biclustering framework
and perspective for reaching consensus in such grouping
problems. In particular, this is the first time that the
task of finding/fitting multiple parametric models to a
dataset is formally posed as a consensus problem. We
highlight the equivalence of these tasks and establish the
connection with the computational Gestalt program, which
seeks to provide a psychologically inspired detection theory
for visual events. We also present a simple but powerful
biclustering algorithm, specially tuned to the nature of the
problem we address, though general enough to handle many
different instances inscribed within our characterization.
The presentation is accompanied with diverse and extensive
experimental results in clustering, community detection, and
multiple parametric model estimation in image processing
applications.},
Doi = {10.1137/140967325},
Key = {fds264705}
}

@article{fds264708,
Author = {Yang, J and Yuan, X and Liao, X and Llull, P and Brady, DJ and Sapiro, G and Carin, L},
Title = {Video compressive sensing using Gaussian mixture
models.},
Journal = {Ieee Transactions on Image Processing : a Publication of the
Ieee Signal Processing Society},
Volume = {23},
Number = {11},
Pages = {4863-4878},
Year = {2014},
Month = {November},
ISSN = {1057-7149},
url = {http://dx.doi.org/10.1109/tip.2014.2344294},
Abstract = {A Gaussian mixture model (GMM)-based algorithm is proposed
for video reconstruction from temporally compressed video
measurements. The GMM is used to model spatio-temporal video
patches, and the reconstruction can be efficiently computed
based on analytic expressions. The GMM-based inversion
method benefits from online adaptive learning and parallel
computation. We demonstrate the efficacy of the proposed
inversion method with videos reconstructed from simulated
compressive video measurements, and from a real compressive
video camera. We also use the GMM as a tool to investigate
temporal compression.},
Doi = {10.1109/tip.2014.2344294},
Key = {fds264708}
}

@article{fds264709,
Author = {Duarte-Carvajalino, JM and Lenglet, C and Xu, J and Yacoub, E and Ugurbil, K and Moeller, S and Carin, L and Sapiro,
G},
Title = {Estimation of the CSA-ODF using Bayesian compressed sensing
of multi-shell HARDI.},
Journal = {Magnetic Resonance in Medicine},
Volume = {72},
Number = {5},
Pages = {1471-1485},
Year = {2014},
Month = {November},
ISSN = {0740-3194},
url = {http://dx.doi.org/10.1002/mrm.25046},
Abstract = {Diffusion MRI provides important information about the brain
white matter structures and has opened new avenues for
neuroscience and translational research. However,
acquisition time needed for advanced applications can still
be a challenge in clinical settings. There is consequently a
need to accelerate diffusion MRI acquisitions.A multi-task
Bayesian compressive sensing (MT-BCS) framework is proposed
to directly estimate the constant solid angle orientation
distribution function (CSA-ODF) from under-sampled (i.e.,
accelerated image acquisition) multi-shell high angular
resolution diffusion imaging (HARDI) datasets, and
accurately recover HARDI data at higher resolution in
q-space. The proposed MT-BCS approach exploits the spatial
redundancy of the data by modeling the statistical
relationships within groups (clusters) of diffusion signal.
This framework also provides uncertainty estimates of the
computed CSA-ODF and diffusion signal, directly computed
from the compressive measurements. Experiments validating
the proposed framework are performed using realistic
multi-shell synthetic images and in vivo multi-shell high
angular resolution HARDI datasets.Results indicate a
practical reduction in the number of required diffusion
volumes (q-space samples) by at least a factor of four to
estimate the CSA-ODF from multi-shell data.This work
presents, for the first time, a multi-task Bayesian
compressive sensing approach to simultaneously estimate the
full posterior of the CSA-ODF and diffusion-weighted volumes
from multi-shell HARDI acquisitions. It demonstrates
improvement of the quality of acquired datasets by means of
CS de-noising, and accurate estimation of the CSA-ODF, as
well as enables a reduction in the acquisition time by a
factor of two to four, especially when "staggered" q-space
sampling schemes are used. The proposed MT-BCS framework can
naturally be combined with parallel MR imaging to further
accelerate HARDI acquisitions.},
Doi = {10.1002/mrm.25046},
Key = {fds264709}
}

@article{fds264710,
Author = {Kim, J and Lenglet, C and Duchin, Y and Sapiro, G and Harel,
N},
Title = {Semiautomatic segmentation of brain subcortical structures
from high-field MRI.},
Journal = {Ieee Journal of Biomedical and Health Informatics},
Volume = {18},
Number = {5},
Pages = {1678-1695},
Year = {2014},
Month = {September},
ISSN = {2168-2194},
url = {http://dx.doi.org/10.1109/JBHI.2013.2292858},
Abstract = {Volumetric segmentation of subcortical structures, such as
the basal ganglia and thalamus, is necessary for noninvasive
diagnosis and neurosurgery planning. This is a challenging
problem due in part to limited boundary information between
structures, similar intensity profiles across the different
structures, and low contrast data. This paper presents a
semiautomatic segmentation system exploiting the superior
image quality of ultrahigh field (7 T) MRI. The proposed
approach utilizes the complementary edge information in the
multiple structural MRI modalities. It combines optimally
selected two modalities from susceptibility-weighted,
T2-weighted, and diffusion MRI, and introduces a tailored
new edge indicator function. In addition to this, we employ
prior shape and configuration knowledge of the subcortical
structures in order to guide the evolution of geometric
active surfaces. Neighboring structures are segmented
iteratively, constraining oversegmentation at their borders
with a nonoverlapping penalty. Several experiments with data
acquired on a 7 T MRI scanner demonstrate the feasibility
and power of the approach for the segmentation of basal
ganglia components critical for neurosurgery applications
such as deep brain stimulation surgery.},
Doi = {10.1109/JBHI.2013.2292858},
Key = {fds264710}
}

@article{fds264720,
Author = {Prasad, G and Joshi, SH and Jahanshad, N and Villalon-Reina, J and Aganj, I and Lenglet, C and Sapiro, G and McMahon, KL and de Zubicaray,
GI and Martin, NG and Wright, MJ and Toga, AW and Thompson,
PM},
Title = {Automatic clustering and population analysis of white matter
tracts using maximum density paths.},
Journal = {Neuroimage},
Volume = {97},
Pages = {284-295},
Year = {2014},
Month = {August},
ISSN = {1053-8119},
url = {http://dx.doi.org/10.1016/j.neuroimage.2014.04.033},
Abstract = {We introduce a framework for population analysis of white
matter tracts based on diffusion-weighted images of the
brain. The framework enables extraction of fibers from high
angular resolution diffusion images (HARDI); clustering of
the fibers based partly on prior knowledge from an atlas;
representation of the fiber bundles compactly using a path
following points of highest density (maximum density path;
MDP); and registration of these paths together using
geodesic curve matching to find local correspondences across
a population. We demonstrate our method on 4-Tesla HARDI
scans from 565 young adults to compute localized statistics
across 50 white matter tracts based on fractional anisotropy
(FA). Experimental results show increased sensitivity in the
determination of genetic influences on principal fiber
tracts compared to the tract-based spatial statistics (TBSS)
method. Our results show that the MDP representation reveals
important parts of the white matter structure and
considerably reduces the dimensionality over comparable
fiber matching approaches.},
Doi = {10.1016/j.neuroimage.2014.04.033},
Key = {fds264720}
}

@article{fds264723,
Author = {Harrison, BD and Hashemi, J and Bibi, M and Pulver, R and Bavli, D and Nahmias, Y and Wellington, M and Sapiro, G and Berman,
J},
Title = {A tetraploid intermediate precedes aneuploid formation in
yeasts exposed to fluconazole.},
Journal = {Plos Biology},
Volume = {12},
Number = {3},
Pages = {e1001815},
Year = {2014},
Month = {March},
ISSN = {1544-9173},
url = {http://dx.doi.org/10.1371/journal.pbio.1001815},
Abstract = {Candida albicans, the most prevalent human fungal pathogen,
is generally diploid. However, 50% of isolates that are
resistant to fluconazole (FLC), the most widely used
antifungal, are aneuploid and some aneuploidies can confer
FLC resistance. To ask if FLC exposure causes or only
selects for aneuploidy, we analyzed diploid strains during
exposure to FLC using flow cytometry and epifluorescence
microscopy. FLC exposure caused a consistent deviation from
normal cell cycle regulation: nuclear and spindle cycles
initiated prior to bud emergence, leading to "trimeras,"
three connected cells composed of a mother, daughter, and
granddaughter bud. Initially binucleate, trimeras underwent
coordinated nuclear division yielding four daughter nuclei,
two of which underwent mitotic collapse to form a tetraploid
cell with extra spindle components. In subsequent cell
cycles, the abnormal number of spindles resulted in unequal
DNA segregation and viable aneuploid progeny. The process of
aneuploid formation in C. albicans is highly reminiscent of
early stages in human tumorigenesis in that aneuploidy
arises through a tetraploid intermediate and subsequent
unequal DNA segregation driven by multiple spindles coupled
with a subsequent selective advantage conferred by at least
some aneuploidies during growth under stress. Finally,
trimera formation was detected in response to other azole
antifungals, in related Candida species, and in an in vivo
model for Candida infection, suggesting that aneuploids
arise due to azole treatment of several pathogenic yeasts
and that this can occur during the infection
process.},
Doi = {10.1371/journal.pbio.1001815},
Key = {fds264723}
}

@article{fds264699,
Author = {Tepper, M and Sapiro, G},
Title = {Intersecting 2D lines: A simple method for detecting
vanishing points},
Journal = {2014 Ieee International Conference on Image Processing, Icip
2014},
Pages = {1056-1060},
Publisher = {IEEE},
Year = {2014},
Month = {January},
ISBN = {9781479957514},
url = {http://dx.doi.org/10.1109/ICIP.2014.7025210},
Abstract = {© 2014 IEEE. We present a simple and powerful technique for
testing with a prescribed precision whether a set of 2D
lines meet at a given point. The method is based on a
probabilistic framework and has a fundamental geometric
interpretation. We use this technique for detecting
vanishing points in images. We developed a very simple
algorithm that yields state-of-the-art results at a much
lower computational cost than its competitors. The
presentation of the proposed formulation is complemented
with numerous examples.},
Doi = {10.1109/ICIP.2014.7025210},
Key = {fds264699}
}

@article{fds264700,
Author = {Qiu, Q and Sapiro, G},
Title = {Learning Transformations},
Journal = {2014 Ieee International Conference on Image Processing, Icip
2014},
Pages = {4008-4012},
Publisher = {IEEE},
Year = {2014},
Month = {January},
ISBN = {9781479957514},
url = {http://dx.doi.org/10.1109/ICIP.2014.7025814},
Abstract = {© 2014 IEEE. A low-rank transformation learning framework
for subspace clustering and classification is here proposed.
Many high-dimensional data, such as face images and motion
sequences, approximately lie in a union of low-dimensional
subspaces. The corresponding subspace clustering problem has
been extensively studied in the literature, partitioning
such high-dimensional data into clusters corresponding to
their underlying low-dimensional subspaces. However,
low-dimensional intrinsic structures are often violated for
real-world observations, as they can be corrupted by errors
or deviate from ideal models. We propose to address this by
learning a linear transformation on subspaces using matrix
rank, via its convex surrogate nuclear norm, as the
optimization criteria. The learned linear transformation
restores a low-rank structure for data from the same
subspace, and, at the same time, forces a high-rank
structure for data from different subspaces. In this way, we
reduce variations within the subspaces, and increase
separation between the subspaces for improved subspace
clustering and classification.},
Doi = {10.1109/ICIP.2014.7025814},
Key = {fds264700}
}

@article{fds264701,
Author = {Qiu, Q and Sapiro, G},
Title = {Learning compressed image classification
features},
Journal = {2014 Ieee International Conference on Image Processing, Icip
2014},
Pages = {5761-5765},
Publisher = {IEEE},
Year = {2014},
Month = {January},
ISBN = {9781479957514},
url = {http://dx.doi.org/10.1109/ICIP.2014.7026165},
Abstract = {© 2014 IEEE. Learning a transformation-based dimension
reduction, thereby compressive, technique for classification
is here proposed. High-dimensional data often approximately
lie in a union of low-dimensional subspaces. We propose to
perform dimension reduction by learning a 'fat' linear
transformation matrix on subspaces using nuclear norm as the
optimization criteria. The learned transformation enables
dimension reduction, and, at the same time, restores a
low-rank structure for data from the same class and
maximizes the separation between different classes, thereby
improving classification via learned low-dimensional
features. Theoretical and experimental results support the
proposed framework, which can be interpreted as learning
compressing sensing matrices for classification.},
Doi = {10.1109/ICIP.2014.7026165},
Key = {fds264701}
}

@article{fds264697,
Author = {Yoo, TS and Lowekamp, BC and Kuybeda, O and Narayan, K and Frank, GA and Bartesaghi, A and Borgnia, M and Subramaniam, S and Sapiro, G and Ackerman, MJ},
Title = {Accelerating discovery in 3D microanalysis: Leveraging open
source software and deskside high performance
computing},
Journal = {Microscopy and Microanalysis : the Official Journal of
Microscopy Society of America, Microbeam Analysis Society,
Volume = {20},
Number = {3},
Pages = {774-775},
Publisher = {Cambridge University Press (CUP)},
Year = {2014},
Month = {January},
ISSN = {1431-9276},
url = {http://dx.doi.org/10.1017/S1431927614005595},
Doi = {10.1017/S1431927614005595},
Key = {fds264697}
}

@article{fds264707,
Author = {Yuan, X and Llull, P and Liao, X and Yang, J and Brady, DJ and Sapiro, G and Carin, L},
Title = {Low-cost compressive sensing for color video and
depth},
Journal = {Proceedings of the Ieee Computer Society Conference on
Computer Vision and Pattern Recognition},
Pages = {3318-3325},
Publisher = {IEEE},
Year = {2014},
Month = {January},
ISBN = {9781479951178},
ISSN = {1063-6919},
url = {http://dx.doi.org/10.1109/CVPR.2014.424},
Abstract = {© 2014 IEEE. A simple and inexpensive (low-power and
low-bandwidth) modification is made to a conventional
off-the-shelf color video camera, from which we recover
multiple color frames for each of the original measured
frames, and each of the recovered frames can be focused at a
different depth. The recovery of multiple frames for each
measured frame is made possible via high-speed coding,
manifested via translation of a single coded aperture, the
inexpensive translation is constituted by mounting the
binary code on a piezoelectric device. To simultaneously
recover depth information, a liquid lens is modulated at
high speed, via a variable voltage. Consequently, during the
aforementioned coding process, the liquid lens allows the
camera to sweep the focus through multiple depths. In
addition to designing and implementing the camera, fast
recovery is achieved by an anytime algorithm exploiting the
group-sparsity of wavelet/DCT coefficients.},
Doi = {10.1109/CVPR.2014.424},
Key = {fds264707}
}

@article{fds264711,
Author = {Tepper, M and Sapiro, G},
Title = {All for one, one for all: Consensus community detection in
networks},
Journal = {2015 Ieee International Conference on Acoustics, Speech, and
Signal Processing (Icassp)},
Pages = {1075-1079},
Publisher = {IEEE},
Year = {2014},
Month = {January},
ISSN = {1520-6149},
url = {http://dx.doi.org/10.1109/ICASSP.2014.6853762},
Abstract = {Given an universe of distinct, low-level communities of a
network, we aim at identifying the 'meaningful' and
consistent communities in this universe. We address this as
the process of obtaining consensual community detections and
formalize it as a bi-clustering problem. While most
consensus algorithms only take into account pairwise
relations and end up analyzing a huge matrix, our proposed
characterization of the consensus problem (1) does not drop
useful information, and (2) analyzes a much smaller matrix,
rendering the problem tractable for large networks. We also
propose a new pa-rameterless bi-clustering algorithm, fit
for the type of matrices we analyze. The approach has proven
successful in a very diverse set of experiments, ranging
from unifying the results of multiple community detection
algorithms to finding common communities from multi-modal or
Doi = {10.1109/ICASSP.2014.6853762},
Key = {fds264711}
}

@article{fds264712,
Author = {Carpenter, K and Sprechmann, P and Fiori, M and Calderbank, R and Egger,
H and Sapiro, G},
Title = {Questionnaire simplification for fast risk analysis of
children's mental health},
Journal = {2015 Ieee International Conference on Acoustics, Speech, and
Signal Processing (Icassp)},
Pages = {6009-6013},
Publisher = {IEEE},
Year = {2014},
Month = {January},
ISSN = {1520-6149},
url = {http://dx.doi.org/10.1109/ICASSP.2014.6854757},
Abstract = {Early detection and treatment of psychiatric disorders on
children has shown significant impact in their subsequent
development and quality of life. The assessment of
psychopathology in childhood is commonly carried out by
performing long comprehensive interviews such as the widely
used Preschool Age Psychiatric Assessment (PAPA).
Unfortunately, the time required to complete a full
interview is too long to apply it at the scale of the actual
population at risk, and most of the population goes
undiagnosed or is diagnosed significantly later than
desired. In this work, we aim to learn from unique and very
rich previously collected PAPA examples the
inter-correlations between different questions in order to
provide a reliable risk analysis in the form of a much
shorter interview. This helps to put such important risk
analysis at the hands of regular practitioners, including
teachers and family doctors. We use for this purpose the
alternating decision trees algorithm, which combines
decision trees with boosting to produce small and
interpretable decision rules. Rather than a binary
prediction, the algorithm provides a measure of confidence
in the classification outcome. This is highly desirable from
a clinical perspective, where it is preferable to abstain a
decision on the low-confidence cases and recommend further
screening. In order to prevent over-fitting, we propose to
use network inference analysis to predefine a set of
candidate question with consistent high correlation with the
diagnosis. We report encouraging results with high levels of
prediction using two independently collected datasets. The
length and accuracy of the developed method suggests that it
could be a valuable tool for preliminary evaluation in
Doi = {10.1109/ICASSP.2014.6854757},
Key = {fds264712}
}

@article{fds264714,
Author = {Sprechmann, P and Bronstein, AM and Sapiro, G},
Title = {Supervised non-euclidean sparse NMF via bilevel optimization
with applications to speech enhancement},
Journal = {2014 4th Joint Workshop on Hands Free Speech Communication
and Microphone Arrays, Hscma 2014},
Pages = {11-15},
Publisher = {IEEE},
Year = {2014},
Month = {January},
url = {http://dx.doi.org/10.1109/HSCMA.2014.6843241},
Abstract = {Traditionally, NMF algorithms consist of two separate
stages: a training stage, in which a generative model is
learned; and a testing stage in which the pre-learned model
is used in a high level task such as enhancement,
separation, or classification. As an alternative, we propose
spectra learned in the first stage to enhance the
performance on the specific task used in the second stage.
We cast this problem as a bilevel optimization program that
can be efficiently solved via stochastic gradient descent.
The proposed approach is general enough to handle sparsity
priors of the activations, and allow non-Euclidean data
terms such as β-divergences. The framework is evaluated on
IEEE.},
Doi = {10.1109/HSCMA.2014.6843241},
Key = {fds264714}
}

@article{fds304067,
Author = {Fiori, M and Musé, P and Sapiro, G},
Title = {A complete system for candidate polyps detection in virtual
colonoscopy},
Journal = {International Journal of Pattern Recognition and Artificial
Intelligence},
Volume = {28},
Number = {7},
Pages = {1460014-1460014},
Publisher = {World Scientific Pub Co Pte Lt},
Year = {2014},
Month = {January},
url = {http://arxiv.org/abs/1209.6525v1},
Abstract = {© World Scientific Publishing Company. We present a
computer-aided detection pipeline for polyp detection in
Computer tomographic colonography. The first stage of the
pipeline consists of a simple colon segmentation technique
that enhances polyps, which is followed by an adaptive-scale
candidate polyp delineation, in order to capture the
appropriate polyp size. In the last step, candidates are
classified based on new texture and geometric features that
consider both the information in the candidate polyp
location and its immediate surrounding area. The system is
tested with ground truth data, including flat and small
polyps which are hard to detect even with optical
colonoscopy. We achieve 100% sensitivity for polyps larger
than 6mm in size with just 0.9 false positives per case, and
93% sensitivity with 2.8 false positives per case for polyps
larger than 3mm in size.},
Doi = {10.1142/S0218001414600143},
Key = {fds304067}
}

@article{fds322691,
Author = {Dennis, EL and Zhan, L and Jahanshad, N and Mueller, BA and Jin, Y and Lenglet, C and Yacoub, E and Sapiro, G and Ugurbil, K and Harel, N and Toga, A and Lim, KO and Thompson, PM},
Title = {Rich club analysis of structural brain connectivity at 7
tesla versus 3 tesla},
Journal = {Mathematics and Visualization},
Pages = {209-218},
Publisher = {Springer International Publishing},
Year = {2014},
Month = {January},
ISBN = {9783319024745},
url = {http://dx.doi.org/10.1007/978-3-319-02475-2_19},
Abstract = {© Springer International Publishing Switzerland 2014. The
‘rich club’ is a relatively new concept in brain
connectivity analysis, which identifies a core of densely
interconnected high-degree nodes. Establishing normative
measures for rich club organization is vital, as is
understanding how scanning parameters affect it.We compared
the rich club organization in 23 subjects scanned at both 7
and 3 T, with 128-gradient high angular resolution diffusion
imaging (HARDI). The rich club coefficient (RCC) did not
differ significantly between low and high field scans, but
the field strength did affect which nodes were included in
the rich club. We also examined 3 subjects with
Alzheimer’s disease and 3 healthy elderly controls to see
how field strength affected the statistical comparison. RCC
did not differ with field strength, but again, which nodes
differed between groups did. These results illustrate how
one key parameter, scanner field strength, impacts rich club
organization – a promising concept in brain connectomics
research.},
Doi = {10.1007/978-3-319-02475-2_19},
Key = {fds322691}
}

@article{fds264715,
Author = {Hashemi, J and Tepper, M and Vallin Spina and T and Esler, A and Morellas,
V and Papanikolopoulos, N and Egger, H and Dawson, G and Sapiro,
G},
Title = {Computer vision tools for low-cost and noninvasive
measurement of autism-related behaviors in
infants.},
Journal = {Autism Research and Treatment},
Volume = {2014},
Pages = {935686},
Year = {2014},
ISSN = {2090-1925},
url = {http://hdl.handle.net/10161/9547 Duke open
access},
Abstract = {The early detection of developmental disorders is key to
child outcome, allowing interventions to be initiated which
promote development and improve prognosis. Research on
autism spectrum disorder (ASD) suggests that behavioral
signs can be observed late in the first year of life. Many
of these studies involve extensive frame-by-frame video
observation and analysis of a child's natural behavior.
Although nonintrusive, these methods are extremely
time-intensive and require a high level of observer
training; thus, they are burdensome for clinical and large
population research purposes. This work is a first milestone
in a long-term project on non-invasive early observation of
children in order to aid in risk detection and research of
neurodevelopmental disorders. We focus on providing low-cost
computer vision tools to measure and identify ASD behavioral
signs based on components of the Autism Observation Scale
for Infants (AOSI). In particular, we develop algorithms to
measure responses to general ASD risk assessment tasks and
activities outlined by the AOSI which assess visual
attention by tracking facial features. We show results,
including comparisons with expert and nonexpert clinicians,
which demonstrate that the proposed computer vision tools
can capture critical behavioral observations and potentially
augment the clinician's behavioral observations obtained
from real in-clinic assessments.},
Doi = {10.1155/2014/935686},
Key = {fds264715}
}

@article{fds264721,
Author = {Tepper, M and Sapiro, G},
Title = {Fast L1 smoothing splines with an application to Kinect
depth data},
Journal = {2013 Ieee International Conference on Image Processing, Icip
2013 Proceedings},
Pages = {504-508},
Publisher = {IEEE},
Year = {2013},
Month = {December},
url = {http://dx.doi.org/10.1109/ICIP.2013.6738104},
Abstract = {Splines are a popular and attractive way of smoothing noisy
data. Computing splines involves minimizing a functional
which is a linear combination of a fitting term and a
regularization term. The former is classically computed
using a (sometimes weighted) L2 norm while the latter
ensures smoothness. In this work we propose to replace the
L2 norm in the fitting term with an L1 norm, leading to
automatic robustness to outliers. To solve the resulting
minimization problem we propose an extremely simple and
efficient numerical scheme based on split-Bregman iteration
and a DCT-based filter. The algorithm is applied to the
problem of smoothing and impainting range data, where
high-quality results are obtained in short processing times.
Doi = {10.1109/ICIP.2013.6738104},
Key = {fds264721}
}

@article{fds264722,
Author = {Yang, J and Yuan, X and Liao, X and Llull, P and Sapiro, G and Brady, DJ and Carin, L},
Title = {Gaussian mixture model for video compressive
sensing},
Journal = {2013 Ieee International Conference on Image Processing, Icip
2013 Proceedings},
Pages = {19-23},
Publisher = {IEEE},
Year = {2013},
Month = {December},
url = {http://dx.doi.org/10.1109/ICIP.2013.6738005},
Abstract = {A Gaussian Mixture Model (GMM)-based algorithm is proposed
for video reconstruction from temporal compressed
measurements. The GMM is used to model spatio-temporal video
patches, and the reconstruction can be efficiently computed
based on analytic expressions. The developed GMM
reconstruction method benefits from online adaptive learning
and parallel computation. We demonstrate the efficacy of the
proposed GMM with videos reconstructed from simulated
compressive video measurements and from a real compressive
Doi = {10.1109/ICIP.2013.6738005},
Key = {fds264722}
}

@article{fds264725,
Author = {Tepper, M and Sapiro, G},
Title = {Ants crawling to discover the community structure in
networks},
Journal = {Lecture Notes in Computer Science (Including Subseries
Lecture Notes in Artificial Intelligence and Lecture Notes
in Bioinformatics)},
Volume = {8259 LNCS},
Number = {PART 2},
Pages = {552-559},
Publisher = {Springer Berlin Heidelberg},
Year = {2013},
Month = {December},
ISSN = {0302-9743},
url = {http://dx.doi.org/10.1007/978-3-642-41827-3_69},
Abstract = {We cast the problem of discovering the community structure
in networks as the composition of community candidates,
obtained from several community detection base algorithms,
into a coherent structure. In turn, this composition can be
cast into a maximum-weight clique problem, and we propose an
ant colony optimization algorithm to solve it. Our results
show that the proposed method is able to discover better
community structures, according to several evaluation
criteria, than the ones obtained with the base algorithms.
It also outperforms, both in quality and in speed, the
recently introduced FG-Tiling algorithm. © Springer-Verlag
2013.},
Doi = {10.1007/978-3-642-41827-3_69},
Key = {fds264725}
}

@article{fds264726,
Author = {Walczak, N and Fasching, J and Toczyski, WD and Morellas, V and Sapiro,
G and Papanikolopoulos, N},
Title = {Locating occupants in preschool classrooms using a multiple
RGB-D sensor system},
Journal = {Ieee International Conference on Intelligent Robots and
Systems},
Pages = {2166-2172},
Publisher = {IEEE},
Year = {2013},
Month = {December},
ISSN = {2153-0858},
url = {http://dx.doi.org/10.1109/IROS.2013.6696659},
Abstract = {Presented are results demonstrating that, in developing a
system with its first objective being the sustained
detection of adults and young children as they move and
interact in a normal preschool setting, the direct
application of the straightforward RGB-D innovations
presented here significantly outperforms even far more
algorithmically advanced methods relying solely on images.
The use of multiple RGB-D sensors by this project for
depth-aware object localization economically resolves
numerous issues regularly frustrating earlier vision-only
detection and human surveillance methods, issues such as
occlusions, illumination changes, unexpected postures,
atypical morphologies, erratic or unanticipated motions,
reflections, and misleading textures and colorations. This
multiple RGB-D installation forms the front-end for a
multi-step pipeline, the first portion of which seeks to
isolate, in situ, 3D renderings of classroom occupants
sufficient for a later analysis of their behaviors and
interactions. Towards this end, a voxel-based approach to
foreground/background separation and an effective adaptation
of supervoxel clustering for 3D were developed, and 3D and
image-only methods were tested and compared. The project's
setting is highly challenging, but then so are its longer
term goals: the automated detection of early childhood
precursors, ofttimes very subtle, to a number of
increasingly common developmental disorders. © 2013
IEEE.},
Doi = {10.1109/IROS.2013.6696659},
Key = {fds264726}
}

@article{fds264727,
Author = {Fiori, M and Musé, P and Sapiro, G},
Title = {Polyps flagging in virtual colonoscopy},
Journal = {Lecture Notes in Computer Science (Including Subseries
Lecture Notes in Artificial Intelligence and Lecture Notes
in Bioinformatics)},
Volume = {8259 LNCS},
Number = {PART 2},
Pages = {181-189},
Publisher = {Springer Berlin Heidelberg},
Year = {2013},
Month = {December},
ISSN = {0302-9743},
url = {http://dx.doi.org/10.1007/978-3-642-41827-3_23},
Abstract = {Computer tomographic colonography, combined with
computer-aided detection, is a promising emerging technique
for colonic polyp analysis. We present a complete pipeline
for polyp detection, starting with a simple colon
segmentation technique that enhances polyps, followed by an
classification based on new texture and geometric features
that consider both the information in the candidate polyp
and its immediate surrounding area. The proposed system is
tested with ground truth data, including challenging flat
and small polyps. For polyps larger than 6mm in size we
achieve 100% sensitivity with just 0.9 false positives per
case, and for polyps larger than 3mm in size we achieve 93%
sensitivity with 2.8 false positives per case. ©
Springer-Verlag 2013.},
Doi = {10.1007/978-3-642-41827-3_23},
Key = {fds264727}
}

@article{fds304061,
Author = {Yuan, X and Yang, J and Llull, P and Liao, X and Sapiro, G and Brady, DJ and Carin, L},
Title = {Adaptive temporal compressive sensing for
video},
Journal = {2013 Ieee International Conference on Image Processing, Icip
2013 Proceedings},
Pages = {14-18},
Year = {2013},
Month = {December},
url = {http://arxiv.org/abs/1302.3446v3},
Abstract = {This paper introduces the concept of adaptive temporal
compressive sensing (CS) for video. We propose a CS
algorithm to adapt the compression ratio based on the
scene's temporal complexity, computed from the compressed
data, without compromising the quality of the reconstructed
video. The temporal adaptivity is manifested by manipulating
the integration time of the camera, opening the possibility
to realtime implementation. The proposed algorithm is a
generalized temporal CS approach that can be incorporated
with a diverse set of existing hardware systems. © 2013
IEEE.},
Doi = {10.1109/ICIP.2013.6738004},
Key = {fds304061}
}

@article{fds264728,
Author = {Sprechmann, P and Bronstein, A and Bronstein, M and Sapiro,
G},
Title = {Learnable low rank sparse models for speech
denoising},
Journal = {2015 Ieee International Conference on Acoustics, Speech, and
Signal Processing (Icassp)},
Pages = {136-140},
Publisher = {IEEE},
Year = {2013},
Month = {October},
ISSN = {1520-6149},
url = {http://dx.doi.org/10.1109/ICASSP.2013.6637624},
Abstract = {In this paper we present a framework for real time
enhancement of speech signals. Our method leverages a new
process-centric approach for sparse and parsimonious models,
where the representation pursuit is obtained applying a
deterministic function or process rather than solving an
optimization problem. We first propose a rank-regularized
robust version of non-negative matrix factorization (NMF)
for modeling time-frequency representations of speech
signals in which the spectral frames are decomposed as
sparse linear combinations of atoms of a low-rank
dictionary. Then, a parametric family of pursuit processes
is derived from the iteration of the proximal descent method
for solving this model. We present several experiments
showing successful results and the potential of the proposed
framework. Incorporating discriminative learning makes the
proposed method significantly outperform exact NMF
algorithms, with fixed latency and at a fraction of it's
Doi = {10.1109/ICASSP.2013.6637624},
Key = {fds264728}
}

@article{fds264729,
Author = {Sprechmann, P and Bronstein, A and Morel, JM and Sapiro,
G},
Title = {Audio restoration from multiple copies},
Journal = {2015 Ieee International Conference on Acoustics, Speech, and
Signal Processing (Icassp)},
Pages = {878-882},
Publisher = {IEEE},
Year = {2013},
Month = {October},
ISSN = {1520-6149},
url = {http://dx.doi.org/10.1109/ICASSP.2013.6637774},
Abstract = {A method for removing impulse noise from audio signals by
fusing multiple copies of the same recording is introduced
in this paper. The proposed algorithm exploits the fact that
while in general multiple copies of a given recording are
available, all sharing the same master, most degradations in
audio signals are record-dependent. Our method first seeks
for the optimal non-rigid alignment of the signals that is
robust to the presence of sparse outliers with arbitrary
magnitude. Unlike previous approaches, we simultaneously
find the optimal alignment of the signals and impulsive
degradation. This is obtained via continuous dynamic time
warping computed solving an Eikonal equation. We propose to
use our approach in the derivative domain, reconstructing
the signal by solving an inverse problem that resembles the
Poisson image editing technique. The proposed framework is
here illustrated and tested in the restoration of old
gramophone recordings showing promising results; however, it
can be used in other applications where different copies of
the signal of interest are available and the degradations
Doi = {10.1109/ICASSP.2013.6637774},
Key = {fds264729}
}

@article{fds264730,
Author = {Cetingul, HE and Dumont, L and Nadar, MS and Thompson, PM and Sapiro, G and Lenglet, C},
Title = {Importance sampling spherical harmonics to improve
probabilistic tractography},
Journal = {Proceedings 2013 3rd International Workshop on Pattern
Recognition in Neuroimaging, Prni 2013},
Pages = {46-49},
Publisher = {IEEE},
Year = {2013},
Month = {October},
url = {http://dx.doi.org/10.1109/PRNI.2013.21},
Abstract = {We consider the problem of improving the accuracy and
reliability of probabilistic white matter tractography
methods by improving the built-in sampling scheme, which
randomly draws, from a diffusion model such as the
orientation distribution function (ODF), a direction of
propagation. Existing methods employing inverse transform
sampling require an ad hoc thresholding step to prevent the
less likely directions from being sampled. We herein propose
to perform importance sampling of spherical harmonics, which
redistributes an input point set on the sphere to match the
ODF using hierarchical sample warping. This produces a point
set that is more concentrated around the modes, allowing the
subsequent inverse transform sampling to generate
orientations that are in better accordance with the local
fiber configuration. Integrated into a Kalman filter-based
framework, our approach is evaluated through experiments on
synthetic, phantom, and real datasets. © 2013
IEEE.},
Doi = {10.1109/PRNI.2013.21},
Key = {fds264730}
}

@article{fds264737,
Author = {Uğurbil, K and Xu, J and Auerbach, EJ and Moeller, S and Vu, AT and Duarte-Carvajalino, JM and Lenglet, C and Wu, X and Schmitter, S and Van
de Moortele, PF and Strupp, J and Sapiro, G and De Martino and F and Wang,
D and Harel, N and Garwood, M and Chen, L and Feinberg, DA and Smith, SM and Miller, KL and Sotiropoulos, SN and Jbabdi, S and Andersson, JLR and Behrens, TEJ and Glasser, MF and Van Essen and DC and Yacoub, E and WU-Minn
HCP Consortium},
Title = {Pushing spatial and temporal resolution for functional and
diffusion MRI in the Human Connectome Project.},
Journal = {Neuroimage},
Volume = {80},
Pages = {80-104},
Year = {2013},
Month = {October},
url = {http://www.ncbi.nlm.nih.gov/pubmed/23702417},
Abstract = {The Human Connectome Project (HCP) relies primarily on three
complementary magnetic resonance (MR) methods. These are: 1)
resting state functional MR imaging (rfMRI) which uses
correlations in the temporal fluctuations in an fMRI time
series to deduce 'functional connectivity'; 2) diffusion
imaging (dMRI), which provides the input for tractography
algorithms used for the reconstruction of the complex axonal
fiber architecture; and 3) task based fMRI (tfMRI), which is
employed to identify functional parcellation in the human
brain in order to assist analyses of data obtained with the
first two methods. We describe technical improvements and
optimization of these methods as well as instrumental
choices that impact speed of acquisition of fMRI and dMRI
images at 3T, leading to whole brain coverage with 2 mm
isotropic resolution in 0.7 s for fMRI, and 1.25 mm
isotropic resolution dMRI data for tractography analysis
with three-fold reduction in total dMRI data acquisition
time. Ongoing technical developments and optimization for
acquisition of similar data at 7 T magnetic field are also
presented, targeting higher spatial resolution, enhanced
specificity of functional imaging signals, mitigation of the
inhomogeneous radio frequency (RF) fields, and reduced power
deposition. Results demonstrate that overall, these
approaches represent a significant advance in MR imaging of
the human brain to investigate brain function and
structure.},
Doi = {10.1016/j.neuroimage.2013.05.012},
Key = {fds264737}
}

@article{fds264738,
Author = {Sotiropoulos, SN and Jbabdi, S and Xu, J and Andersson, JL and Moeller,
S and Auerbach, EJ and Glasser, MF and Hernandez, M and Sapiro, G and Jenkinson, M and Feinberg, DA and Yacoub, E and Lenglet, C and Van
Essen, DC and Ugurbil, K and Behrens, TEJ and WU-Minn HCP
Consortium},
Title = {Advances in diffusion MRI acquisition and processing in the
Human Connectome Project.},
Journal = {Neuroimage},
Volume = {80},
Pages = {125-143},
Year = {2013},
Month = {October},
url = {http://www.ncbi.nlm.nih.gov/pubmed/23702418},
Abstract = {The Human Connectome Project (HCP) is a collaborative 5-year
effort to map human brain connections and their variability
in healthy adults. A consortium of HCP investigators will
study a population of 1200 healthy adults using multiple
imaging modalities, along with extensive behavioral and
genetic data. In this overview, we focus on diffusion MRI
(dMRI) and the structural connectivity aspect of the
project. We present recent advances in acquisition and
processing that allow us to obtain very high-quality in-vivo
MRI data, whilst enabling scanning of a very large number of
subjects. These advances result from 2 years of intensive
efforts in optimising many aspects of data acquisition and
processing during the piloting phase of the project. The
data quality and methods described here are representative
of the datasets and processing pipelines that will be made
freely available to the community at quarterly intervals,
beginning in 2013.},
Doi = {10.1016/j.neuroimage.2013.05.057},
Key = {fds264738}
}

@article{fds264736,
Author = {Chen, B and Polatkan, G and Sapiro, G and Blei, D and Dunson, D and Carin,
L},
Title = {Deep learning with hierarchical convolutional factor
analysis.},
Journal = {Ieee Transactions on Pattern Analysis and Machine
Intelligence},
Volume = {35},
Number = {8},
Pages = {1887-1901},
Year = {2013},
Month = {August},
url = {http://www.ncbi.nlm.nih.gov/pubmed/23787342},
Abstract = {Unsupervised multilayered (&#x201C;deep&#x201D;) models are
considered for imagery. The model is represented using a
hierarchical convolutional factor-analysis construction,
layer-dependent model parameters is implemented within a
Bayesian setting, employing a Gibbs sampler and variational
Bayesian (VB) analysis that explicitly exploit the
convolutional nature of the expansion. To address
large-scale and streaming data, an online version of VB is
also developed. The number of dictionary elements at each
layer is inferred from the data, based on a beta-Bernoulli
implementation of the Indian buffet process. Example results
are presented for several image-processing applications,
with comparisons to related models in the
literature.},
Doi = {10.1109/TPAMI.2013.19},
Key = {fds264736}
}

@article{fds264741,
Author = {Caruyer, E and Lenglet, C and Sapiro, G and Deriche,
R},
Title = {Design of multishell sampling schemes with uniform coverage
in diffusion MRI.},
Journal = {Magnetic Resonance in Medicine},
Volume = {69},
Number = {6},
Pages = {1534-1540},
Year = {2013},
Month = {June},
url = {http://www.ncbi.nlm.nih.gov/pubmed/23625329},
Abstract = {In diffusion MRI, a technique known as diffusion spectrum
imaging reconstructs the propagator with a discrete Fourier
transform, from a Cartesian sampling of the diffusion
signal. Alternatively, it is possible to directly
reconstruct the orientation distribution function in q-ball
imaging, providing so-called high angular resolution
diffusion imaging. In between these two techniques,
acquisitions on several spheres in q-space offer an
interesting trade-off between the angular resolution and the
radial information gathered in diffusion MRI. A careful
design is central in the success of multishell acquisition
and reconstruction techniques.The design of acquisition in
multishell is still an open and active field of research,
however. In this work, we provide a general method to design
multishell acquisition with uniform angular coverage. This
method is based on a generalization of electrostatic
repulsion to multishell.We evaluate the impact of our method
using simulations, on the angular resolution in one and two
bundles of fiber configurations. Compared to more commonly
used radial sampling, we show that our method improves the
angular resolution, as well as fiber crossing
discrimination.We propose a novel method to design sampling
schemes with optimal angular coverage and show the positive
impact on angular resolution in diffusion
MRI.},
Doi = {10.1002/mrm.24736},
Key = {fds264741}
}

@article{fds304060,
Author = {Spina, TV and Tepper, M and Esler, A and Morellas, V and Papanikolopoulos, N and Falcão, AX and Sapiro,
G},
Title = {Video Human Segmentation using Fuzzy Object Models and its
Application to Body Pose Estimation of Toddlers for Behavior
Studies},
Year = {2013},
Month = {May},
url = {http://arxiv.org/abs/1305.6918v1},
Abstract = {Video object segmentation is a challenging problem due to
the presence of deformable, connected, and articulated
objects, intra- and inter-object occlusions, object motion,
and poor lighting. Some of these challenges call for object
models that can locate a desired object and separate it from
its surrounding background, even when both share similar
colors and textures. In this work, we extend a fuzzy object
model, named cloud system model (CSM), to handle video
segmentation, and evaluate it for body pose estimation of
toddlers at risk of autism. CSM has been successfully used
to model the parts of the brain (cerebrum, left and right
brain hemispheres, and cerebellum) in order to automatically
locate and separate them from each other, the connected
brain stem, and the background in 3D MR-images. In our case,
the objects are articulated parts (2D projections) of the
human body, which can deform, cause self-occlusions, and
move along the video. The proposed CSM extension handles
articulation by connecting the individual clouds, body
parts, of the system using a 2D stickman model. The stickman
representation naturally allows us to extract 2D body pose
measures of arm asymmetry patterns during unsupported gait
of toddlers, a possible behavioral marker of autism. The
results show that our method can provide insightful
knowledge to assist the specialist's observations during
real in-clinic assessments.},
Key = {fds304060}
}

@article{fds264872,
Author = {Su, S and White, T and Schmidt, M and Kao, C-Y and Sapiro,
G},
Title = {Geometric computation of human gyrification indexes from
magnetic resonance images.},
Journal = {Human Brain Mapping},
Volume = {34},
Number = {5},
Pages = {1230-1244},
Year = {2013},
Month = {May},
url = {http://www.ncbi.nlm.nih.gov/pubmed/22331577},
Abstract = {Human brains are highly convoluted surfaces with multiple
folds. To characterize the complexity of these folds and
their relationship with neurological and psychiatric
conditions, different techniques have been developed to
quantify the folding patterns, also known as the surface
complexity or gyrification of the brain. In this study, the
authors propose a new geometric approach to measure the
gyrification of human brains from magnetic resonance images.
This approach is based on intrinsic 3D measurements that
relate the local brain surface area to the corresponding
area of a tightly wrapped sheet. The authors also present an
adaptation of this technique in which the geodesic depth is
incorporated into the gyrification computation. These
gyrification measures are efficiently and accurately
computed by solving geometric partial differential
equations. The presentation of the geometric framework is
complemented with experimental results for brain complexity
in typically developing children and adolescents. Using this
novel approach, the authors provide evidence for a gradual
decrease in brain surface complexity throughout childhood
and adolescence. These developmental differences occur
earlier in the occipital lobe and move anterior as children
Doi = {10.1002/hbm.21510},
Key = {fds264872}
}

@article{fds304062,
Author = {Llull, P and Liao, X and Yuan, X and Yang, J and Kittle, D and Carin, L and Sapiro, G and Brady, DJ},
Title = {Coded aperture compressive temporal imaging.},
Journal = {Optics Express},
Volume = {21},
Number = {9},
Pages = {10526-10545},
Year = {2013},
Month = {May},
url = {http://arxiv.org/abs/1302.2575v1},
Abstract = {We use mechanical translation of a coded aperture for code
division multiple access compression of video. We discuss
the compressed video's temporal resolution and present
experimental results for reconstructions of > 10 frames of
temporal data per coded snapshot.},
Doi = {10.1364/oe.21.010526},
Key = {fds304062}
}

@article{fds264747,
Author = {Harris, AK and Meyerson, JR and Matsuoka, Y and Kuybeda, O and Moran, A and Bliss, D and Das, SR and Yewdell, JW and Sapiro, G and Subbarao, K and Subramaniam, S},
Title = {Structure and accessibility of HA trimers on intact 2009
H1N1 pandemic influenza virus to stem region-specific
neutralizing antibodies.},
Journal = {Proceedings of the National Academy of Sciences of the
United States of America},
Volume = {110},
Number = {12},
Pages = {4592-4597},
Year = {2013},
Month = {March},
url = {http://www.ncbi.nlm.nih.gov/pubmed/23460696},
Abstract = {Rapid antigenic variation of HA, the major virion surface
protein of influenza A virus, remains the principal
challenge to the development of broader and more effective
vaccines. Some regions of HA, such as the stem region
proximal to the viral membrane, are nevertheless highly
conserved across strains and among most subtypes. A
fundamental question in vaccine design is the extent to
which HA stem regions on the surface of the virus are
accessible to broadly neutralizing antibodies. Here we
report 3D structures derived from cryoelectron tomography of
HA on intact 2009 H1N1 pandemic virions in the presence and
absence of the antibody C179, which neutralizes viruses
expressing a broad range of HA subtypes, including H1, H2,
H5, H6, and H9. By fitting previously derived
crystallographic structures of trimeric HA into the density
maps, we deduced the locations of the molecular surfaces of
HA involved in interaction with C179. Using computational
methods to distinguish individual unliganded HA trimers from
those that have bound C179 antibody, we demonstrate that
∼75% of HA trimers on the surface of the virus have C179
bound to the stem domain. Thus, despite their close packing
on the viral membrane, the majority of HA trimers on intact
virions are available to bind anti-stem antibodies that
target conserved HA epitopes, establishing the feasibility
of universal influenza vaccines that elicit such
antibodies.},
Doi = {10.1073/pnas.1214913110},
Key = {fds264747}
}

@article{fds265109,
Author = {Kuybeda, O and Frank, GA and Bartesaghi, A and Borgnia, M and Subramaniam, S and Sapiro, G},
Title = {A collaborative framework for 3D alignment and
classification of heterogeneous subvolumes in cryo-electron
tomography.},
Journal = {Journal of Structural Biology},
Volume = {181},
Number = {2},
Pages = {116-127},
Year = {2013},
Month = {February},
url = {http://www.ncbi.nlm.nih.gov/pubmed/23110852},
Abstract = {The limitation of using low electron doses in
non-destructive cryo-electron tomography of biological
specimens can be partially offset via averaging of aligned
and structurally homogeneous subsets present in tomograms.
This type of sub-volume averaging is especially challenging
when multiple species are present. Here, we tackle the
problem of conformational separation and alignment with a
"collaborative" approach designed to reduce the effect of
the "curse of dimensionality" encountered in standard
pair-wise comparisons. Our new approach is based on using
the nuclear norm as a collaborative similarity measure for
alignment of sub-volumes, and by exploiting the presence of
symmetry early in the processing. We provide a strict
validation of this method by analyzing mixtures of intact
simian immunodeficiency viruses SIV mac239 and SIV CP-MAC.
Electron microscopic images of these two virus preparations
are indistinguishable except for subtle differences in
conformation of the envelope glycoproteins displayed on the
surface of each virus particle. By using the nuclear
norm-based, collaborative alignment method presented here,
we demonstrate that the genetic identity of each virus
particle present in the mixture can be assigned based solely
on the structural information derived from single envelope
glycoproteins displayed on the virus surface.},
Doi = {10.1016/j.jsb.2012.10.010},
Key = {fds265109}
}

@article{fds264870,
Author = {Zhan, L and Mueller, BA and Jahanshad, N and Jin, Y and Lenglet, C and Yacoub, E and Sapiro, G and Ugurbil, K and Harel, N and Toga, AW and Lim,
KO and Thompson, PM},
Title = {Magnetic resonance field strength effects on diffusion
measures and brain connectivity networks.},
Journal = {Brain Connectivity},
Volume = {3},
Number = {1},
Pages = {72-86},
Year = {2013},
Month = {January},
url = {http://www.ncbi.nlm.nih.gov/pubmed/23205551},
Abstract = {The quest to map brain connectivity is being pursued
worldwide using diffusion imaging, among other techniques.
Even so, we know little about how brain connectivity
measures depend on the magnetic field strength of the
scanner. To investigate this, we scanned 10 healthy subjects
at 7 and 3 tesla-using 128-gradient high-angular resolution
diffusion imaging. For each subject and scan, whole-brain
tractography was used to estimate connectivity between 113
cortical and subcortical regions. We examined how scanner
field strength affects (i) the signal-to-noise ratio (SNR)
of the non-diffusion-sensitized reference images (b(0));
(ii) diffusion tensor imaging (DTI)-derived fractional
anisotropy (FA), mean, radial, and axial diffusivity
(MD/RD/AD), in atlas-defined regions; (iii) whole-brain
tractography; (iv) the 113 × 113 brain connectivity maps;
and (v) five commonly used network topology measures. We
also assessed effects of the multi-channel reconstruction
methods (sum-of-squares, SOS, at 7T; adaptive recombine, AC,
at 3T). At 7T with SOS, the b0 images had 18.3% higher SNR
than with 3T-AC. FA was similar for most regions of interest
(ROIs) derived from an online DTI atlas (ICBM81), but higher
at 7T in the cerebral peduncle and internal capsule. MD, AD,
and RD were lower at 7T for most ROIs. The apparent fiber
density between some subcortical regions was greater at
7T-SOS than 3T-AC, with a consistent connection pattern
overall. Suggesting the need for caution, the recovered
brain network was apparently more efficient at 7T, which
cannot be biologically true as the same subjects were
assessed. Care is needed when comparing network measures
across studies, and when interpreting apparently discrepant
findings.},
Doi = {10.1089/brain.2012.0114},
Key = {fds264870}
}

@article{fds311296,
Author = {Duarte-Carvajalino, JM and Yu, G and Carin, L and Sapiro,
G},
gaussian mixture models},
Journal = {Ieee Transactions on Signal Processing},
Volume = {61},
Number = {3},
Pages = {585-600},
Publisher = {Institute of Electrical and Electronics Engineers
(IEEE)},
Year = {2013},
Month = {January},
url = {http://arxiv.org/abs/1201.5404v1},
compressive sensing is developed, where a statistical model
replaces the standard sparsity model of classical
compressive sensing. We propose within this framework
optimal task-specific sensing protocols specifically and
jointly designed for classification and reconstruction. A
online sensing is applied to detect the signal class in the
first step, followed by a reconstruction step adapted to the
detected class and the observed samples. The approach is
based on information theory, here tailored for Gaussian
mixture models (GMMs), where an information-theoretic
objective relationship between the sensed signals and a
representation of the specific task of interest is
maximized. Experimental results using synthetic signals,
Landsat satellite attributes, and natural images of
different sizes and with different noise levels show the
improvements achieved using the proposed framework when
compared to more standard sensing protocols. The underlying
formulation can be applied beyond GMMs, at the price of
higher mathematical and computational complexity. ©
1991-2012 IEEE.},
Doi = {10.1109/TSP.2012.2225054},
Key = {fds311296}
}

@article{fds264716,
Author = {Elhamifar, E and Sapiro, G and Yang, A and Sasrty,
SS},
Title = {A convex optimization framework for active
learning},
Journal = {Proceedings of the Ieee International Conference on Computer
Vision},
Pages = {209-216},
Publisher = {IEEE},
Year = {2013},
Month = {January},
url = {http://dx.doi.org/10.1109/ICCV.2013.33},
Abstract = {In many image/video/web classification problems, we have
is typically expensive and time consuming to obtain labels
for the samples. Active learning is the problem of
progressively selecting and annotating the most informative
unlabeled samples, in order to obtain a high classification
performance. Most existing active learning algorithms select
only one sample at a time prior to retraining the
classifier. Hence, they are computationally expensive and
cannot take advantage of parallel labeling systems such as
Mechanical Turk. On the other hand, algorithms that allow
the selection of multiple samples prior to retraining the
classifier, may select samples that have significant
information overlap or they involve solving a non-convex
optimization. More importantly, the majority of active
learning algorithms are developed for a certain classifier
type such as SVM. In this paper, we develop an efficient
active learning framework based on convex programming, which
can select multiple samples at a time for annotation. Unlike
the state of the art, our algorithm can be used in
conjunction with any type of classifiers, including those of
the family of the recently proposed Sparse
Representation-based Classification (SRC). We use the two
principles of classifier uncertainty and sample diversity in
order to guide the optimization program towards selecting
the most informative unlabeled samples, which have the least
information overlap. Our method can incorporate the data
distribution in the selection process by using the
appropriate dissimilarity between pairs of samples. We show
the effectiveness of our framework in person detection,
scene categorization and face recognition on real-world
Doi = {10.1109/ICCV.2013.33},
Key = {fds264716}
}

@article{fds264717,
Author = {Fiori, M and Sprechmann, P and Vogelstein, J and Musé, P and Sapiro,
G},
Title = {Robust multimodal graph matching: Sparse coding meets graph
matching},
Journal = {Advances in Neural Information Processing
Systems},
Year = {2013},
Month = {January},
ISSN = {1049-5258},
Abstract = {Graph matching is a challenging problem with very important
applications in a wide range of fields, from image and video
analysis to biological and biomedical problems. We propose a
robust graph matching algorithm inspired in sparsity-related
techniques. We cast the problem, resembling group or
collaborative sparsity formulations, as a non-smooth convex
optimization problem that can be efficiently solved using
augmented Lagrangian techniques. The method can deal with
weighted or unweighted graphs, as well as multimodal data,
where different graphs represent different types of data.
The proposed approach is also naturally integrated with
collaborative graph inference techniques, solving general
network inference problems where the observed variables,
possibly coming from different modalities, are not in
correspondence. The algorithm is tested and compared with
state-of-the-art graph matching techniques in both synthetic
and real graphs. We also present results on multimodal
graphs and applications to collaborative inference of brain
connectivity from alignment-free functional magnetic
resonance imaging (fMRI) data. The code is publicly
available.},
Key = {fds264717}
}

@article{fds264718,
Author = {Sprechmann, P and Litman, R and Ben Yakar and T and Bronstein, A and Sapiro, G},
Title = {Efficient supervised sparse analysis and synthesis
operators},
Journal = {Advances in Neural Information Processing
Systems},
Year = {2013},
Month = {January},
ISSN = {1049-5258},
Abstract = {In this paper, we propose a new computationally efficient
framework for learning sparse models. We formulate a unified
approach that contains as particular cases models promoting
sparse synthesis and analysis type of priors, and mixtures
thereof. The supervised training of the proposed model is
formulated as a bilevel optimization problem, in which the
operators are optimized to achieve the best possible
performance on a specific task, e.g., reconstruction or
classification. By restricting the operators to be shift
invariant, our approach can be thought as a way of learning
sparsity-promoting convolutional operators. Leveraging
recent ideas on fast trainable regressors designed to
approximate exact sparse codes, we propose a way of
constructing feed-forward networks capable of approximating
the learned models at a fraction of the computational cost
of exact solvers. In the shift-invariant case, this leads to
a principled way of constructing a form of task-specific
convolutional networks. We illustrate the proposed models on
several experiments in music analysis and image processing
applications.},
Key = {fds264718}
}

@article{fds264719,
Author = {Llull, P and Liao, X and Yuan, X and Yang, J and Kittle, D and Carin, L and Sapiro, G and Brady, DJ},
Title = {Compressive sensing for video using a passive coding
element},
Journal = {Optics Infobase Conference Papers},
Year = {2013},
Month = {January},
Abstract = {We present a prototype system that utilizes mechanical
translation of a passive coding element to compress
high-speed temporal information into low-framerate video
sequences. Reconstructions of 148 frames per experimental
coded snapshot are reported. © OSA 2013.},
Key = {fds264719}
}

@article{fds264744,
Author = {Harris, AK and Meyerson, JR and Matsuoka, Y and Kuybeda, O and Moran, A and Bliss, D and Das, SR and Yewdell, J and Sapiro, G and Subbarao, K and Subramaniam, S},
Title = {Molecular Structures of Native HA Trimers on 2009 H1N1
Pandemic Influenza Virus Complexed with Neutralizing
Antibodies},
Journal = {Biophysical Journal},
Volume = {104},
Number = {2},
Pages = {414a-414a},
Publisher = {Elsevier BV},
Year = {2013},
Month = {January},
ISSN = {0006-3495},
Doi = {10.1016/j.bpj.2012.11.2310},
Key = {fds264744}
}

@article{fds264745,
Author = {Frank, GA and Kuybeda, O and Bartesaghi, A and Borgnia, MJ and Sapiro,
G and Subramaniam, S},
Title = {Computational Separation of Conformational Heterogeneity
using Cryo-Electron Tomography and 3D Sub-Volume
Averaging},
Journal = {Biophysical Journal},
Volume = {104},
Number = {2},
Pages = {350a-351a},
Publisher = {Elsevier BV},
Year = {2013},
Month = {January},
ISSN = {0006-3495},
Doi = {10.1016/j.bpj.2012.11.1947},
Key = {fds264745}
}

@article{fds264746,
Author = {Duarte-Carvajalino, JM and Sapiro, G and Harel, N and Lenglet,
C},
Title = {A Framework for Linear and Non-Linear Registration of
Diffusion-Weighted MRIs Using Angular Interpolation.},
Journal = {Frontiers in Neuroscience},
Volume = {7},
Pages = {41},
Year = {2013},
Month = {January},
ISSN = {1662-4548},
url = {http://www.ncbi.nlm.nih.gov/pubmed/23596381},
Abstract = {Registration of diffusion-weighted magnetic resonance images
(DW-MRIs) is a key step for population studies, or
construction of brain atlases, among other important tasks.
Given the high dimensionality of the data, registration is
usually performed by relying on scalar representative
images, such as the fractional anisotropy (FA) and
non-diffusion-weighted (b0) images, thereby ignoring much of
the directional information conveyed by DW-MR datasets
itself. Alternatively, model-based registration algorithms
have been proposed to exploit information on the preferred
fiber orientation(s) at each voxel. Models such as the
diffusion tensor or orientation distribution function (ODF)
have been used for this purpose. Tensor-based registration
methods rely on a model that does not completely capture the
information contained in DW-MRIs, and largely depends on the
accurate estimation of tensors. ODF-based approaches are
more recent and computationally challenging, but also better
describe complex fiber configurations thereby potentially
improving the accuracy of DW-MRI registration. A new
algorithm based on angular interpolation of the
diffusion-weighted volumes was proposed for affine
registration, and does not rely on any specific local
diffusion model. In this work, we first extensively compare
the performance of registration algorithms based on (i)
angular interpolation, (ii) non-diffusion-weighted scalar
volume (b0), and (iii) diffusion tensor image (DTI).
Moreover, we generalize the concept of angular interpolation
(AI) to non-linear image registration, and implement it in
the FMRIB Software Library (FSL). We demonstrate that AI
registration of DW-MRIs is a powerful alternative to volume
and tensor-based approaches. In particular, we show that AI
improves the registration accuracy in many cases over
existing state-of-the-art algorithms, while providing
registered raw DW-MRI data, which can be used for any
subsequent analysis.},
Doi = {10.3389/fnins.2013.00041},
Key = {fds264746}
}

@article{fds264748,
Author = {Caruyer, E and Aganj, I and Lenglet, C and Sapiro, G and Deriche,
R},
Title = {Motion Detection in Diffusion MRI via Online ODF
Estimation.},
Journal = {International Journal of Biomedical Imaging},
Volume = {2013},
Pages = {849363},
Year = {2013},
Month = {January},
ISSN = {1687-4188},
url = {http://www.ncbi.nlm.nih.gov/pubmed/23509445},
Abstract = {The acquisition of high angular resolution diffusion MRI is
particularly long and subject motion can become an issue.
The orientation distribution function (ODF) can be
reconstructed online incrementally from diffusion-weighted
MRI with a Kalman filtering framework. This online
reconstruction provides real-time feedback throughout the
first adapted to the reconstruction of the ODF in constant
solid angle. Then, a method called STAR (STatistical
Analysis of Residuals) is presented and applied to the
online detection of motion in high angular resolution
diffusion images. Compared to existing techniques, this
method is image based and is built on top of a Kalman
filter. Therefore, it introduces no additional scan time and
does not require additional hardware. The performance of
STAR is tested on simulated and real data and compared to
the classical generalized likelihood ratio test. Successful
detection of small motion is reported (rotation under 2°)
with no delay and robustness to noise.},
Doi = {10.1155/2013/849363},
Key = {fds264748}
}

@article{fds264871,
Author = {Chen, B and Polatkan, G and Sapiro, G and Blei, D and Dunson, D and Carin,
L},
Title = {Deep Learning with Hierarchical Convolutional Factor
Analysis.},
Journal = {Ieee Transactions on Pattern Analysis and Machine
Intelligence},
Year = {2013},
Month = {January},
url = {http://www.ncbi.nlm.nih.gov/pubmed/23319498},
Abstract = {Unsupervised multi-layered ("deep") models are considered
for general data, with a particular focus on imagery. The
model is represented using a hierarchical convolutional
and scores. The computation of layer-dependent model
parameters is implemented within a Bayesian setting,
employing a Gibbs sampler and variational Bayesian (VB)
analysis, that explicitly exploit the convolutional nature
of the expansion. In order to address large-scale and
streaming data, an online version of VB is also developed.
The number of basis functions or dictionary elements at each
layer is inferred from the data, based on a beta-Bernoulli
implementation of the Indian buffet process. Example results
are presented for several image-processing applications,
with comparisons to related models in the
literature.},
Key = {fds264871}
}

@article{fds304064,
Author = {Pokrass, J and Bronstein, AM and Bronstein, MM and Sprechmann, P and Sapiro, G},
Title = {Sparse modeling of intrinsic correspondences},
Journal = {Computer Graphics Forum},
Volume = {32},
Number = {2 PART4},
Pages = {459-468},
Publisher = {WILEY},
Year = {2013},
Month = {January},
url = {http://arxiv.org/abs/1209.6560v1},
Abstract = {We present a novel sparse modeling approach to non-rigid
shape matching using only the ability to detect repeatable
regions. As the input to our algorithm, we are given only
two sets of regions in two shapes; no descriptors are
provided so the correspondence between the regions is not
know, nor we know how many regions correspond in the two
shapes. We show that even with such scarce information, it
is possible to establish very accurate correspondence
between the shapes by using methods from the field of sparse
modeling, being this, the first non-trivial use of sparse
models in shape correspondence. We formulate the problem of
permuted sparse coding, in which we solve simultaneously for
an unknown permutation ordering the regions on two shapes
and for an unknown correspondence in functional
representation. We also propose a robust variant capable of
handling incomplete matches. Numerically, the problem is
solved efficiently by alternating the solution of a linear
assignment and a sparse coding problem. The proposed methods
are evaluated qualitatively and quantitatively on standard
benchmarks containing both synthetic and scanned objects. ©
2013 The Eurographics Association and Blackwell Publishing
Ltd.},
Doi = {10.1111/cgf.12066},
Key = {fds304064}
}

@article{fds264704,
Author = {Tang, Z and Tepper, M and Sapiro, G},
Title = {Reflective Symmetry Detection by Rectifying Randomized
Correspondences},
Journal = {Procedings of the British Machine Vision Conference
2013},
Publisher = {British Machine Vision Association},
Year = {2013},
Doi = {10.5244/c.27.115},
Key = {fds264704}
}

@article{fds264733,
Author = {Sotiropoulos, SN and Jbabdi, S and Xu, J and Andersson, JL and Moeller,
S and Auerbach, EJ and Glasser, MF and Hernandez, M and Sapiro, G and Jenkinson, M and Feinberg, DA and Yacoub, E and Lenglet, C and Essen,
DCV and Ugurbil, K and Behrens, TEJ},
Title = {Advances in diffusion MRI acquisition and processing in the
Human Connectome Project},
Journal = {Neuroimage},
Volume = {80},
Pages = {125-143},
Year = {2013},
ISSN = {1053-8119},
url = {http://dx.doi.org/10.1016/j.neuroimage.2013.05.057},
Abstract = {The Human Connectome Project (HCP) is a collaborative 5-year
effort to map human brain connections and their variability
in healthy adults. A consortium of HCP investigators will
study a population of 1200 healthy adults using multiple
imaging modalities, along with extensive behavioral and
genetic data. In this overview, we focus on diffusion MRI
(dMRI) and the structural connectivity aspect of the
project. We present recent advances in acquisition and
processing that allow us to obtain very high-quality in-vivo
MRI data, whilst enabling scanning of a very large number of
subjects. These advances result from 2. years of intensive
efforts in optimising many aspects of data acquisition and
processing during the piloting phase of the project. The
data quality and methods described here are representative
of the datasets and processing pipelines that will be made
freely available to the community at quarterly intervals,
beginning in 2013. © 2013 Elsevier Inc.},
Doi = {10.1016/j.neuroimage.2013.05.057},
Key = {fds264733}
}

@article{fds264740,
Author = {Su, S and White, T and Schmidt, M and Kao, CY and Sapiro,
G},
Title = {Geometric computation of human gyrification indexes from
magnetic resonance images},
Journal = {Human Brain Mapping},
Volume = {34},
Number = {5},
Pages = {1230-1244},
Year = {2013},
ISSN = {1065-9471},
url = {http://dx.doi.org/10.1002/hbm.21510},
Abstract = {Human brains are highly convoluted surfaces with multiple
folds. To characterize the complexity of these folds and
their relationship with neurological and psychiatric
conditions, different techniques have been developed to
quantify the folding patterns, also known as the surface
complexity or gyrification of the brain. In this study, the
authors propose a new geometric approach to measure the
gyrification of human brains from magnetic resonance images.
This approach is based on intrinsic 3D measurements that
relate the local brain surface area to the corresponding
area of a tightly wrapped sheet. The authors also present an
adaptation of this technique in which the geodesic depth is
incorporated into the gyrification computation. These
gyrification measures are efficiently and accurately
computed by solving geometric partial differential
equations. The presentation of the geometric framework is
complemented with experimental results for brain complexity
in typically developing children and adolescents. Using this
novel approach, the authors provide evidence for a gradual
decrease in brain surface complexity throughout childhood
and adolescence. These developmental differences occur
earlier in the occipital lobe and move anterior as children
Inc.},
Doi = {10.1002/hbm.21510},
Key = {fds264740}
}

@article{fds264743,
Author = {Kuybeda, O and Frank, GA and Bartesaghi, A and Borgnia, M and Subramaniam, S and Sapiro, G},
Title = {A collaborative framework for 3D alignment and
classification of heterogeneous subvolumes in cryo-electron
tomography},
Journal = {Journal of Structural Biology},
Volume = {181},
Number = {2},
Pages = {116-127},
Year = {2013},
ISSN = {1047-8477},
url = {http://dx.doi.org/10.1016/j.jsb.2012.10.010},
Abstract = {The limitation of using low electron doses in
non-destructive cryo-electron tomography of biological
specimens can be partially offset via averaging of aligned
and structurally homogeneous subsets present in tomograms.
This type of sub-volume averaging is especially challenging
when multiple species are present. Here, we tackle the
problem of conformational separation and alignment with a "
collaborative" approach designed to reduce the effect of the
" curse of dimensionality" encountered in standard pair-wise
comparisons. Our new approach is based on using the nuclear
norm as a collaborative similarity measure for alignment of
sub-volumes, and by exploiting the presence of symmetry
early in the processing. We provide a strict validation of
this method by analyzing mixtures of intact simian
immunodeficiency viruses SIV mac239 and SIV CP-MAC. Electron
microscopic images of these two virus preparations are
indistinguishable except for subtle differences in
conformation of the envelope glycoproteins displayed on the
surface of each virus particle. By using the nuclear
norm-based, collaborative alignment method presented here,
we demonstrate that the genetic identity of each virus
particle present in the mixture can be assigned based solely
on the structural information derived from single envelope
glycoproteins displayed on the virus surface. ©
2012.},
Doi = {10.1016/j.jsb.2012.10.010},
Key = {fds264743}
}

@article{fds264827,
Author = {Duarte-Carvajalino, JM and Yu, G and Carin, L and Sapiro,
G},
Gaussian Mixture Models.},
Journal = {Ieee Trans. Signal Processing},
Volume = {61},
Pages = {585-600},
Year = {2013},
url = {http://arxiv.org/abs/1201.5404v1},
compressive sensing is developed, where a statistical model
replaces the standard sparsity model of classical
compressive sensing. We propose within this framework
optimal task-specific sensing protocols specifically and
jointly designed for classification and reconstruction. A
online sensing is applied to detect the signal class in the
first step, followed by a reconstruction step adapted to the
detected class and the observed samples. The approach is
based on information theory, here tailored for Gaussian
mixture models (GMMs), where an information-theoretic
objective relationship between the sensed signals and a
representation of the specific task of interest is
maximized. Experimental results using synthetic signals,
Landsat satellite attributes, and natural images of
different sizes and with different noise levels show the
improvements achieved using the proposed framework when
compared to more standard sensing protocols. The underlying
formulation can be applied beyond GMMs, at the price of
higher mathematical and computational complexity.},
Doi = {10.1109/TSP.2012.2225054},
Key = {fds264827}
}

@article{fds304058,
Author = {Qiu, Q and Sapiro, G and Chen, C-H},
Title = {Domain-invariant Face Recognition using Learned Low-rank
Transformation.},
Journal = {Corr},
Volume = {abs/1308.0275},
Year = {2013},
url = {http://arxiv.org/abs/1308.0275v1},
Abstract = {We present a low-rank transformation approach to compensate
for face variations due to changes in visual domains, such
as pose and illumination. The key idea is to learn
discriminative linear transformations for face images using
matrix rank as the optimization criteria. The learned linear
transformations restore a shared low-rank structure for
faces from the same subject, and, at the same time, force a
high-rank structure for faces from different subjects. In
this way, among the transformed faces, we reduce variations
caused by domain changes within the classes, and increase
separations between the classes for better face recognition
across domains. Extensive experiments using public datasets
are presented to demonstrate the effectiveness of our
approach for face recognition across domains. The potential
of the approach for feature extraction in generic object
recognition and coded aperture design are discussed as
well.},
Key = {fds304058}
}

@article{fds304059,
Author = {Qiu, Q and Sapiro, G},
Title = {Learning Robust Subspace Clustering.},
Journal = {Corr},
Volume = {abs/1308.0273},
Year = {2013},
url = {http://arxiv.org/abs/1308.0273v1},
Abstract = {We propose a low-rank transformation-learning framework to
robustify subspace clustering. Many high-dimensional data,
such as face images and motion sequences, lie in a union of
low-dimensional subspaces. The subspace clustering problem
has been extensively studied in the literature to partition
such high-dimensional data into clusters corresponding to
their underlying low-dimensional subspaces. However,
low-dimensional intrinsic structures are often violated for
real-world observations, as they can be corrupted by errors
or deviate from ideal models. We propose to address this by
learning a linear transformation on subspaces using matrix
rank, via its convex surrogate nuclear norm, as the
optimization criteria. The learned linear transformation
restores a low-rank structure for data from the same
subspace, and, at the same time, forces a high-rank
structure for data from different subspaces. In this way, we
reduce variations within the subspaces, and increase
separations between the subspaces for more accurate subspace
clustering. This proposed learned robust subspace clustering
framework significantly enhances the performance of existing
subspace clustering methods. To exploit the low-rank
structures of the transformed subspaces, we further
introduce a subspace clustering technique, called Robust
Sparse Subspace Clustering, which efficiently combines
robust PCA with sparse modeling. We also discuss the online
learning of the transformation, and learning of the
transformation while simultaneously reducing the data
dimensionality. Extensive experiments using public datasets
are presented, showing that the proposed approach
significantly outperforms state-of-the-art subspace
clustering methods.},
Key = {fds304059}
}

@article{fds265112,
Author = {Asiaee T. and A and Tepper, M and Banerjee, A and Sapiro,
G},
Title = {If you are happy and you know it... tweet},
Journal = {Acm International Conference Proceeding Series},
Pages = {1602-1606},
Publisher = {ACM Press},
Year = {2012},
Month = {December},
url = {http://dx.doi.org/10.1145/2396761.2398481},
Abstract = {Extracting sentiment from Twitter data is one of the
fundamental problems in social media analytics. Twitter's
length constraint renders determining the positive/negative
sentiment of a tweet difficult, even for a human judge. In
this work we present a general framework for per-tweet (in
contrast with batches of tweets) sentiment analysis which
consists of: (1) extracting tweets about a desired target
subject, (2) separating tweets with sentiment, and (3)
setting apart positive from negative tweets. For each step,
we study the performance of a number of classical and new
machine learning algorithms. We also show that the intrinsic
sparsity of tweets allows performing classification in a low
dimensional space, via random projections, without losing
accuracy. In addition, we present weighted variants of all
employed algorithms, exploiting the available labeling
uncertainty, which further improve classification accuracy.
Finally, we show that spatially aggregating our per-tweet
classification results produces a very satisfactory outcome,
making our approach a good candidate for batch tweet
Doi = {10.1145/2396761.2398481},
Key = {fds265112}
}

@article{fds264734,
Author = {Elhamifar, E and Sapiro, G and Vidal, R},
Title = {Finding exemplars from pairwise dissimilarities via
simultaneous sparse recovery},
Journal = {Advances in Neural Information Processing
Systems},
Volume = {1},
Pages = {19-27},
Year = {2012},
Month = {December},
ISSN = {1049-5258},
Abstract = {Given pairwise dissimilarities between data points, we
consider the problem of finding a subset of data points,
called representatives or exemplars, that can efficiently
describe the data collection. We formulate the problem as a
row-sparsity regularized trace minimization problem that can
be solved efficiently using convex programming. The solution
of the proposed optimization program finds the
representatives and the probability that each data point is
associated with each one of the representatives. We obtain
the range of the regularization parameter for which the
solution of the proposed optimization program changes from
selecting one representative for all data points to
selecting all data points as representatives. When data
points are distributed around multiple clusters according to
the dissimilarities, we show that the data points in each
cluster select representatives only from that cluster.
Unlike metric-based methods, our algorithm can be applied to
dissimilarities that are asymmetric or violate the triangle
inequality, i.e., it does not require that the pairwise
dissimilarities come from a metric. We demonstrate the
effectiveness of the proposed algorithm on synthetic data as
well as real-world image and text data.},
Key = {fds264734}
}

@article{fds264735,
Author = {Fiori, M and Musé, P and Sapiro, G},
Title = {Topology constraints in graphical models},
Journal = {Advances in Neural Information Processing
Systems},
Volume = {1},
Pages = {791-799},
Year = {2012},
Month = {December},
ISSN = {1049-5258},
Abstract = {Graphical models are a very useful tool to describe and
understand natural phenomena, from gene expression to
climate change and social interactions. The topological
structure of these graphs/networks is a fundamental part of
the analysis, and in many cases the main goal of the study.
However, little work has been done on incorporating prior
topological knowledge onto the estimation of the underlying
graphical models from sample data. In this work we propose
extensions to the basic joint regression model for network
estimation, which explicitly incorporate graph-topological
constraints into the corresponding optimization approach.
The first proposed extension includes an eigenvector
centrality constraint, thereby promoting this important
prior topological property. The second developed extension
promotes the formation of certain motifs, triangle-shaped
ones in particular, which are known to exist for example in
genetic regulatory networks. The presentation of the
underlying formulations, which serve as examples of the
introduction of topological constraints in network
estimation, is complemented with examples in diverse
datasets demonstrating the importance of incorporating such
critical prior knowledge.},
Key = {fds264735}
}

@article{fds264742,
Author = {Tepper, M and Sapiro, G},
Title = {Decoupled coarse-to-fine matching and nonlinear
regularization for efficient motion estimation},
Journal = {Proceedings International Conference on Image Processing,
Icip},
Pages = {1517-1520},
Publisher = {IEEE},
Year = {2012},
Month = {December},
url = {http://dx.doi.org/10.1109/ICIP.2012.6467160},
Abstract = {A simple motion estimation algorithm, light-weighted both in
memory and in time, is presented in this paper. This
simplicity is achieved by decoupling the matching and the
regularization stages in the estimation process. Experiments
show that the obtained results are comparable with
state-of-the-art algorithms that are much more
Doi = {10.1109/ICIP.2012.6467160},
Key = {fds264742}
}

@article{fds264830,
Author = {Fasching, J and Walczak, N and Sivalingam, R and Cullen, K and Murphy,
B and Sapiro, G and Morellas, V and Papanikolopoulos,
N},
Title = {Detecting risk-markers in children in a preschool
classroom},
Journal = {Ieee International Conference on Intelligent Robots and
Systems},
Pages = {1010-1016},
Publisher = {IEEE},
Year = {2012},
Month = {December},
ISBN = {9781467317375},
url = {http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6363628},
Abstract = {Early intervention in mental disorders can dramatically
increase an individual's quality of life. Additionally, when
symptoms of mental illness appear in childhood or
adolescence, they represent the later stages of a process
that began years earlier. One goal of psychiatric research
is to identify risk-markers: genetic, neural, behavioral
and/or social deviations that indicate elevated risk of a
particular mental disorder. Ideally, screening of
risk-markers should occur in a community setting, and not a
clinical setting which may be time-consuming and
resource-intensive. Given this situation, a system for
automatically detecting risk-markers in children would be
highly valuable. In this paper, we describe such a system
that has been installed at the Shirley G. Moore Lab School,
a research pre-school at the University of Minnesota. This
system consists of multiple RGB+D sensors and is able to
detect children and adults in the classroom, tracking them
as they move around the room. We use the tracking results to
extract high-level information about the behavior and social
interaction of children, that can then be used to screen for
early signs of mental disorders. © 2012
IEEE.},
Doi = {10.1109/IROS.2012.6385732},
Key = {fds264830}
}

@article{fds264832,
Author = {Hashemi, J and Spina, TV and Tepper, M and Esler, A and Morellas, V and Papanikolopoulos, N and Sapiro, G},
Title = {A computer vision approach for the assessment of
autism-related behavioral markers},
Journal = {2012 Ieee International Conference on Development and
Learning and Epigenetic Robotics, Icdl 2012},
Pages = {1-7},
Publisher = {IEEE},
Year = {2012},
Month = {December},
ISBN = {9781467349635},
url = {http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6384412},
Abstract = {The early detection of developmental disorders is key to
child outcome, allowing interventions to be initiated that
promote development and improve prognosis. Research on
autism spectrum disorder (ASD) suggests behavioral markers
can be observed late in the first year of life. Many of
these studies involved extensive frame-by-frame video
observation and analysis of a child's natural behavior.
Although non-intrusive, these methods are extremely
time-intensive and require a high level of observer
training; thus, they are impractical for clinical purposes.
Diagnostic measures for ASD are available for infants but
are only accurate when used by specialists experienced in
early diagnosis. This work is a first milestone in a
long-term multidisciplinary project that aims at helping
clinicians and general practitioners accomplish this early
detection/measurement task automatically. We focus on
providing computer vision tools to measure and identify ASD
behavioral markers based on components of the Autism
Observation Scale for Infants (AOSI). In particular, we
develop algorithms to measure three critical AOSI activities
that assess visual attention. We augment these AOSI
activities with an additional test that analyzes
asymmetrical patterns in unsupported gait. The first set of
algorithms involves assessing head motion by facial feature
tracking, while the gait analysis relies on joint foreground
segmentation and 2D body pose estimation in video. We show
results that provide insightful knowledge to augment the
clinician's behavioral observations obtained from real
Doi = {10.1109/DevLrn.2012.6400865},
Key = {fds264832}
}

@article{fds264836,
Author = {Sprechmann, P and Bronstein, A and Sapiro, G},
Title = {Real-time online singing voice separation from monaural
recordings using robust low-rank modeling},
Journal = {Proceedings of the 13th International Society for Music
Information Retrieval Conference, Ismir 2012},
Pages = {67-72},
Publisher = {FEUP Edições},
Editor = {Gouyon, F and Herrera, P and Martins, LG and Müller,
M},
Year = {2012},
Month = {December},
ISBN = {9789727521449},
url = {http://www.informatik.uni-trier.de/~ley/db/conf/ismir/ismir2012.html},
Abstract = {Separating the leading vocals from the musical accompaniment
is a challenging task that appears naturally in several
music processing applications. Robust principal component
analysis (RPCA) has been recently employed to this problem
producing very successful results. The method decomposes the
signal into a low-rank component corresponding to the
accompaniment with its repetitive structure, and a sparse
component corresponding to the voice with its quasi-harmonic
structure. In this paper we first introduce a non-negative
variant of RPCA, termed as robust low-rank non-negative
matrix factorization (RNMF). This new framework better suits
audio applications. We then propose two efficient
feed-forward architectures that approximate the RPCA and
RNMF with low latency and a fraction of the complexity of
the original optimization method. These approximants allow
incorporating elements of unsupervised, semi- and
fully-supervised learning into the RPCA and RNMF frameworks.
Our basic implementation shows several orders of magnitude
speedup compared to the exact solvers with no performance
degradation, and allows online and faster-than-real-time
processing. Evaluation on the MIR-1K dataset demonstrates
state-of-the-art performance. © 2012 International Society
for Music Information Retrieval.},
Key = {fds264836}
}

@article{fds264841,
Author = {Castrodad, A and Khuon, T and Rand, R and Sapiro,
G},
Title = {Sparse modeling for hyperspectral imagery with LiDAR data
fusion for subpixel mapping},
Journal = {International Geoscience and Remote Sensing Symposium
Pages = {7275-7278},
Publisher = {IEEE},
Year = {2012},
Month = {December},
ISBN = {9781467311601},
url = {http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6334512},
Abstract = {Several studies suggest that the use of geometric features
along with spectral information improves the classification
and visualization quality of hyperspectral imagery. These
studies normally make use of spatial neighborhoods of
hyperspectral pixels for extracting these geometric
features. In this work, we merge point cloud Light Detection
and Ranging (LiDAR) data and hyperspectral imagery (HSI)
into a single sparse modeling pipeline for subpixel mapping
and classification. The model accounts for material
variability and noise by using learned dictionaries that act
as spectral endmembers. Additionally, the estimated
abundances are influenced by the LiDAR point cloud density,
particularly helpful in spectral mixtures involving partial
occlusions and illumination changes caused by elevation
differences. We demonstrate the advantages of the proposed
algorithm with co-registered LiDAR-HSI data. © 2012
IEEE.},
Key = {fds264841}
}

@article{fds264842,
Author = {Zhou, T and Shan, H and Banerjee, A and Sapiro, G},
Title = {Kernelized probabilistic matrix factorization: Exploiting
graphs and side information},
Journal = {Proceedings of the 12th Siam International Conference on
Data Mining, Sdm 2012},
Pages = {403-414},
Publisher = {SIAM / Omnipress},
Year = {2012},
Month = {December},
ISBN = {9781611972320},
url = {http://dx.doi.org/10.1137/1.9781611972825},
Abstract = {We propose a new matrix completion algorithm| Kernelized
Probabilistic Matrix Factorization (KPMF), which effectively
incorporates external side information into the matrix
factorization process. Unlike Probabilistic Matrix
Factorization (PMF) [14], which assumes an independent
latent vector for each row (and each column) with Gaussian
priors, KMPF works with latent vectors spanning all rows
(and columns) with Gaussian Process (GP) priors. Hence, KPMF
explicitly captures the underlying (nonlinear) covariance
structures across rows and columns. This crucial difference
greatly boosts the performance of KPMF when appropriate side
information, e.g., users' social network in recommender
systems, is incorporated. Furthermore, GP priors allow the
KPMF model to fill in a row that is entirely missing in the
original matrix based on the side information alone, which
is not feasible for standard PMF formulation. In our paper,
we mainly work on the matrix completion problem with a graph
among the rows and/or columns as side information, but the
proposed framework can be easily used with other types of
side information as well. Finally, we demonstrate the
efficacy of KPMF through two different applications: 1)
2012 by the Society for Industrial and Applied
Mathematics.},
Doi = {10.1137/1.9781611972825.35},
Key = {fds264842}
}

@article{fds265123,
Author = {Bartesaghi, A and Lecumberry, F and Sapiro, G and Subramaniam,
S},
Title = {Protein secondary structure determination by constrained
single-particle cryo-electron tomography.},
Journal = {Structure (London, England : 1993)},
Volume = {20},
Number = {12},
Pages = {2003-2013},
Year = {2012},
Month = {December},
url = {http://www.ncbi.nlm.nih.gov/pubmed/23217682},
Abstract = {Cryo-electron microscopy (cryo-EM) is a powerful technique
for 3D structure determination of protein complexes by
averaging information from individual molecular images. The
resolutions that can be achieved with single-particle
cryo-EM are frequently limited by inaccuracies in assigning
molecular orientations based solely on 2D projection images.
Tomographic data collection schemes, however, provide
powerful constraints that can be used to more accurately
determine molecular orientations necessary for 3D
reconstruction. Here, we propose "constrained
single-particle tomography" as a general strategy for 3D
structure determination in cryo-EM. A key component of our
approach is the effective use of images recorded in tilt
series to extract high-resolution information and correct
for the contrast transfer function. By incorporating
geometric constraints into the refinement to improve
orientational accuracy of images, we reduce model bias and
overrefinement artifacts and demonstrate that protein
structures can be determined at resolutions of ∼8 Å
starting from low-dose tomographic tilt series.},
Doi = {10.1016/j.str.2012.10.016},
Key = {fds265123}
}

@article{fds265106,
Author = {Ramírez, I and Sapiro, G},
Title = {LOw-rank data modeling via the minimum description length
principle},
Journal = {2015 Ieee International Conference on Acoustics, Speech, and
Signal Processing (Icassp)},
Pages = {2165-2168},
Publisher = {IEEE},
Year = {2012},
Month = {October},
ISSN = {1520-6149},
url = {http://dx.doi.org/10.1109/ICASSP.2012.6288341},
Abstract = {Robust low-rank matrix estimation is a topic of increasing
interest, with promising applications in a variety of
fields, from computer vision to data mining and recommender
systems. Recent theoretical results establish the ability of
such data models to recover the true underlying low-rank
matrix when a large portion of the measured matrix is either
missing or arbitrarily corrupted. However, if low rank is
not a hypothesis about the true nature of the data, but a
device for extracting regularity from it, no current
guidelines exist for choosing the rank of the estimated
matrix. In this work we address this problem by means of the
Minimum Description Length (MDL) principle - a well
established information-theoretic approach to statistical
inference - as a guideline for selecting a model for the
data at hand. We demonstrate the practical usefulness of our
formal approach with results for complex background
extraction in video sequences. © 2012 IEEE.},
Doi = {10.1109/ICASSP.2012.6288341},
Key = {fds265106}
}

@article{fds265107,
Author = {Michaeli, T and Eldar, YC and Sapiro, G},
Title = {Semi-supervised multi-domain regression with distinct
training sets},
Journal = {2015 Ieee International Conference on Acoustics, Speech, and
Signal Processing (Icassp)},
Pages = {2145-2148},
Publisher = {IEEE},
Year = {2012},
Month = {October},
ISSN = {1520-6149},
url = {http://dx.doi.org/10.1109/ICASSP.2012.6288336},
Abstract = {We address the problems of multi-domain and single-domain
regression based on distinct labeled training sets for each
of the domains and a large unlabeled training set from all
domains. We formulate these problems as ones of Bayesian
estimation with partial knowledge of statistical relations.
We propose a worst-case design strategy and study the
resulting estimators. Our analysis explicitly accounts for
the cardinality of the labeled sets and includes the special
cases in which one of the labeled sets is very large or, in
the other extreme, completely missing. We demonstrate our
estimators in the context of audio-visual word recognition
and provide comparisons to several recently proposed
IEEE.},
Doi = {10.1109/ICASSP.2012.6288336},
Key = {fds265107}
}

@article{fds265108,
Author = {Sprechmann, P and Cancela, P and Sapiro, G},
Title = {Gaussian mixture models for score-informed instrument
separation},
Journal = {2015 Ieee International Conference on Acoustics, Speech, and
Signal Processing (Icassp)},
Pages = {49-52},
Publisher = {IEEE},
Year = {2012},
Month = {October},
ISSN = {1520-6149},
url = {http://dx.doi.org/10.1109/ICASSP.2012.6287814},
Abstract = {A new framework for representing quasi-harmonic signals, and
its application to score-informed single channel musical
instruments separation, is introduced in this paper. In the
proposed approach, the signal's pitch and spectral envelope
are modeled separately. The model combines parametric
filters enforcing an harmonic structure in the
representation, with Gaussian modeling for representing the
spectral envelope. The estimation of the signal's model is
cast as an inverse problem efficiently solved via a maximum
a posteriori expectation-maximization algorithm. The
relation of the proposed framework with common non-negative
factorization methods is also discussed. The algorithm is
evaluated with both real and synthetic instruments mixtures,
and comparisons with recently proposed techniques are
Doi = {10.1109/ICASSP.2012.6287814},
Key = {fds265108}
}

@article{fds265135,
Author = {Duarte-Carvajalino, JM and Yu, G and Carin, L and Sapiro,
G},
Title = {Adapted statistical compressive sensing: Learning to sense
gaussian mixture models},
Journal = {2015 Ieee International Conference on Acoustics, Speech, and
Signal Processing (Icassp)},
Pages = {3653-3656},
Publisher = {IEEE},
Year = {2012},
Month = {October},
ISSN = {1520-6149},
url = {http://dx.doi.org/10.1109/ICASSP.2012.6288708},
Abstract = {A framework for learning sensing kernels adapted to signals
that follow a Gaussian mixture model (GMM) is introduced in
this paper. This follows the paradigm of statistical
compressive sensing (SCS), where a statistical model, a GMM
in particular, replaces the standard sparsity model of
classical compressive sensing (CS), leading to both
theoretical and practical improvements. We show that the
optimized sensing matrix outperforms random sampling
matrices originally exploited both in CS and SCS. © 2012
IEEE.},
Doi = {10.1109/ICASSP.2012.6288708},
Key = {fds265135}
}

@article{fds265105,
Author = {Sprechmann, P and Bronstein, A and Sapiro, G},
Title = {Learning efficient structured sparse models},
Journal = {Proceedings of the 29th International Conference on Machine
Learning, Icml 2012},
Volume = {1},
Pages = {615-622},
Year = {2012},
Month = {October},
Abstract = {We present a comprehensive framework for structured sparse
coding and modeling extending the recent ideas of using
learnable fast regressors to approximate exact sparse codes.
For this purpose, we propose an efficient feed forward
architecture derived from the iteration of the
block-coordinate algorithm. This architecture approximates
the exact structured sparse codes with a fraction of the
complexity of the standard optimization methods. We also
show that by using different training objective functions,
the proposed learnable sparse encoders are not only
restricted to be approximants of the exact sparse code for a
pre-given dictionary, but can be rather used as
full-featured sparse encoders or even modelers. A simple
implementation shows several orders of magnitude speedup
compared to the state-of-the-art exact optimization
algorithms at minimal performance degradation, making the
proposed framework suitable for real time and large-scale
applications. Copyright 2012 by the author(s)/owner(s).},
Key = {fds265105}
}

@article{fds265104,
Author = {Elhamifar, E and Sapiro, G and Vidal, R},
Title = {See all by looking at a few: Sparse modeling for finding
representative objects},
Journal = {Proceedings of the Ieee Computer Society Conference on
Computer Vision and Pattern Recognition},
Pages = {1600-1607},
Publisher = {IEEE},
Year = {2012},
Month = {October},
ISSN = {1063-6919},
url = {http://dx.doi.org/10.1109/CVPR.2012.6247852},
Abstract = {We consider the problem of finding a few representatives for
a dataset, i.e., a subset of data points that efficiently
describes the entire dataset. We assume that each data point
can be expressed as a linear combination of the
representatives and formulate the problem of finding the
representatives as a sparse multiple measurement vector
problem. In our formulation, both the dictionary and the
measurements are given by the data matrix, and the unknown
sparse codes select the representatives via convex
optimization. In general, we do not assume that the data are
low-rank or distributed around cluster centers. When the
data do come from a collection of low-rank models, we show
that our method automatically selects a few representatives
from each low-rank model. We also analyze the geometry of
the representatives and discuss their relationship to the
vertices of the convex hull of the data. We show that our
framework can be extended to detect and reject outliers in
datasets, and to efficiently deal with new observations and
large datasets. The proposed framework and theoretical
foundations are illustrated with examples in video
summarization and image classification using
Doi = {10.1109/CVPR.2012.6247852},
Key = {fds265104}
}

@article{fds265121,
Author = {Castrodad, A and Sapiro, G},
Title = {Sparse modeling of human actions from motion
imagery},
Journal = {International Journal of Computer Vision},
Volume = {100},
Number = {1},
Pages = {1-15},
Publisher = {Springer Nature},
Year = {2012},
Month = {October},
ISSN = {0920-5691},
url = {http://dx.doi.org/10.1007/s11263-012-0534-7},
Abstract = {An efficient sparse modeling pipeline for the classification
of human actions from video is here developed.
Spatio-temporal features that characterize local changes in
the image are first extracted. This is followed by the
learning of a class-structured dictionary encoding the
individual actions of interest. Classification is then based
on reconstruction, where the label assigned to each video
comes from the optimal sparse linear combination of the
learned basis vectors (action primitives) representing the
actions. A low computational cost deep-layer model learning
the inter-class correlations of the data is added for
increasing discriminative power. In spite of its simplicity
and low computational cost, the method outperforms
previously reported results for virtually all standard
(outside the USA).},
Doi = {10.1007/s11263-012-0534-7},
Key = {fds265121}
}

@article{fds265103,
Author = {Ramírez, I and Sapiro, G},
Title = {Universal regularizers for robust sparse coding and
modeling.},
Journal = {Ieee Transactions on Image Processing : a Publication of the
Ieee Signal Processing Society},
Volume = {21},
Number = {9},
Pages = {3850-3864},
Year = {2012},
Month = {September},
url = {http://www.ncbi.nlm.nih.gov/pubmed/22562754},
Abstract = {Sparse data models, where data is assumed to be well
represented as a linear combination of a few elements from a
dictionary, have gained considerable attention in recent
years, and their use has led to state-of-the-art results in
many signal and image processing tasks. It is now well
understood that the choice of the sparsity regularization
term is critical in the success of such models. Based on a
codelength minimization interpretation of sparse coding, and
using tools from universal coding theory, we propose a
framework for designing sparsity regularization terms which
have theoretical and practical advantages when compared with
the more standard l(0) or l(1) ones. The presentation of the
framework and theoretical foundations is complemented with
examples that show its practical advantages in image
denoising, zooming and classification.},
Doi = {10.1109/tip.2012.2197006},
Key = {fds265103}
}

@article{fds264753,
Author = {Abosch, A and Harel, N and Sapiro, G and Duchin, Y and Yacoub,
E},
Title = {178 Utility of 7T Imaging for Deep Brain Stimulation
Surgery},
Journal = {Neurosurgery},
Volume = {71},
Number = {2},
Pages = {E569-E570},
Publisher = {Oxford University Press (OUP)},
Year = {2012},
Month = {August},
ISSN = {0148-396X},
Doi = {10.1227/01.neu.0000417768.55934.bf},
Key = {fds264753}
}

@article{fds265100,
Author = {Yatziv, L and Chartouni, M and Datta, S and Sapiro,
G},
Title = {Toward multiple catheters detection in fluoroscopic image
guided interventions.},
Journal = {Ieee Transactions on Information Technology in Biomedicine :
a Publication of the Ieee Engineering in Medicine and
Biology Society},
Volume = {16},
Number = {4},
Pages = {770-781},
Year = {2012},
Month = {July},
url = {http://www.ncbi.nlm.nih.gov/pubmed/22389155},
Abstract = {Catheters are routinely inserted via vessels to cavities of
the heart during fluoroscopic image guided interventions for
electrophysiology (EP) procedures such as ablation. During
such interventions, the catheter undergoes nonrigid
deformation due to physician interaction, patient's
breathing, and cardiac motions. EP clinical applications can
benefit from fast and accurate automatic catheter tracking
in the fluoroscopic images. The typical low quality in
fluoroscopic images and the presence of other medical
instruments in the scene make the automatic detection and
tracking of catheters in clinical environments very
challenging. Toward the development of such an application,
a robust and efficient method for detecting and tracking the
catheter sheath is developed. The proposed approach exploits
the clinical setup knowledge to constrain the search space
while boosting both tracking speed and accuracy, and is
based on a computationally efficient framework to trace the
sheath and simultaneously detect one or multiple catheter
tips. The algorithm is based on a modification of the fast
marching weighted distance computation that efficiently
calculates, on the fly, important geodesic properties in
relevant regions of the image. This is followed by a cascade
classifier for detecting the catheter tips. The proposed
technique is validated on 1107 fluoroscopic images acquired
on multiple patients across four different clinics,
achieving multiple catheter tracking at a rate of 10
images/s with a very low false positive rate of
1.06.},
Doi = {10.1109/titb.2012.2189407},
Key = {fds265100}
}

@article{fds265120,
Author = {Esser, E and Möller, M and Osher, S and Sapiro, G and Xin,
J},
Title = {A convex model for nonnegative matrix factorization and
dimensionality reduction on physical space.},
Journal = {Ieee Transactions on Image Processing : a Publication of the
Ieee Signal Processing Society},
Volume = {21},
Number = {7},
Pages = {3239-3252},
Year = {2012},
Month = {July},
url = {http://www.ncbi.nlm.nih.gov/pubmed/22410332},
Abstract = {A collaborative convex framework for factoring a data matrix
X into a nonnegative product AS , with a sparse coefficient
matrix S, is proposed. We restrict the columns of the
dictionary matrix A to coincide with certain columns of the
data matrix X, thereby guaranteeing a physically meaningful
dictionary and dimensionality reduction. We use l(1, ∞)
regularization to select the dictionary from the data and
show that this leads to an exact convex relaxation of l(0)
in the case of distinct noise-free data. We also show how to
relax the restriction-to- X constraint by initializing an
alternating minimization approach with the solution of the
convex model, obtaining a dictionary close to but not
necessarily in X. We focus on applications of the proposed
framework to hyperspectral endmember and abundance
identification and also show an application to blind source
separation of nuclear magnetic resonance
data.},
Doi = {10.1109/tip.2012.2190081},
Key = {fds265120}
}

@article{fds265119,
Author = {Ramirez, I and Sapiro, G},
Title = {An MDL framework for sparse coding and dictionary
learning},
Journal = {Ieee Transactions on Signal Processing},
Volume = {60},
Number = {6},
Pages = {2913-2927},
Publisher = {Institute of Electrical and Electronics Engineers
(IEEE)},
Year = {2012},
Month = {June},
ISSN = {1053-587X},
url = {http://dx.doi.org/10.1109/TSP.2012.2187203},
Abstract = {The power of sparse signal modeling with learned
overcomplete dictionaries has been demonstrated in a variety
of applications and fields, from signal processing to
statistical inference and machine learning. However, the
statistical properties of these models, such as underfitting
or overfitting given sets of data, are still not well
characterized in the literature. As a result, the success of
sparse modeling depends on hand-tuning critical parameters
for each data and application. This work aims at addressing
this by providing a practical and objective characterization
of sparse models by means of the minimum description length
(MDL) principlea well-established information-theoretic
approach to model selection in statistical inference. The
resulting framework derives a family of efficient sparse
coding and dictionary learning algorithms which, by virtue
of the MDL principle, are completely parameter free.
Furthermore, such framework allows to incorporate additional
prior information to existing models, such as Markovian
dependencies, or to define completely new problem
formulations, including in the matrix analysis area, in a
natural way. These virtues will be demonstrated with
parameter-free algorithms for the classic image denoising
and classification problems, and for low-rank matrix
recovery in video applications. However, the framework is
not limited to this imaging data, and can be applied to a
IEEE.},
Doi = {10.1109/TSP.2012.2187203},
Key = {fds265119}
}

@article{fds264826,
Author = {Sapiro, G},
Title = {ON THE STATE Classes at College d eFrance,
1989-1992},
Journal = {Quinzaine Litteraire},
Number = {1061},
Pages = {4-4},
Year = {2012},
Month = {May},
ISSN = {0048-6493},
Key = {fds264826}
}

@article{fds265099,
Author = {Walczak, N and Fasching, J and Toczyski, W and Sivalingam, R and Bird,
N and Cullen, K and Morellas, V and Murphy, B and Sapiro, G and Papanikolopoulos, N},
Title = {A nonintrusive system for behavioral analysis of children
using multiple RGB+depth sensors},
Journal = {Proceedings of Ieee Workshop on Applications of Computer
Vision},
Pages = {217-222},
Publisher = {IEEE},
Year = {2012},
Month = {May},
ISSN = {2158-3978},
url = {http://dx.doi.org/10.1109/WACV.2012.6163011},
Abstract = {In developmental disorders such as autism and schizophrenia,
observing behavioral precursors in very early childhood can
allow for early intervention and can improve patient
outcomes. While such precursors open the possibility of
broad and large-scale screening, until now they have been
identified only through experts' painstaking examinations
and their manual annotations of limited, unprocessed video
footage. Here we introduce a system to automate and assist
in such procedures. Employing multiple inexpensive real-time
rgb+depth (rgb+d) sensors recording from multiple
viewpoints, our non-invasive systemnow installed at the
Shirley G. Moore Lab School, a research preschool-is being
developed to monitor and reconstruct the play and
interactions of preschoolers. The system's role is to help
in assessing the growing volumes of its on-site recordings
and to provide the data needed to uncover additional
neuromotor behavioral markers via techniques such as data
Doi = {10.1109/WACV.2012.6163011},
Key = {fds265099}
}

@article{fds264844,
Author = {Guoshen Yu, and Sapiro, G and Mallat, S},
Title = {Solving Inverse Problems With Piecewise Linear Estimators:
From Gaussian Mixture Models to Structured
Sparsity},
Journal = {Ieee Transactions on Image Processing : a Publication of the
Ieee Signal Processing Society},
Volume = {21},
Number = {5},
Pages = {2481-2499},
Publisher = {Institute of Electrical and Electronics Engineers
(IEEE)},
Year = {2012},
Month = {May},
url = {http://dx.doi.org/10.1109/tip.2011.2176743},
Doi = {10.1109/tip.2011.2176743},
Key = {fds264844}
}

@article{fds265115,
Author = {Yu, G and Sapiro, G and Mallat, S},
Title = {Solving inverse problems with piecewise linear estimators:
from Gaussian mixture models to structured
sparsity.},
Journal = {Ieee Transactions on Image Processing : a Publication of the
Ieee Signal Processing Society},
Volume = {21},
Number = {5},
Pages = {2481-2499},
Year = {2012},
Month = {May},
url = {http://www.ncbi.nlm.nih.gov/pubmed/22180506},
Abstract = {A general framework for solving image inverse problems with
piecewise linear estimations is introduced in this paper.
The approach is based on Gaussian mixture models, which are
estimated via a maximum a posteriori expectation-maximization
algorithm. A dual mathematical interpretation of the
proposed framework with a structured sparse estimation is
described, which shows that the resulting piecewise linear
estimate stabilizes the estimation when compared with
traditional sparse inverse problem techniques. We
demonstrate that, in a number of image inverse problems,
including interpolation, zooming, and deblurring of narrow
kernels, the same simple and computationally efficient
algorithm yields results in the same ballpark as that of the
state of the art.},
Doi = {10.1109/tip.2011.2176743},
Key = {fds265115}
}

@article{fds265116,
Author = {Mahmoudi, M and Sapiro, G},
Title = {Sparse representations for range data restoration.},
Journal = {Ieee Transactions on Image Processing : a Publication of the
Ieee Signal Processing Society},
Volume = {21},
Number = {5},
Pages = {2909-2915},
Year = {2012},
Month = {May},
url = {http://www.ncbi.nlm.nih.gov/pubmed/22287242},
Abstract = {In this paper, the problem of denoising and occlusion
restoration of 3-D range data based on dictionary learning
and sparse representation methods is explored. We apply
these techniques after converting the noisy 3-D surface into
one or more images. We present experimental results on the
proposed approaches.},
Doi = {10.1109/tip.2012.2185940},
Key = {fds265116}
}

@article{fds265117,
Author = {Frank, GA and Bartesaghi, A and Kuybeda, O and Borgnia, MJ and White,
TA and Sapiro, G and Subramaniam, S},
Title = {Computational separation of conformational heterogeneity
using cryo-electron tomography and 3D sub-volume
averaging.},
Journal = {Journal of Structural Biology},
Volume = {178},
Number = {2},
Pages = {165-176},
Year = {2012},
Month = {May},
url = {http://www.ncbi.nlm.nih.gov/pubmed/22248450},
Abstract = {We have previously used cryo-electron tomography combined
with sub-volume averaging and classification to obtain 3D
structures of macromolecular assemblies in cases where a
single dominant species was present, and applied these
methods to the analysis of a variety of trimeric HIV-1 and
SIV envelope glycoproteins (Env). Here, we extend these
studies by demonstrating automated, iterative, missing
wedge-corrected 3D image alignment and classification
methods to distinguish multiple conformations that are
present simultaneously. We present a method for measuring
the spatial distribution of the vector elements representing
distinct conformational states of Env. We identify data
processing strategies that allow clear separation of the
previously characterized closed and open conformations, as
well as unliganded and antibody-liganded states of Env when
they are present in mixtures. We show that identifying and
removing spikes with the lowest signal-to-noise ratios
improves the overall accuracy of alignment between
individual Env sub-volumes, and that alignment accuracy, in
turn, determines the success of image classification in
assessing conformational heterogeneity in heterogeneous
mixtures. We validate these procedures for computational
separation by successfully separating and reconstructing
distinct 3D structures for unliganded and antibody-liganded
as well as open and closed conformations of Env present
simultaneously in mixtures.},
Doi = {10.1016/j.jsb.2012.01.004},
Key = {fds265117}
}

@article{fds265114,
Author = {Aganj, I and Lenglet, C and Yacoub, E and Sapiro, G and Harel,
N},
Title = {A 3D wavelet fusion approach for the reconstruction of
isotropic-resolution MR images from orthogonal
anisotropic-resolution scans.},
Journal = {Magnetic Resonance in Medicine},
Volume = {67},
Number = {4},
Pages = {1167-1172},
Year = {2012},
Month = {April},
url = {http://www.ncbi.nlm.nih.gov/pubmed/21761448},
Abstract = {Hardware constraints, scanning time limitations, patient
movement, and signal-to-noise ratio (SNR) considerations,
restrict the slice-selection and the in-plane resolutions of
MRI differently, generally resulting in anisotropic voxels.
This nonuniform sampling can be problematic, especially in
image segmentation and clinical examination. To alleviate
this, the acquisition is divided into (two or) three
separate scans, with higher in-plane resolutions and thick
slices, yet orthogonal slice-selection directions. In this
work, a noniterative wavelet-based approach for combining
compared with other existing methods, such as Fourier
techniques, are discussed, including the consideration of
the actual pulse response of the MRI scanner, and its lower
computational complexity. Experimental results are shown on
simulated and real 7 T MRI data.},
Doi = {10.1002/mrm.23086},
Key = {fds265114}
}

@article{fds264755,
Author = {Sapiro, G},
Title = {Autonomy Revisited: The Question of Mediations and its
Methodological Implications},
Journal = {Paragraph},
Volume = {35},
Number = {1},
Pages = {30-48},
Publisher = {Edinburgh University Press},
Year = {2012},
Month = {March},
ISSN = {0264-8334},
Doi = {10.3366/para.2012.0040},
Key = {fds264755}
}

@article{fds265134,
Author = {Xing, Z and Zhou, M and Castrodad, A and Sapiro, G and Carin,
L},
Title = {Dictionary learning for noisy and incomplete hyperspectral
images},
Journal = {Siam Journal on Imaging Sciences},
Volume = {5},
Number = {1},
Pages = {33-56},
Publisher = {Society for Industrial & Applied Mathematics
(SIAM)},
Year = {2012},
Month = {February},
ISSN = {1936-4954},
url = {http://dx.doi.org/10.1137/110837486},
Abstract = {We consider analysis of noisy and incomplete hyperspectral
imagery, with the objective of removing the noise and
inferring the missing data. The noise statistics may be
wavelength dependent, and the fraction of data missing (at
random) may be substantial, including potentially entire
bands, offering the potential to significantly reduce the
quantity of data that need be measured. To achieve this
objective, the imagery is divided into contiguous
three-dimensional (3D) spatio-spectral blocks of spatial
dimension much less than the image dimension. It is assumed
that each such 3D block may be represented as a linear
combination of dictionary elements of the same dimension,
plus noise, and the dictionary elements are learned in situ
based on the observed data (no a priori training). The
number of dictionary elements needed for representation of
any particular block is typically small relative to the
block dimensions, and all the image blocks are processed
jointly ("collaboratively") to infer the underlying
dictionary. We address dictionary learning from a Bayesian
perspective, considering two distinct means of imposing
sparse dictionary usage. These models allow inference of the
number of dictionary elements needed as well as the
underlying wavelength-dependent noise statistics. It is
demonstrated that drawing the dictionary elements from a
Gaussian process prior, imposing structure on the wavelength
dependence of the dictionary elements, yields significant
advantages, relative to the more conventional approach of
using an independent and identically distributed Gaussian
prior for the dictionary elements; this advantage is
particularly evident in the presence of noise. The framework
is demonstrated by processing hyperspectral imagery with a
significant number of voxels missing uniformly at random,
with imagery at specific wavelengths missing entirely, and
Society for Industrial and Applied Mathematics.},
Doi = {10.1137/110837486},
Key = {fds265134}
}

@article{fds265113,
Author = {Duarte-Carvajalino, JM and Jahanshad, N and Lenglet, C and McMahon,
KL and de Zubicaray, GI and Martin, NG and Wright, MJ and Thompson, PM and Sapiro, G},
Title = {Hierarchical topological network analysis of anatomical
human brain connectivity and differences related to sex and
kinship.},
Journal = {Neuroimage},
Volume = {59},
Number = {4},
Pages = {3784-3804},
Year = {2012},
Month = {February},
url = {http://www.ncbi.nlm.nih.gov/pubmed/22108644},
Abstract = {Modern non-invasive brain imaging technologies, such as
diffusion weighted magnetic resonance imaging (DWI), enable
the mapping of neural fiber tracts in the white matter,
providing a basis to reconstruct a detailed map of brain
structural connectivity networks. Brain connectivity
networks differ from random networks in their topology,
which can be measured using small worldness, modularity, and
high-degree nodes (hubs). Still, little is known about how
individual differences in structural brain network
properties relate to age, sex, or genetic differences.
Recently, some groups have reported brain network biomarkers
that enable differentiation among individuals, pairs of
individuals, and groups of individuals. In addition to
studying new topological features, here we provide a
unifying general method to investigate topological brain
networks and connectivity differences between individuals,
pairs of individuals, and groups of individuals at several
levels of the data hierarchy, while appropriately
controlling false discovery rate (FDR) errors. We apply our
new method to a large dataset of high quality brain
connectivity networks obtained from High Angular Resolution
Diffusion Imaging (HARDI) tractography in 303 young adult
twins, siblings, and unrelated people. Our proposed approach
can accurately classify brain connectivity networks based on
sex (93% accuracy) and kinship (88.5% accuracy). We find
statistically significant differences associated with sex
and kinship both in the brain connectivity networks and in
derived topological metrics, such as the clustering
coefficient and the communicability matrix.},
Doi = {10.1016/j.neuroimage.2011.10.096},
Key = {fds265113}
}

@article{fds265111,
Author = {Lenglet, C and Abosch, A and Yacoub, E and De Martino and F and Sapiro, G and Harel, N},
Title = {Comprehensive in vivo mapping of the human basal ganglia and
thalamic connectome in individuals using 7T
MRI.},
Journal = {Plos One},
Volume = {7},
Number = {1},
Pages = {e29153},
Year = {2012},
Month = {January},
url = {http://www.ncbi.nlm.nih.gov/pubmed/22235267},
Abstract = {Basal ganglia circuits are affected in neurological
disorders such as Parkinson's disease (PD), essential
tremor, dystonia and Tourette syndrome. Understanding the
structural and functional connectivity of these circuits is
critical for elucidating the mechanisms of the movement and
neuropsychiatric disorders, and is vital for developing new
therapeutic strategies such as deep brain stimulation (DBS).
Knowledge about the connectivity of the human basal ganglia
and thalamus has rapidly evolved over recent years through
non-invasive imaging techniques, but has remained incomplete
because of insufficient resolution and sensitivity of these
techniques. Here, we present an imaging and computational
protocol designed to generate a comprehensive in vivo and
subject-specific, three-dimensional model of the structure
and connections of the human basal ganglia. High-resolution
structural and functional magnetic resonance images were
acquired with a 7-Tesla magnet. Capitalizing on the enhanced
signal-to-noise ratio (SNR) and enriched contrast obtained
at high-field MRI, detailed structural and connectivity
representations of the human basal ganglia and thalamus were
achieved. This unique combination of multiple imaging
modalities enabled the in-vivo visualization of the
individual human basal ganglia and thalamic nuclei, the
reconstruction of seven white-matter pathways and their
connectivity probability that, to date, have only been
reported in animal studies, histologically, or
group-averaged MRI population studies. Also described are
subject-specific parcellations of the basal ganglia and
thalamus into sub-territories based on their distinct
connectivity patterns. These anatomical connectivity
findings are supported by functional connectivity data
derived from resting-state functional MRI (R-fMRI). This
work demonstrates new capabilities for studying basal
ganglia circuitry, and opens new avenues of investigation
into the movement and neuropsychiatric disorders, in
individual human subjects.},
Doi = {10.1371/journal.pone.0029153},
Key = {fds265111}
}

@article{fds265101,
Author = {Sivalingam, R and Cherian, A and Fasching, J and Walczak, N and Bird, N and Morellas, V and Murphy, B and Cullen, K and Lim, K and Sapiro, G and Papanikolopoulos, N},
Title = {A multi-sensor visual tracking system for behavior
monitoring of at-risk children},
Journal = {Proceedings Ieee International Conference on Robotics and
Automation},
Pages = {1345-1350},
Publisher = {IEEE},
Year = {2012},
Month = {January},
ISSN = {1050-4729},
url = {http://dx.doi.org/10.1109/ICRA.2012.6225280},
Abstract = {Clinical studies confirm that mental illnesses such as
autism, Obsessive Compulsive Disorder (OCD), etc. show
behavioral abnormalities even at very young ages; the early
diagnosis of which can help steer effective treatments. Most
often, the behavior of such at-risk children deviate in very
subtle ways from that of a normal child; correct diagnosis
of which requires prolonged and continuous monitoring of
their activities by a clinician, which is a difficult and
time intensive task. As a result, the development of
automation tools for assisting in such monitoring activities
will be an important step towards effective utilization of
the diagnostic resources. In this paper, we approach the
problem from a computer vision standpoint, and propose a
novel system for the automatic monitoring of the behavior of
children in their natural environment through the deployment
of multiple non-invasive sensors (cameras and depth
sensors). We provide details of our system, together with
algorithms for the robust tracking of the activities of the
children. Our experiments, conducted in the Shirley G. Moore
Laboratory School, demonstrate the effectiveness of our
Doi = {10.1109/ICRA.2012.6225280},
Key = {fds265101}
}

@article{fds265102,
Author = {Tong, M and Kim, Y and Zhan, L and Sapiro, G and Lenglet, C and Mueller,
BA and Thompson, PM and Vese, LA},
Title = {A VARIATIONAL MODEL FOR DENOISING HIGH ANGULAR RESOLUTION
DIFFUSION IMAGING.},
Journal = {Proceedings International Symposium on Biomedical
Imaging},
Pages = {530-533},
Year = {2012},
Month = {January},
ISSN = {1945-7928},
url = {http://www.ncbi.nlm.nih.gov/pubmed/22902985},
Abstract = {The presence of noise in High Angular Resolution Diffusion
Imaging (HARDI) data of the brain can limit the accuracy
with which fiber pathways of the brain can be extracted. In
this work, we present a variational model to denoise HARDI
data corrupted by Rician noise. Numerical experiments are
performed on three types of data: 2D synthetic data, 3D
diffusion-weighted Magnetic Resonance Imaging (DW-MRI) data
of a hardware phantom containing synthetic fibers, and 3D
real HARDI brain data. Experiments show that our model is
effective for denoising HARDI-type data while preserving
important aspects of the fiber pathways such as fractional
anisotropy and the orientation distribution
functions.},
Doi = {10.1109/ISBI.2012.6235602},
Key = {fds265102}
}

@article{fds265110,
Author = {Cetingul, HE and Cetingül, HE and Nadar, M and Thompson, P and Sapiro,
G and Lenglet, C},
Title = {Simultaneous ODF estimation and tractography in
HARDI.},
Journal = {Conference Proceedings : ... Annual International Conference
of the Ieee Engineering in Medicine and Biology Society.
Ieee Engineering in Medicine and Biology Society. Annual
Conference},
Volume = {2012},
Pages = {86-89},
Year = {2012},
Month = {January},
ISSN = {1557-170X},
url = {http://www.ncbi.nlm.nih.gov/pubmed/23365838},
Abstract = {We consider the problem of tracking white matter fibers in
high angular resolution diffusion imaging (HARDI) data while
simultaneously estimating the local fiber orientation
profile. Prior work showed that an unscented Kalman filter
(UKF) can be used for this problem, yet existing algorithms
employ parametric mixture models to represent water
diffusion and to define the state space. To address this
restrictive model dependency, we propose to extend the UKF
to HARDI data modeled by orientation distribution functions
(ODFs), a more generic diffusion model. We consider the
spherical harmonic representation of the HARDI signal as the
state, enforce nonnegativity of the ODFs, and perform
tractography using the directions at which the ODFs attain
their peaks. In simulations, our method outperforms filtered
two-tensor tractography at different levels of noise by
achieving a reduction in mean Chamfer error of 0.05 to 0.27
voxels; it also produced in vivo fiber tracking that is
consistent with the neuroanatomy.},
Doi = {10.1109/embc.2012.6345877},
Key = {fds265110}
}

@article{fds265118,
Author = {Duchin, Y and Abosch, A and Yacoub, E and Sapiro, G and Harel,
N},
Title = {Feasibility of using ultra-high field (7 T) MRI for clinical
surgical targeting.},
Journal = {Plos One},
Volume = {7},
Number = {5},
Pages = {e37328},
Year = {2012},
Month = {January},
url = {http://www.ncbi.nlm.nih.gov/pubmed/22615980},
Abstract = {The advantages of ultra-high magnetic field (7 Tesla) MRI
for basic science research and neuroscience applications
have proven invaluable. Structural and functional MR images
of the human brain acquired at 7 T exhibit rich information
content with potential utility for clinical applications.
However, (1) substantial increases in susceptibility
artifacts, and (2) geometrical distortions at 7 T would be
detrimental for stereotactic surgeries such as deep brain
stimulation (DBS), which typically use 1.5 T images for
surgical planning. Here, we explore whether these issues can
be addressed, making feasible the use of 7 T MRI to guide
surgical planning. Twelve patients with Parkinson's disease,
candidates for DBS, were scanned on a standard clinical 1.5
T MRI and a 7 T MRI scanner. Qualitative and quantitative
assessments of global and regional distortion were evaluated
based on anatomical landmarks and transformation matrix
values. Our analyses show that distances between identical
landmarks on 1.5 T vs. 7 T, in the mid-brain region, were
less than one voxel, indicating a successful co-registration
between the 1.5 T and 7 T images under these specific
imaging parameter sets. On regional analysis, the central
part of the brain showed minimal distortion, while inferior
and frontal areas exhibited larger distortion due to
proximity to air-filled cavities. We conclude that 7 T MR
images of the central brain regions have comparable
distortions to that observed on a 1.5 T MRI, and that
clinical applications targeting structures such as the STN,
are feasible with information-rich 7 T imaging.},
Doi = {10.1371/journal.pone.0037328},
Key = {fds265118}
}

@article{fds265122,
Author = {Tran, EEH and Borgnia, MJ and Kuybeda, O and Schauder, DM and Bartesaghi, A and Frank, GA and Sapiro, G and Milne, JLS and Subramaniam, S},
Title = {Structural mechanism of trimeric HIV-1 envelope glycoprotein
activation.},
Journal = {Plos Pathogens},
Volume = {8},
Number = {7},
Pages = {e1002797},
Year = {2012},
Month = {January},
url = {http://www.ncbi.nlm.nih.gov/pubmed/22807678},
Abstract = {HIV-1 infection begins with the binding of trimeric viral
envelope glycoproteins (Env) to CD4 and a co-receptor on
target T-cells. Understanding how these ligands influence
the structure of Env is of fundamental interest for HIV
vaccine development. Using cryo-electron microscopy, we
describe the contrasting structural outcomes of trimeric Env
binding to soluble CD4, to the broadly neutralizing,
CD4-binding site antibodies VRC01, VRC03 and b12, or to the
monoclonal antibody 17b, a co-receptor mimic. Binding of
trimeric HIV-1 BaL Env to either soluble CD4 or 17b alone,
is sufficient to trigger formation of the open quaternary
conformation of Env. In contrast, VRC01 locks Env in the
closed state, while b12 binding requires a partial opening
in the quaternary structure of trimeric Env. Our results
show that, despite general similarities in regions of the
HIV-1 gp120 polypeptide that contact CD4, VRC01, VRC03 and
b12, there are important differences in quaternary
structures of the complexes these ligands form on native
trimeric Env, and potentially explain differences in the
neutralizing breadth and potency of antibodies with similar
specificities. From cryo-electron microscopic analysis at
∼9 Å resolution of a cleaved, soluble version of trimeric
Env, we show that a structural signature of the open Env
conformation is a three-helix motif composed of α-helical
segments derived from highly conserved, non-glycosylated
N-terminal regions of the gp41 trimer. The three N-terminal
gp41 helices in this novel, activated Env conformation are
held apart by their interactions with the rest of Env, and
are less compactly packed than in the post-fusion, six-helix
bundle state. These findings suggest a new structural
template for designing immunogens that can elicit antibodies
targeting HIV at a vulnerable, pre-entry
stage.},
Doi = {10.1371/journal.ppat.1002797},
Key = {fds265122}
}

@article{fds265133,
Author = {Zhou, M and Chen, H and Paisley, J and Ren, L and Li, L and Xing, Z and Dunson, D and Sapiro, G and Carin, L},
Title = {Nonparametric Bayesian dictionary learning for analysis of
noisy and incomplete images.},
Journal = {Ieee Transactions on Image Processing : a Publication of the
Ieee Signal Processing Society},
Volume = {21},
Number = {1},
Pages = {130-144},
Year = {2012},
Month = {January},
url = {http://www.ncbi.nlm.nih.gov/pubmed/21693421},
Abstract = {Nonparametric Bayesian methods are considered for recovery
of imagery based upon compressive, incomplete, and/or noisy
measurements. A truncated beta-Bernoulli process is employed
to infer an appropriate dictionary for the data under test
and also for image recovery. In the context of compressive
sensing, significant improvements in image recovery are
manifested using learned dictionaries, relative to using
standard orthonormal image expansions. The
compressive-measurement projections are also optimized for
the learned dictionary. Additionally, we consider simpler
(incomplete) measurements, defined by measuring a subset of
image pixels, uniformly selected at random. Spatial
interrelationships within imagery are exploited through use
of the Dirichlet and probit stick-breaking processes.
Several example results are presented, with comparisons to
other methods in the literature.},
Doi = {10.1109/TIP.2011.2160072},
Key = {fds265133}
}

@article{fds264732,
Author = {Cetingul, HE and Cetingül, HE and Nadar, M and Thompson, P and Sapiro,
G and Lenglet, C},
Title = {Simultaneous ODF estimation and tractography in
HARDI.},
Journal = {Conference Proceedings : ... Annual International Conference
of the Ieee Engineering in Medicine and Biology Society.
Ieee Engineering in Medicine and Biology Society. Annual
Conference},
Volume = {2012},
Pages = {86-89},
Year = {2012},
ISSN = {1557-170X},
url = {http://dx.doi.org/10.1109/EMBC.2012.6345877},
Abstract = {We consider the problem of tracking white matter fibers in
high angular resolution diffusion imaging (HARDI) data while
simultaneously estimating the local fiber orientation
profile. Prior work showed that an unscented Kalman filter
(UKF) can be used for this problem, yet existing algorithms
employ parametric mixture models to represent water
diffusion and to define the state space. To address this
restrictive model dependency, we propose to extend the UKF
to HARDI data modeled by orientation distribution functions
(ODFs), a more generic diffusion model. We consider the
spherical harmonic representation of the HARDI signal as the
state, enforce nonnegativity of the ODFs, and perform
tractography using the directions at which the ODFs attain
their peaks. In simulations, our method outperforms filtered
two-tensor tractography at different levels of noise by
achieving a reduction in mean Chamfer error of 0.05 to 0.27
voxels; it also produced in vivo fiber tracking that is
consistent with the neuroanatomy.},
Doi = {10.1109/EMBC.2012.6345877},
Key = {fds264732}
}

@article{fds264739,
Author = {Tepper, M and Sapiro, G},
Title = {Decoupled coarse-to-fine matching and nonlinear
regularization for efficient motion estimation},
Journal = {Proceedings International Conference on Image Processing,
Icip},
Pages = {1517-1520},
Year = {2012},
ISSN = {1522-4880},
url = {http://dx.doi.org/10.1109/ICIP.2012.6467160},
Abstract = {A simple motion estimation algorithm, light-weighted both in
memory and in time, is presented in this paper. This
simplicity is achieved by decoupling the matching and the
regularization stages in the estimation process. Experiments
show that the obtained results are comparable with
state-of-the-art algorithms that are much more
Doi = {10.1109/ICIP.2012.6467160},
Key = {fds264739}
}

@article{fds264840,
Author = {Bronstein, AM and Sprechmann, P and Sapiro, G},
Title = {Learning Efficient Structured Sparse Models},
Journal = {Corr},
Volume = {abs/1206.4649},
Year = {2012},
Key = {fds264840}
}

@article{fds264853,
Author = {Ramírez, I and Sapiro, G},
Title = {An MDL Framework for Sparse Coding and Dictionary
Learning.},
Journal = {Ieee Trans. Signal Processing},
Volume = {60},
Pages = {2913-2927},
Year = {2012},
url = {http://dx.doi.org/10.1109/TSP.2012.2187203},
Doi = {10.1109/TSP.2012.2187203},
Key = {fds264853}
}

@article{fds264855,
Author = {Ramírez, I and Sapiro, G},
Title = {LOw-rank data modeling via the minimum description length
principle.},
Journal = {Icassp},
Pages = {2165-2168},
Publisher = {IEEE},
Year = {2012},
ISBN = {978-1-4673-0046-9},
url = {http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6268628},
Doi = {10.1109/ICASSP.2012.6288341},
Key = {fds264855}
}

@article{fds304063,
Author = {Hashemi, J and Spina, TV and Tepper, M and Esler, A and Morellas, V and Papanikolopoulos, N and Sapiro, G},
Title = {Computer vision tools for the non-invasive assessment of
autism-related behavioral markers},
Journal = {Corr},
Volume = {abs/1210.7014},
Year = {2012},
url = {http://arxiv.org/abs/1210.7014v2},
Abstract = {The early detection of developmental disorders is key to
child outcome, allowing interventions to be initiated that
promote development and improve prognosis. Research on
autism spectrum disorder (ASD) suggests behavioral markers
can be observed late in the first year of life. Many of
these studies involved extensive frame-by-frame video
observation and analysis of a child's natural behavior.
Although non-intrusive, these methods are extremely
time-intensive and require a high level of observer
training; thus, they are impractical for clinical and large
population research purposes. Diagnostic measures for ASD
are available for infants but are only accurate when used by
specialists experienced in early diagnosis. This work is a
first milestone in a long-term multidisciplinary project
that aims at helping clinicians and general practitioners
automatically. We focus on providing computer vision tools
to measure and identify ASD behavioral markers based on
components of the Autism Observation Scale for Infants
(AOSI). In particular, we develop algorithms to measure
three critical AOSI activities that assess visual attention.
We augment these AOSI activities with an additional test
that analyzes asymmetrical patterns in unsupported gait. The
first set of algorithms involves assessing head motion by
tracking facial features, while the gait analysis relies on
joint foreground segmentation and 2D body pose estimation in
video. We show results that provide insightful knowledge to
augment the clinician's behavioral observations obtained
from real in-clinic assessments.},
Key = {fds304063}
}

@article{fds304065,
Author = {Tang, Z and Castrodad, A and Tepper, M and Sapiro,
G},
Title = {Are You Imitating Me? Unsupervised Sparse Modeling for Group
Activity Analysis from a Single Video},
Journal = {Corr},
Volume = {abs/1208.5451},
Year = {2012},
url = {http://arxiv.org/abs/1208.5451v1},
Abstract = {A framework for unsupervised group activity analysis from a
single video is here presented. Our working hypothesis is
that human actions lie on a union of low-dimensional
subspaces, and thus can be efficiently modeled as sparse
linear combinations of atoms from a learned dictionary
representing the action's primitives. Contrary to prior art,
and with the primary goal of spatio-temporal action
grouping, in this work only one single video segment is
available for both unsupervised learning and analysis
without any prior training information. After extracting
simple features at a single spatio-temporal scale, we learn
a dictionary for each individual in the video during each
short time lapse. These dictionaries allow us to compare the
individuals' actions by producing an affinity matrix which
contains sufficient discriminative information about the
actions in the scene leading to grouping with simple and
efficient tools. With diverse publicly available real
videos, we demonstrate the effectiveness of the proposed
framework and its robustness to cluttered backgrounds,
changes of human appearance, and action variability.},
Key = {fds304065}
}

@article{fds304068,
Author = {Sprechmann, P and Bronstein, AM and Sapiro, G},
Title = {Learning Robust Low-Rank Representations},
Journal = {Corr},
Volume = {abs/1209.6393},
Year = {2012},
url = {http://arxiv.org/abs/1209.6393v1},
Abstract = {In this paper we present a comprehensive framework for
learning robust low-rank representations by combining and
extending recent ideas for learning fast sparse coding
regressors with structured non-convex optimization
techniques. This approach connects robust principal
component analysis (RPCA) with dictionary learning
techniques and allows its approximation via trainable
encoders. We propose an efficient feed-forward architecture
derived from an optimization algorithm designed to exactly
solve robust low dimensional projections. This architecture,
in combination with different training objective functions,
allows the regressors to be used as online approximants of
the exact offline RPCA problem or as RPCA-based neural
networks. Simple modifications of these encoders can handle
challenging extensions, such as the inclusion of geometric
data transformations. We present several examples with real
data from image, audio, and video processing. When used to
approximate RPCA, our basic implementation shows several
orders of magnitude speedup compared to the exact solvers
with almost no performance degradation. We show the strength
of the inclusion of learning to the RPCA approach on a music
source separation application, where the encoders outperform
the exact RPCA algorithms, which are already reported to
produce state-of-the-art results on a benchmark database.
Our preliminary implementation on an iPad shows
faster-than-real-time performance with minimal
latency.},
Key = {fds304068}
}

@article{fds304069,
Author = {Michaeli, T and Eldar, YC and Sapiro, G},
Title = {Semi-Supervised Single- and Multi-Domain Regression with
Multi-Domain Training},
Journal = {Corr},
Volume = {abs/1203.4422},
Year = {2012},
url = {http://arxiv.org/abs/1203.4422v1},
Abstract = {We address the problems of multi-domain and single-domain
regression based on distinct and unpaired labeled training
sets for each of the domains and a large unlabeled training
set from all domains. We formulate these problems as a
Bayesian estimation with partial knowledge of statistical
relations. We propose a worst-case design strategy and study
the resulting estimators. Our analysis explicitly accounts
for the cardinality of the labeled sets and includes the
special cases in which one of the labeled sets is very large
or, in the other extreme, completely missing. We demonstrate
our estimators in the context of removing expressions from
facial images and in the context of audio-visual word
recognition, and provide comparisons to several recently
proposed multi-modal learning algorithms.},
Key = {fds304069}
}

@article{fds304070,
Author = {Tepper, M and Sapiro, G},
Title = {L1 Splines for Robust, Simple, and Fast Smoothing of Grid
Data},
Journal = {Corr},
Volume = {abs/1208.2292},
Year = {2012},
url = {http://arxiv.org/abs/1208.2292v2},
Abstract = {Splines are a popular and attractive way of smoothing noisy
data. Computing splines involves minimizing a functional
which is a linear combination of a fitting term and a
regularization term. The former is classically computed
using a (weighted) L2 norm while the latter ensures
smoothness. Thus, when dealing with grid data, the
optimization can be solved very efficiently using the DCT.
In this work we propose to replace the L2 norm in the
fitting term with an L1 norm, leading to automatic
robustness to outliers. To solve the resulting minimization
problem we propose an extremely simple and efficient
numerical scheme based on split-Bregman iteration combined
with DCT. Experimental validation shows the high-quality
results obtained in short processing times.},
Key = {fds304070}
}

@article{fds265090,
Author = {Bar, L and Sapiro, G},
Title = {Hierarchical invariant sparse modeling for image
analysis},
Journal = {Proceedings International Conference on Image Processing,
Icip},
Pages = {2397-2400},
Publisher = {IEEE},
Year = {2011},
Month = {December},
ISSN = {1522-4880},
url = {http://dx.doi.org/10.1109/ICIP.2011.6116125},
Abstract = {Sparse representation theory has been increasingly used in
signal processing and machine learning. In this paper we
introduce a hierarchical sparse modeling approach which
integrates information from the image patch level to derive
a mid-level invariant image and pattern representation. The
proposed framework is based on a hierarchical architecture
of dictionary learning for sparse coding in a cortical
(log-polar) space, combined with a novel pooling operator
which incorporates the Rapid transform and max pooling to
attain rotation and scale invariance. The invariant sparse
representation of patterns here presented - can be used in
different object recognition tasks. Promising results are
obtained for three applications - 2D shapes classification,
texture recognition and object detection. © 2011
IEEE.},
Doi = {10.1109/ICIP.2011.6116125},
Key = {fds265090}
}

@article{fds265098,
Author = {Yu, G and Sapiro, G},
Title = {Statistical compressed sensing of Gaussian mixture
models},
Journal = {Ieee Transactions on Signal Processing},
Volume = {59},
Number = {12},
Pages = {5842-5858},
Publisher = {Institute of Electrical and Electronics Engineers
(IEEE)},
Year = {2011},
Month = {December},
ISSN = {1053-587X},
url = {http://dx.doi.org/10.1109/TSP.2011.2168521},
Abstract = {A novel framework of compressed sensing, namely statistical
compressed sensing (SCS), that aims at efficiently sampling
a collection of signals that follow a statistical
distribution, and achieving accurate reconstruction on
average, is introduced. SCS based on Gaussian models is
investigated in depth. For signals that follow a single
Gaussian model, with Gaussian or Bernoulli sensing matrices
of ${\cal O}(k)$ measurements, considerably smaller than the
${\cal O}(k \log(N/k))$ required by conventional CS based on
sparse models, where $N$ is the signal dimension, and with
an optimal decoder implemented via linear filtering,
significantly faster than the pursuit decoders applied in
conventional CS, the error of SCS is shown tightly upper
bounded by a constant times the best $k$-term approximation
error, with overwhelming probability. The failure
probability is also significantly smaller than that of
conventional sparsity-oriented CS. Stronger yet simpler
results further show that for any sensing matrix, the error
of Gaussian SCS is upper bounded by a constant times the
best $k$-term approximation with probability one, and the
bound constant can be efficiently calculated. For Gaussian
mixture models (GMMs), that assume multiple Gaussian
distributions and that each signal follows one of them with
an unknown index, a piecewise linear estimator is introduced
to decode SCS. The accuracy of model selection, at the heart
of the piecewise linear decoder, is analyzed in terms of the
properties of the Gaussian distributions and the number of
sensing measurements. A maximization-maximization (Max-Max)
algorithm that iteratively estimates the Gaussian models
parameters, the signals model selection, and decodes the
signals, is presented for GMM-based SCS. In real image
sensing applications, GMM-based SCS is shown to lead to
improved results compared to conventional CS, at a
considerably lower computational cost. © 2011
IEEE.},
Doi = {10.1109/TSP.2011.2168521},
Key = {fds265098}
}

@article{fds265130,
Author = {Zhou, M and Carin, L and Yang, H and Dunson, D and Sapiro,
G},
Title = {Dependent hierarchical beta process for image interpolation
and denoising},
Journal = {Journal of Machine Learning Research},
Volume = {15},
Pages = {883-891},
Year = {2011},
Month = {December},
ISSN = {1532-4435},
Abstract = {A dependent hierarchical beta process (dHBP) is developed as
a prior for data that may be represented in terms of a
sparse set of latent features, with covariate-dependent
feature usage. The dHBP is applicable to general covariates
and data models, imposing that signals with similar
covariates are likely to be manifested in terms of similar
features. Coupling the dHBP with the Bernoulli process, and
upon marginalizing out the dHBP, the model may be
interpreted as a covariate-dependent hierarchical Indian
buffet process. As applications, we consider interpolation
and denoising of an image, with covariates defined by the
location of image patches within an image. Two types of
noise models are considered: (i) typical white Gaussian
noise; and (ii) spiky noise of arbitrary amplitude,
distributed uniformly at random. In these examples, the
features correspond to the atoms of a dictionary, learned
based upon the data under test (without a priori training
data). State-of-the-art performance is demonstrated, with
efficient inference using hybrid Gibbs, Metropolis-Hastings
and slice sampling. Copyright 2011 by the
authors.},
Key = {fds265130}
}

@article{fds265084,
Author = {Jin, Y and Shi, Y and Jahanshad, N and Aganj, I and Sapiro, G and Toga, AW and Thompson, PM},
Title = {3D elastic registration improves HARDI-derived fiber
alignment and automated tract clustering},
Journal = {Proceedings International Symposium on Biomedical
Imaging},
Pages = {822-826},
Publisher = {IEEE},
Year = {2011},
Month = {November},
ISSN = {1945-7928},
url = {http://dx.doi.org/10.1109/ISBI.2011.5872531},
Abstract = {High angular resolution diffusion imaging (HARDI) allows
population studies of fiber integrity and connectivity.
Tractography can extract individual fibers. For group
studies, fibers must be clustered into recognizable bundles
found consistently across subjects. Nonlinear image
registration may improve population clustering. To test
this, we performed whole-brain tractography with an
orientation distribution function based Hough transform
HARDI. We warped all extracted fibers to a
geometrically-centered template using a 3D elastic
registration driven by fractional anisotropy maps, to align
embedded tracts. Fiber alignment was evaluated by
calculating distances among corresponding fibers across
subjects. Before and after warping, we performed spectral
clustering of the fibers using a k-means method, based on
eigenvectors of a fiber similarity matrix. In tests with an
overlap metric, non-rigid fiber warping yielded more robust
clustering results. Non-rigid warping is therefore
advantageous for population studies using multi-subject
Doi = {10.1109/ISBI.2011.5872531},
Key = {fds265084}
}

@article{fds265085,
Author = {Caruyer, E and Aganj, I and Lenglet, C and Sapiro, G and Deriche,
R},
Title = {Online motion detection in high angular resolution diffusion
imaging},
Journal = {Proceedings International Symposium on Biomedical
Imaging},
Pages = {516-519},
Publisher = {IEEE},
Year = {2011},
Month = {November},
ISSN = {1945-7928},
url = {http://dx.doi.org/10.1109/ISBI.2011.5872458},
Abstract = {The orientation distribution function (ODF) can be
reconstructed online incrementally from diffusion-weighted
MRI with a Kalman filtering framework. This online
reconstruction can provide real-time feedback to the
practitioner, especially appreciated for long acquisition
protocols typical in Q-ball imaging. On top of the Kalman
filter, we propose a method to evaluate online the
reconstruction accuracy of the estimated ODF in constant
solid angle. In addition, monitoring the residuals of the
Kalman filter, we design, based on statistical tests, two
algorithms for online detection of subject motion. The
proposed techniques, tested on real and synthetic data under
various experimental conditions, can detect rotation by
angle less than 3. © 2011 IEEE.},
Doi = {10.1109/ISBI.2011.5872458},
Key = {fds265085}
}

@article{fds265086,
Author = {Jahanshad, N and Aganj, I and Lenglet, C and Joshi, A and Jin, Y and Barysheva, M and McMahon, KL and De Zubicaray and GI and Martin, NG and Wright, MJ and Toga, AW and Sapiro, G and Thompson,
PM},
Title = {Sex differences in the human connectome: 4-Tesla high
angular resolution diffusion imaging (HARDI) tractography in
Journal = {Proceedings International Symposium on Biomedical
Imaging},
Pages = {939-943},
Publisher = {IEEE},
Year = {2011},
Month = {November},
ISSN = {1945-7928},
url = {http://dx.doi.org/10.1109/ISBI.2011.5872558},
Abstract = {Cortical connectivity is associated with cognitive and
behavioral traits that are thought to vary between sexes.
Using high-angular resolution diffusion imaging at 4 Tesla,
we scanned 234 young adult twins and siblings (mean age:
23.4 2.0 SD years) with 94 diffusion-encoding directions. We
applied a novel Hough transform method to extract fiber
tracts throughout the entire brain, based on fields of
constant solid angle orientation distribution functions
(ODFs). Cortical surfaces were generated from each subject's
3D T1-weighted structural MRI scan, and tracts were aligned
to the anatomy. Network analysis revealed the proportions of
fibers interconnecting 5 key subregions of the frontal
cortex, including connections between hemispheres. We found
significant sex differences (147 women/87 men) in the
proportions of fibers connecting contralateral superior
frontal cortices. Interhemispheric connectivity was greater
in women, in line with long-standing theories of hemispheric
specialization. These findings may be relevant for ongoing
studies of the human connectome. © 2011
IEEE.},
Doi = {10.1109/ISBI.2011.5872558},
Key = {fds265086}
}

@article{fds265087,
Author = {Zhan, L and Leow, AD and Aganj, I and Lenglet, C and Sapiro, G and Yacoub,
E and Harel, N and Toga, AW and Thompson, PM},
Title = {Differential information content in staggered multiple shell
hardi measured by the tensor distribution
function},
Journal = {Proceedings International Symposium on Biomedical
Imaging},
Pages = {305-309},
Publisher = {IEEE},
Year = {2011},
Month = {November},
ISSN = {1945-7928},
url = {http://dx.doi.org/10.1109/ISBI.2011.5872411},
Abstract = {Diffusion tensor imaging has accelerated the study of brain
connectivity, but single-tensor diffusion models are too
simplistic to model fiber crossing and mixing. Hybrid
diffusion imaging (HYDI) samples the radial and angular
structure of local diffusion on multiple spherical shells in
q-space, combining the high SNR and CNR achievable at low
and high b-values, respectively. We acquired and analyzed
human multi-shell HARDI at ultra-high field-strength (7
Tesla; b=1000, 2000, 3000 s/mm2). In experiments with the
tensor distribution function (TDF), the b-value affected the
intrinsic uncertainty for estimating component fiber
orientations and their diffusion eigenvalues. We computed
orientation density functions by least-squares fitting in
multiple HARDI shells simultaneously. Within the range
examined, higher b-values gave improved orientation
estimates but poorer eigenvalue estimates; lower b-values
showed opposite strengths and weaknesses. Combining these
strengths, multiple-shell HARDI, especially with staggered
angular sampling, outperformed single-shell scanning
protocols, even when overall scanning time was held
Doi = {10.1109/ISBI.2011.5872411},
Key = {fds265087}
}

@article{fds265088,
Author = {Prasad, G and Jahanshad, N and Aganj, I and Lenglet, C and Sapiro, G and Toga, AW and Thompson, PM},
Title = {Atlas-based fiber clustering for multi-subject analysis of
high angular resolution diffusion imaging
tractography},
Journal = {Proceedings International Symposium on Biomedical
Imaging},
Pages = {276-280},
Publisher = {IEEE},
Year = {2011},
Month = {November},
ISSN = {1945-7928},
url = {http://dx.doi.org/10.1109/ISBI.2011.5872405},
Abstract = {High angular resolution diffusion imaging (HARDI) allows in
vivo analysis of the white matter structure and
connectivity. Based on orientation distribution functions
(ODFs) that represent the directionality of water diffusion
at each point in the brain, tractography methods can recover
major axonal pathways. This enables tract-based analysis of
fiber integrity and connectivity. For multi-subject
comparisons, fibers may be clustered into bundles that are
consistently found across subjects. To do this, we scanned
20 young adults with HARDI at 4 T. From the reconstructed
ODFs, we performed whole-brain tractography with a novel
Hough transform method. We then used measures of agreement
between the extracted 3D curves and a co-registered
probabilistic DTI atlas to select key pathways. Using median
filtering and a shortest path graph search, we derived the
maximum density path to compactly represent each tract in
the population. With this tract-based method, we performed
tract-based analysis of fractional anisotropy, and assessed
how the chosen tractography algorithm influenced the
results. The resulting method may expedite population-based
statistical analysis of HARDI and DTI. © 2011
IEEE.},
Doi = {10.1109/ISBI.2011.5872405},
Key = {fds265088}
}

@article{fds265097,
Author = {Sapiro, G},
Title = {Comparing shapes, understanding evolution.},
Journal = {Proceedings of the National Academy of Sciences of the
United States of America},
Volume = {108},
Number = {45},
Pages = {18189-18190},
Year = {2011},
Month = {November},
url = {http://www.ncbi.nlm.nih.gov/pubmed/22042848},
Doi = {10.1073/pnas.1114928108},
Key = {fds265097}
}

@article{fds265132,
Author = {Castrodad, A and Xing, Z and Greer, JB and Bosch, E and Carin, L and Sapiro, G},
Title = {Learning discriminative sparse representations for modeling,
source separation, and mapping of hyperspectral
imagery},
Journal = {Ieee Transactions on Geoscience and Remote
Sensing},
Volume = {49},
Number = {11 PART 1},
Pages = {4263-4281},
Publisher = {Institute of Electrical and Electronics Engineers
(IEEE)},
Year = {2011},
Month = {November},
ISSN = {0196-2892},
url = {http://dx.doi.org/10.1109/TGRS.2011.2163822},
Abstract = {A method is presented for subpixel modeling, mapping, and
classification in hyperspectral imagery using learned
block-structured discriminative dictionaries, where each
block is adapted and optimized to represent a material in a
compact and sparse manner. The spectral pixels are modeled
by linear combinations of subspaces defined by the learned
dictionary atoms, allowing for linear mixture analysis. This
model provides flexibility in source representation and
selection, thus accounting for spectral variability,
small-magnitude errors, and noise. A spatial-spectral
coherence regularizer in the optimization allows pixel
classification to be influenced by similar neighbors. We
extend the proposed approach for cases for which there is no
knowledge of the materials in the scene, unsupervised
classification, and provide experiments and comparisons with
simulated and real data. We also present results when the
data have been significantly undersampled and then
reconstructed, still retaining high-performance
classification, showing the potential role of compressive
sensing and sparse modeling techniques in efficient
acquisition/transmission missions for hyperspectral imagery.
Doi = {10.1109/TGRS.2011.2163822},
Key = {fds265132}
}

@article{fds265128,
Author = {Chen, B and Polatkan, G and Sapiro, G and Dunson, DB and Carin,
L},
Title = {The hierarchical beta process for convolutional factor
analysis and deep learning},
Journal = {Proceedings of the 28th International Conference on Machine
Learning, Icml 2011},
Pages = {361-368},
Year = {2011},
Month = {October},
Abstract = {A convolutional factor-analysis model is developed, with the
number of filters (factors) inferred via the beta process
learning, respectively. The computation of the model
parameters is implemented within a Bayesian setting,
employing Gibbs sampling; we explicitly exploit the
convolutional nature of the expansion to accelerate
computations. The model is used in a multi-level ("deep")
analysis of general data, with specific results presented
for image-processing data sets, e.g., classification.
Key = {fds265128}
}

@article{fds265129,
Author = {Li, L and Zhou, M and Sapiro, G and Carin, L},
Title = {On the integration of topic modeling and dictionary
learning},
Journal = {Proceedings of the 28th International Conference on Machine
Learning, Icml 2011},
Pages = {625-632},
Year = {2011},
Month = {October},
Abstract = {A new nonparametric Bayesian model is developed to integrate
dictionary learning and topic model into a unified
framework. The model is employed to analyze partially
annotated images, with the dictionary learning performed
directly on image patches. Efficient inference is performed
with a Gibbs-slice sampler, and encouraging results are
reported on widely used datasets. Copyright 2011 by the
author(s)/owner(s).},
Key = {fds265129}
}

@article{fds264856,
Author = {Sprechmann, P and Ramirez, I and Sapiro, G and Eldar,
YC},
Title = {C-HiLasso: A Collaborative Hierarchical Sparse Modeling
Framework},
Journal = {Ieee Transactions on Signal Processing},
Volume = {59},
Number = {9},
Pages = {4183-4198},
Publisher = {Institute of Electrical and Electronics Engineers
(IEEE)},
Year = {2011},
Month = {September},
url = {http://dx.doi.org/10.1109/tsp.2011.2157912},
Doi = {10.1109/tsp.2011.2157912},
Key = {fds264856}
}

@article{fds265094,
Author = {Sprechmann, P and Ramírez, I and Sapiro, G and Eldar,
YC},
Title = {C-HiLasso: A collaborative hierarchical sparse modeling
framework},
Journal = {Ieee Transactions on Signal Processing},
Volume = {59},
Number = {9},
Pages = {4183-4198},
Year = {2011},
Month = {September},
ISSN = {1053-587X},
url = {http://dx.doi.org/10.1109/TSP.2011.2157912},
Abstract = {Sparse modeling is a powerful framework for data analysis
and processing. Traditionally, encoding in this framework is
performed by solving an ℓ1-regularized linear regression
problem, commonly referred to as Lasso or Basis Pursuit. In
this work we combine the sparsity-inducing property of the
Lasso at the individual feature level, with the
block-sparsity property of the Group Lasso, where sparse
groups of features are jointly encoded, obtaining a sparsity
pattern hierarchically structured. This results in the
Hierarchical Lasso (HiLasso), which shows important
practical advantages. We then extend this approach to the
collaborative case, where a set of simultaneously coded
signals share the same sparsity pattern at the higher
(group) level, but not necessarily at the lower (inside the
group) level, obtaining the collaborative HiLasso model
(C-HiLasso). Such signals then share the same active groups,
or classes, but not necessarily the same active set. This
model is very well suited for applications such as source
identification and separation. An efficient optimization
procedure, which guarantees convergence to the global
optimum, is developed for these new models. The underlying
presentation of the framework and optimization approach is
complemented by experimental examples and theoretical
results regarding recovery guarantees. © 2011
IEEE.},
Doi = {10.1109/TSP.2011.2157912},
Key = {fds265094}
}

@article{fds265096,
Author = {Yatziv, L and Ibarz, J and Strobel, N and Datta, S and Sapiro,
G},
Title = {Esophagus silhouette extraction and reconstruction from
fluoroscopic views for cardiac ablation procedure
guidance.},
Journal = {Ieee Transactions on Information Technology in Biomedicine :
a Publication of the Ieee Engineering in Medicine and
Biology Society},
Volume = {15},
Number = {5},
Pages = {703-708},
Year = {2011},
Month = {September},
url = {http://www.ncbi.nlm.nih.gov/pubmed/21775266},
Abstract = {Cardiac ablation involves the risk of serious complications
when thermal injury to the esophagus occurs. This paper
proposes to reduce the risk of such injuries by a proactive
visualization technique, improving physician awareness of
the esophagus location in the absence of or in addition to a
reactive monitoring device such as a thermal probe. This is
achieved by combining a graphical representation of the
esophagus with live fluoroscopy. Toward this goal, we
present an automated method to reconstruct and visualize a
3-D esophagus model from fluoroscopy image sequences
acquired using different C-arm viewing directions. In order
to visualize the esophagus under fluoroscopy, it is first
biomarked by swallowing a contrast agent such as barium.
Images obtained in this procedure are then used to
automatically extract the 2-D esophagus silhouette and
reconstruct a 3-D surface of the esophagus internal wall.
Once the 3-D representation has been computed, it can be
visualized using fluoroscopy overlay techniques. Compared to
3-D esophagus imaging using CT or C-arm CT, our proposed
fluoroscopy method requires low radiation dose and enables a
simpler workflow on geometry-calibrated standard C-arm
systems.},
Doi = {10.1109/titb.2011.2162247},
Key = {fds265096}
}

@article{fds265080,
Author = {Yu, G and Sapiro, G},
Title = {Statistical compressive sensing of Gaussian mixture
models},
Journal = {2015 Ieee International Conference on Acoustics, Speech, and
Signal Processing (Icassp)},
Pages = {3728-3731},
Year = {2011},
Month = {August},
ISSN = {1520-6149},
url = {http://dx.doi.org/10.1109/ICASSP.2011.5947161},
Abstract = {A new framework of compressive sensing (CS), namely
statistical compressive sensing (SCS), that aims at
efficiently sampling a collection of signals that follow a
statistical distribution and achieving accurate
reconstruction on average, is introduced. For signals
following a Gaussian distribution, with Gaussian or
Bernoulli sensing matrices of O(k) measurements,
considerably smaller than the O(k log(N/k)) required by
conventional CS, where N is the signal dimension, and with
an optimal decoder implemented with linear filtering,
significantly faster than the pursuit decoders applied in
conventional CS, the error of SCS is shown tightly upper
bounded by a constant times the best k-term approximation
error, with overwhelming probability. The failure
probability is also significantly smaller than that of
conventional CS. Stronger yet simpler results further show
that for any sensing matrix, the error of Gaussian SCS is
upper bounded by a constant times the best k-term
approximation with probability one, and the bound constant
can be efficiently calculated. For signals following
Gaussian mixture models, SCS with a piecewise linear decoder
is introduced and shown to produce for real images better
results than conventional CS based on sparse models. © 2011
IEEE.},
Doi = {10.1109/ICASSP.2011.5947161},
Key = {fds265080}
}

@article{fds265081,
Author = {Sprechmann, P and Ramirez, I and Cancela, P and Sapiro,
G},
Title = {Collaborative sources identification in mixed signals via
hierarchical sparse modeling},
Journal = {2015 Ieee International Conference on Acoustics, Speech, and
Signal Processing (Icassp)},
Pages = {5816-5819},
Publisher = {IEEE},
Year = {2011},
Month = {August},
ISSN = {1520-6149},
url = {http://dx.doi.org/10.1109/ICASSP.2011.5947683},
Abstract = {A collaborative framework for detecting the different
sources in mixed signals is presented in this paper. The
approach is based on C-HiLasso, a convex collaborative
hierarchical sparse model, and proceeds as follows. First,
we build a structured dictionary for mixed signals by
concatenating a set of sub-dictionaries, each one of them
learned to sparsely model one of a set of possible classes.
Then, the coding of the mixed signal is performed by
efficiently solving a convex optimization problem that
combines standard sparsity with group and collaborative
sparsity. The present sources are identified by looking at
the sub-dictionaries automatically selected in the coding.
The collaborative filtering in C-HiLasso takes advantage of
the temporal/spatial redundancy in the mixed signals,
letting collections of samples collaborate in identifying
the classes, while allowing individual samples to have
different internal sparse representations. This
collaboration is critical to further stabilize the sparse
representation of signals, in particular the
class/sub-dictionary selection. The internal sparsity inside
the sub-dictionaries, as naturally incorporated by the
hierarchical aspects of C-HiLasso, is critical to make the
model consistent with the essence of the sub-dictionaries
that have been trained for sparse representation of each
individual class. We present applications from speaker and
instrument identification and texture separation. In the
case of audio signals, we use sparse modeling to describe
the short-term power spectrum envelopes of harmonic sounds.
The proposed pitch independent method automatically detects
the number of sources on a recording. © 2011
IEEE.},
Doi = {10.1109/ICASSP.2011.5947683},
Key = {fds265081}
}

@article{fds265082,
Author = {Léger, F and Yu, G and Sapiro, G},
Title = {Efficient matrix completion with Gaussian
models},
Journal = {2015 Ieee International Conference on Acoustics, Speech, and
Signal Processing (Icassp)},
Pages = {1113-1116},
Publisher = {IEEE},
Year = {2011},
Month = {August},
ISSN = {1520-6149},
url = {http://dx.doi.org/10.1109/ICASSP.2011.5946603},
Abstract = {A general framework based on Gaussian models and a MAPEM
algorithm is introduced in this paper for solving matrix/
table completion problems. The numerical experiments with
the standard and challenging movie ratings data show that
the proposed approach, based on probably one of the simplest
probabilistic models, leads to the results in the same
ballpark as the state-of-the-art, at a lower computational
Doi = {10.1109/ICASSP.2011.5946603},
Key = {fds265082}
}

@article{fds265083,
Author = {Ramírez, I and Sapiro, G},
Title = {Sparse coding and dictionary learning based on the MDL
principle},
Journal = {2015 Ieee International Conference on Acoustics, Speech, and
Signal Processing (Icassp)},
Pages = {2160-2163},
Publisher = {IEEE},
Year = {2011},
Month = {August},
ISSN = {1520-6149},
url = {http://dx.doi.org/10.1109/ICASSP.2011.5946755},
Abstract = {The power of sparse signal coding with learned overcomplete
dictionaries has been demonstrated in a variety of
applications and fields, from signal processing to
statistical inference and machine learning. However, the
statistical properties of these models, such as underfitting
or overfitting given sets of data, are still not well
characterized in the literature. This work aims at filling
this gap by means of the Minimum Description Length (MDL)
principle - a well established information-theoretic
approach to statistical inference. The resulting framework
derives a family of efficient sparse coding and modeling
(dictionary learning) algorithms, which by virtue of the MDL
principle, are completely parameter free. Furthermore, such
framework allows to incorporate additional prior information
in the model, such as Markovian dependencies, in a natural
way. We demonstrate the performance of the proposed
framework with results for image denoising and
Doi = {10.1109/ICASSP.2011.5946755},
Key = {fds265083}
}

@article{fds265127,
Author = {Zhou, M and Yang, H and Sapiro, G and Dunson, D and Carin,
L},
Title = {Covariate-dependent dictionary learning and sparse
coding},
Journal = {2015 Ieee International Conference on Acoustics, Speech, and
Signal Processing (Icassp)},
Pages = {5824-5827},
Publisher = {IEEE},
Year = {2011},
Month = {August},
ISSN = {1520-6149},
url = {http://dx.doi.org/10.1109/ICASSP.2011.5947685},
Abstract = {A dependent hierarchical beta process (dHBP) is developed as
a prior for data that may be represented in terms of a
sparse set of latent features (dictionary elements), with
covariate-dependent feature usage. The dHBP is applicable to
general covariates and data models, imposing that signals
with similar covariates are likely to be manifested in terms
of similar features. As an application, we consider the
simultaneous sparse modeling of multiple images, with the
covariate of a given image linked to its similarity to all
other images (as applied in manifold learning). Efficient
inference is performed using hybrid Gibbs,
Metropolis-Hastings and slice sampling. © 2011
IEEE.},
Doi = {10.1109/ICASSP.2011.5947685},
Key = {fds265127}
}

@article{fds264774,
Author = {Sapiro, G},
Title = {Should we choose to explain and understand? The
interpretation of human actions},
Journal = {Quinzaine Litteraire},
Number = {1043},
Pages = {28-28},
Year = {2011},
Month = {August},
ISSN = {0048-6493},
Key = {fds264774}
}

@article{fds265095,
Author = {Aganj, I and Lenglet, C and Jahanshad, N and Yacoub, E and Harel, N and Thompson, PM and Sapiro, G},
Title = {A Hough transform global probabilistic approach to
multiple-subject diffusion MRI tractography.},
Journal = {Medical Image Analysis},
Volume = {15},
Number = {4},
Pages = {414-425},
Year = {2011},
Month = {August},
url = {http://www.ncbi.nlm.nih.gov/pubmed/21376655},
Abstract = {A global probabilistic fiber tracking approach based on the
voting process provided by the Hough transform is introduced
in this work. The proposed framework tests candidate 3D
curves in the volume, assigning to each one a score computed
from the diffusion images, and then selects the curves with
the highest scores as the potential anatomical connections.
The algorithm avoids local minima by performing an
exhaustive search at the desired resolution. The technique
is easily extended to multiple subjects, considering a
single representative volume where the registered
high-angular resolution diffusion images (HARDI) from all
the subjects are non-linearly combined, thereby obtaining
population-representative tracts. The tractography algorithm
is run only once for the multiple subjects, and no tract
alignment is necessary. We present experimental results on
HARDI volumes, ranging from simulated and 1.5T physical
phantoms to 7T and 4T human brain and 7T monkey brain
datasets.},
Doi = {10.1016/j.media.2011.01.003},
Key = {fds265095}
}

@article{fds265092,
Author = {Arias, P and Facciolo, G and Caselles, V and Sapiro,
G},
Title = {A variational framework for exemplar-based image
inpainting},
Journal = {International Journal of Computer Vision},
Volume = {93},
Number = {3},
Pages = {319-347},
Publisher = {Springer Nature},
Year = {2011},
Month = {July},
ISSN = {0920-5691},
url = {http://dx.doi.org/10.1007/s11263-010-0418-7},
Abstract = {Non-local methods for image denoising and inpainting have
gained considerable attention in recent years. This is in
part due to their superior performance in textured images, a
known weakness of purely local methods. Local methods on the
other hand have demonstrated to be very appropriate for the
recovering of geometric structures such as image edges. The
synthesis of both types of methods is a trend in current
research. Variational analysis in particular is an
appropriate tool for a unified treatment of local and
nonlocal methods. In this work we propose a general
variational framework for non-local image inpainting, from
which important and representative previous inpainting
ones. We explicitly study some of these, relating them to
previous work and showing results on synthetic and real
LLC.},
Doi = {10.1007/s11263-010-0418-7},
Key = {fds265092}
}

@article{fds265093,
Author = {Wirth, B and Bar, L and Rumpf, M and Sapiro, G},
Title = {A continuum mechanical approach to geodesics in shape
space},
Journal = {International Journal of Computer Vision},
Volume = {93},
Number = {3},
Pages = {293-318},
Publisher = {Springer Nature},
Year = {2011},
Month = {July},
ISSN = {0920-5691},
url = {http://dx.doi.org/10.1007/s11263-010-0416-9},
Abstract = {In this paper concepts from continuum mechanics are used to
define geodesic paths in the space of shapes, where shapes
are implicitly described as boundary contours of objects.
The proposed shape metric is derived from a continuum
mechanical notion of viscous dissipation. A geodesic path is
defined as the family of shapes such that the total amount
of viscous dissipation caused by an optimal material
transport along the path is minimized. The approach can
easily be generalized to shapes given as segment contours of
multi-labeled images and to geodesic paths between partially
occluded objects. The proposed computational framework for
finding such a minimizer is based on the time discretization
of a geodesic path as a sequence of pairwise matching
problems, which is strictly invariant with respect to rigid
body motions and ensures a 1-1 correspondence along the
induced flow in shape space. When decreasing the time step
size, the proposed model leads to the minimization of the
actual geodesic length, where the Hessian of the pairwise
matching energy reflects the chosen Riemannian metric on the
underlying shape space. If the constraint of pairwise shape
correspondence is replaced by the volume of the shape
mismatch as a penalty functional, one obtains for decreasing
time step size an optical flow term controlling the
transport of the shape by the underlying motion field. The
method is implemented via a level set representation of
shapes, and a finite element approximation is employed as
spatial discretization both for the pairwise matching
deformations and for the level set representations. The
numerical relaxation of the energy is performed via an
efficient multi-scale procedure in space and time. Various
examples for 2D and 3D shapes underline the effectiveness
and robustness of the proposed approach. © 2010 Springer
Doi = {10.1007/s11263-010-0416-9},
Key = {fds265093}
}

@article{fds265091,
Author = {Sapiro, G},
Title = {Technical perspective: Images everywhere looking for
models},
Journal = {Communications of the Acm},
Volume = {54},
Number = {5},
Pages = {108},
Publisher = {Association for Computing Machinery (ACM)},
Year = {2011},
Month = {May},
ISSN = {0001-0782},
url = {http://dx.doi.org/10.1145/1941487.1941512},
Abstract = {Deriving appropriate regularization terms, priors or models,
has occupied the research community since the early days of
digital image processing. Different image models can be
appropriate for different types of images; for example, MRI
and natural images should have different models. The basic
underlying concept is that local image information repeats
itself across the non-local image. Noise, on the other hand,
is expected in numerous scenarios to be random. Therefore,
collecting those similar local regions all across the image,
the noise can be eliminated by simple estimators based on
having multiple observations of the same underlying signal
under different noise conditions. The self-similarity model
assumes the dictionary is the image itself, or actually its
local patches. All these models indicate that images, and in
particular image patches, do not actually live in the
ambient high-dimensional space, but in some much lower
dimensional stratification embedded on it.},
Doi = {10.1145/1941487.1941512},
Key = {fds265091}
}

@article{fds264817,
Author = {Sapiro, G},
Title = {WHAT IS A FRENCH PHILOSOPHER? The social life of concepts
(1880-1980)},
Journal = {Quinzaine Litteraire},
Number = {1034},
Pages = {21-21},
Year = {2011},
Month = {March},
ISSN = {0048-6493},
Key = {fds264817}
}

@article{fds264781,
Author = {Sapiro, G},
Title = {Merchants of Culture. The Publishing Business in the
Twenty-First Century},
Journal = {Actes De La Recherche En Sciences Sociales},
Number = {186-87},
Pages = {132-135},
Year = {2011},
Month = {March},
ISSN = {0335-5322},
Key = {fds264781}
}

@article{fds265131,
Author = {Carin, L and Baraniuk, RG and Cevher, V and Dunson, D and Jordan, MI and Sapiro, G and Wakin, MB},
Title = {Learning Low-Dimensional Signal Models: A Bayesian approach
based on incomplete measurements.},
Journal = {Ieee Signal Processing Magazine},
Volume = {28},
Number = {2},
Pages = {39-51},
Year = {2011},
Month = {March},
ISSN = {1053-5888},
url = {http://dx.doi.org/10.1109/MSP.2010.939733},
Abstract = {Sampling, coding, and streaming even the most essential
data, e.g., in medical imaging and weather-monitoring
applications, produce a data deluge that severely stresses
the available analog-to-digital converter, communication
bandwidth, and digital-storage resources. Surprisingly,
while the ambient data dimension is large in many problems,
the relevant information in the data can reside in a much
lower dimensional space. © 2006 IEEE.},
Doi = {10.1109/MSP.2010.939733},
Key = {fds265131}
}

@article{fds265089,
Author = {Ma, Y and Niyogi, P and Sapiro, G and Vidal, R},
Title = {Dimensionality reduction via subspace and submanifold
learning},
Journal = {Ieee Signal Processing Magazine},
Volume = {28},
Number = {2},
Pages = {14-126},
Publisher = {Institute of Electrical and Electronics Engineers
(IEEE)},
Year = {2011},
Month = {January},
ISSN = {1053-5888},
url = {http://dx.doi.org/10.1109/MSP.2010.940005},
Abstract = {The problem of finding and exploiting low-dimensional
structures in high-dimensional data is taking on increasing
importance in image, video, or audio processing; Web data
analysis/search; and bioinformatics, where data sets now
routinely lie in observational spaces of thousands,
millions, or even billions of dimensions. The curse of
dimensionality is in full play here: We often need to
conduct meaningful inference with a limited number of
samples in a very high-dimensional space. Conventional
statistical and computational tools have become severely
inadequate for processing and analyzing such
Doi = {10.1109/MSP.2010.940005},
Key = {fds265089}
}

@article{fds264848,
Author = {Sprechmann, P and Ramírez, I and Cancela, P and Sapiro,
G},
Title = {Collaborative sources identification in mixed signals via
hierarchical sparse modeling.},
Journal = {Icassp},
Pages = {5816-5819},
Publisher = {IEEE},
Year = {2011},
ISBN = {978-1-4577-0539-7},
url = {http://www.informatik.uni-trier.de/~ley/db/conf/icassp/icassp2010.html},
Doi = {10.1109/ICASSP.2011.5947683},
Key = {fds264848}
}

@article{fds264849,
Author = {Léger, F and Yu, G and Sapiro, G},
Title = {Efficient matrix completion with Gaussian
models.},
Journal = {Icassp},
Pages = {1113-1116},
Publisher = {IEEE},
Year = {2011},
ISBN = {978-1-4577-0539-7},
url = {http://www.informatik.uni-trier.de/~ley/db/conf/icassp/icassp2010.html},
Doi = {10.1109/ICASSP.2011.5946603},
Key = {fds264849}
}

@article{fds264858,
Author = {Yu, G and Sapiro, G},
Title = {Statistical Compressed Sensing of Gaussian Mixture
Models.},
Journal = {Ieee Trans. Signal Processing},
Volume = {59},
Pages = {5842-5858},
Year = {2011},
url = {http://dx.doi.org/10.1109/TSP.2011.2168521},
Doi = {10.1109/TSP.2011.2168521},
Key = {fds264858}
}

@article{fds264860,
Author = {Yu, G and Sapiro, G},
Title = {Statistical compressive sensing of Gaussian mixture
models.},
Journal = {Icassp},
Pages = {3728-3731},
Publisher = {IEEE},
Year = {2011},
ISBN = {978-1-4577-0539-7},
url = {http://www.informatik.uni-trier.de/~ley/db/conf/icassp/icassp2010.html},
Doi = {10.1109/ICASSP.2011.5947161},
Key = {fds264860}
}

@article{fds304071,
Author = {Silva, JG and Chen, M and Eldar, YC and Sapiro, G and Carin,
L},
Title = {Blind Compressed Sensing Over a Structured Union of
Subspaces},
Journal = {Corr},
Volume = {abs/1103.2469},
Year = {2011},
url = {http://arxiv.org/abs/1103.2469v1},
Abstract = {This paper addresses the problem of simultaneous signal
recovery and dictionary learning based on compressive
measurements. Multiple signals are analyzed jointly, with
multiple sensing matrices, under the assumption that the
unknown signals come from a union of a small number of
disjoint subspaces. This problem is important, for instance,
in image inpainting applications, in which the multiple
signals are constituted by (incomplete) image patches taken
from the overall image. This work extends standard
dictionary learning and block-sparse dictionary
optimization, by considering compressive measurements, e.g.,
incomplete data). Previous work on blind compressed sensing
is also generalized by using multiple sensing matrices and
relaxing some of the restrictions on the learned dictionary.
Drawing on results developed in the context of matrix
completion, it is proven that both the dictionary and
signals can be recovered with high probability from
compressed measurements. The solution is unique up to block
permutations and invertible linear transformations of the
dictionary atoms. The recovery is contingent on the number
of measurements per signal and the number of signals being
sufficiently large; bounds are derived for these quantities.
In addition, this paper presents a computationally practical
algorithm that performs dictionary learning and signal
recovery, and establishes conditions for its convergence to
a local optimum. Experimental results for image inpainting
demonstrate the capabilities of the method.},
Key = {fds304071}
}

@article{fds304072,
Author = {Duarte-Carvajalino, JM and Sapiro, G and Yu, G and Carin,
L},
Title = {Online Adaptive Statistical Compressed Sensing of Gaussian
Mixture Models},
Journal = {Corr},
Volume = {abs/1112.5895},
Year = {2011},
url = {http://arxiv.org/abs/1112.5895v1},
Abstract = {A framework of online adaptive statistical compressed
sensing is introduced for signals following a mixture model.
The scheme first uses non-adaptive measurements, from which
an online decoding scheme estimates the model selection. As
soon as a candidate model has been selected, an optimal
sensing scheme for the selected model continues to apply.
The final signal reconstruction is calculated from the
measurements. For signals generated from a Gaussian mixture
model, the online adaptive sensing algorithm is given and
its performance is analyzed. On both synthetic and real
image data, the proposed adaptive scheme considerably
reduces the average reconstruction error with respect to
standard statistical compressed sensing that uses fully
random measurements, at a marginally increased computational
complexity.},
Key = {fds304072}
}

@article{fds264788,
Author = {Sapiro, G and Steinmetz, G and Ducournau, C},
Title = {The production of colonial and post-colonial
representations},
Journal = {Actes De La Recherche En Sciences Sociales},
Number = {185},
Pages = {4-11},
Year = {2010},
Month = {December},
ISSN = {0335-5322},
Key = {fds264788}
}

@article{fds264812,
Author = {Steinmetz, G and Sapiro, G and Balandier, G},
Title = {All scientific careers have autobiographical
moments},
Journal = {Actes De La Recherche En Sciences Sociales},
Number = {185},
Pages = {44-61},
Year = {2010},
Month = {December},
ISSN = {0335-5322},
Key = {fds264812}
}

@article{fds265073,
Author = {Yu, G and Sapiro, G and Mallat, S},
Title = {Image modeling and enhancement via structured sparse model
selection},
Journal = {Proceedings International Conference on Image Processing,
Icip},
Pages = {1641-1644},
Publisher = {IEEE},
Year = {2010},
Month = {December},
ISSN = {1522-4880},
url = {http://dx.doi.org/10.1109/ICIP.2010.5653853},
Abstract = {An image representation framework based on structured
sparsemodel selection is introduced in this work. The
corresponding modeling dictionary is comprised of a family
of learned orthogonal bases. For an image patch, a model is
first selected from this dictionary through linear
approximation in a best basis, and the signal estimation is
then calculated with the selected model. The model selection
leads to a guaranteed near optimal denoising estimator. The
degree of freedom in the model selection is equal to the
number of the bases, typically about 10 for natural images,
and is significantly lower than with traditional
overcomplete dictionary approaches, stabilizing the
representation. For an image patch of size √N × √N, the
computational complexity of the proposed framework is O(N2),
typically 2 to 3 orders of magnitude faster than estimation
in an overcomplete dictionary. The orthogonal bases are
adapted to the image of interest and are computed with a
simple and fast procedure. State-of-the-art results are
shown in image denoising, deblurring, and inpainting. ©
2010 IEEE.},
Doi = {10.1109/ICIP.2010.5653853},
Key = {fds265073}
}

@article{fds265125,
Author = {Paisley, J and Zhou, M and Sapiro, G and Carin, L},
Title = {Nonparametric image interpolation and dictionary learning
using spatially-dependent dirichlet and beta process
priors},
Journal = {Proceedings International Conference on Image Processing,
Icip},
Pages = {1869-1872},
Publisher = {IEEE},
Year = {2010},
Month = {December},
ISSN = {1522-4880},
url = {http://dx.doi.org/10.1109/ICIP.2010.5653350},
Abstract = {We present a Bayesian model for image interpolation and
dictionary learning that uses two nonparametric priors for
sparse signal representations: the beta process and the
Dirichlet process. Additionally, the model uses spatial
information within the image to encourage sharing of
information within image subregions. We derive a hybrid
MAP/Gibbs sampler, which performs Gibbs sampling for the
latent indicator variables and MAP estimation for all other
parameters. We present experimental results, where we show
an improvement over other state-of-the-art algorithms in the
Doi = {10.1109/ICIP.2010.5653350},
Key = {fds265125}
}

@article{fds265126,
Author = {Castrodad, A and Xing, Z and Greer, J and Bosch, E and Carin, L and Sapiro,
G},
Title = {Discriminative sparse representations in hyperspectral
imagery},
Journal = {Proceedings International Conference on Image Processing,
Icip},
Pages = {1313-1316},
Publisher = {IEEE},
Year = {2010},
Month = {December},
ISSN = {1522-4880},
url = {http://dx.doi.org/10.1109/ICIP.2010.5651568},
Abstract = {Recent advances in sparse modeling and dictionary learning
for discriminative applications show high potential for
numerous classification tasks. In this paper, we show that
highly accurate material classification from hyperspectral
imagery (HSI) can be obtained with these models, even when
the data is reconstructed from a very small percentage of
the original image samples. The proposed supervised HSI
classification is performed using a measure that accounts
for both reconstruction errors and sparsity levels for
sparse representations based on class-dependent learned
dictionaries. Combining the dictionaries learned for the
different materials, a linear mixing model is derived for
sub-pixel classification. Results with real hyperspectral
data cubes are shown both for urban and non-urban terrain.
Doi = {10.1109/ICIP.2010.5651568},
Key = {fds265126}
}

@article{fds265070,
Author = {Aganj, I and Lenglet, C and Sapiro, G},
Title = {ODF maxima extraction in spherical harmonic representation
via analytical search space reduction},
Journal = {Lecture Notes in Computer Science (Including Subseries
Lecture Notes in Artificial Intelligence and Lecture Notes
in Bioinformatics)},
Volume = {6362 LNCS},
Number = {PART 2},
Pages = {84-91},
Publisher = {Springer Berlin Heidelberg},
Year = {2010},
Month = {November},
ISSN = {0302-9743},
url = {http://dx.doi.org/10.1007/978-3-642-15745-5_11},
Abstract = {By revealing complex fiber structure through the orientation
distribution function (ODF), q-ball imaging has recently
become a popular reconstruction technique in
diffusion-weighted MRI. In this paper, we propose an
analytical dimension reduction approach to ODF maxima
extraction. We show that by expressing the ODF, or any
antipodally symmetric spherical function, in the common
fourth order real and symmetric spherical harmonic basis,
the maxima of the two-dimensional ODF lie on an analytically
derived one-dimensional space, from which we can detect the
ODF maxima. This method reduces the computational complexity
of the maxima detection, without compromising the accuracy.
We demonstrate the performance of our technique on both
artificial and human brain data. © 2010
Springer-Verlag.},
Doi = {10.1007/978-3-642-15745-5_11},
Key = {fds265070}
}

@article{fds265071,
Author = {Bai, X and Wang, J and Sapiro, G},
Title = {Dynamic color flow: A motion-adaptive color model for object
segmentation in video},
Journal = {Lecture Notes in Computer Science (Including Subseries
Lecture Notes in Artificial Intelligence and Lecture Notes
in Bioinformatics)},
Volume = {6315 LNCS},
Number = {PART 5},
Pages = {617-630},
Publisher = {Springer Berlin Heidelberg},
Year = {2010},
Month = {November},
ISSN = {0302-9743},
url = {http://dx.doi.org/10.1007/978-3-642-15555-0_45},
Abstract = {Accurately modeling object colors, and features in general,
plays a critical role in video segmentation and analysis.
Commonly used color models, such as global Gaussian
mixtures, localized Gaussian mixtures, and pixel-wise
adaptive ones, often fail to accurately represent the object
appearance in complicated scenes, thereby leading to
segmentation errors. We introduce a new color model, Dynamic
Color Flow, which unlike previous approaches, incorporates
motion estimation into color modeling in a probabilistic
framework, and adaptively changes model parameters to match
the local properties of the motion. The proposed model
accurately and reliably describes changes in the scene's
appearance caused by motion across frames. We show how to
apply this color model to both foreground and background
layers in a balanced way for efficient object segmentation
in video. Experimental results show that when compared with
previous approaches, our model provides more accurate
foreground and background estimations, leading to more
efficient video object cutout systems. © 2010
Springer-Verlag.},
Doi = {10.1007/978-3-642-15555-0_45},
Key = {fds265071}
}

@article{fds265068,
Author = {Sprechmann, P and Sapiro, G},
Title = {Dictionary learning and sparse coding for unsupervised
clustering},
Journal = {2015 Ieee International Conference on Acoustics, Speech, and
Signal Processing (Icassp)},
Pages = {2042-2045},
Publisher = {IEEE},
Year = {2010},
Month = {November},
ISSN = {1520-6149},
url = {http://dx.doi.org/10.1109/ICASSP.2010.5494985},
Abstract = {A clustering framework within the sparse modeling and
dictionary learning setting is introduced in this work.
Instead of searching for the set of centroid that best fit
the data, as in k-means type of approaches that model the
data as distributions around discrete points, we optimize
for a set of dictionaries, one for each cluster, for which
the signals are best reconstructed in a sparse coding
manner. Thereby, we are modeling the data as the of union of
learned low dimensional subspaces, and data points
associated to subspaces spanned by just a few atoms of the
same learned dictionary are clustered together. Using
learned dictionaries makes this method robust and well
suited to handle large datasets. The proposed clustering
algorithm uses a novel measurement for the quality of the
sparse representation, inspired by the robustness of the
ℓ1 regularization term in sparse coding. We first
illustrate this measurement with examples on standard image
and speech datasets in the supervised classification
setting, showing with a simple approach its discriminative
power and obtaining results comparable to the
state-of-the-art. We then conclude with experiments for
fully unsupervised clustering on extended standard datasets
and texture images, obtaining excellent performance. ©2010
IEEE.},
Doi = {10.1109/ICASSP.2010.5494985},
Key = {fds265068}
}

@article{fds265069,
Author = {Bar, L and Sapiro, G},
Title = {Hierarchical dictionary learning for invariant
classification},
Journal = {2015 Ieee International Conference on Acoustics, Speech, and
Signal Processing (Icassp)},
Pages = {3578-3581},
Publisher = {IEEE},
Year = {2010},
Month = {November},
ISSN = {1520-6149},
url = {http://dx.doi.org/10.1109/ICASSP.2010.5495916},
Abstract = {Sparse representation theory has been increasingly used in
the fields of signal processing and machine learning. The
standard sparse models are not invariant to spatial
transformations such as image rotations, and the
representation is very sensitive even under small such
distortions. Most studies addressing this problem proposed
algorithms which either use transformed data as part of the
training set, or are invariant or robust only under minor
transformations. In this paper we suggest a framework which
extracts sparse features invariant under significant
rotations and scalings. The algorithm is based on a
hierarchical architecture of dictionary learning for sparse
coding in a cortical (log-polar) space. The proposed model
is tested in supervised classification applications and
proved to be robust under transformed data. ©2010
IEEE.},
Doi = {10.1109/ICASSP.2010.5495916},
Key = {fds265069}
}

@article{fds265079,
Author = {Bugeau, A and Bertalmío, M and Caselles, V and Sapiro,
G},
Title = {A comprehensive framework for image inpainting.},
Journal = {Ieee Transactions on Image Processing : a Publication of the
Ieee Signal Processing Society},
Volume = {19},
Number = {10},
Pages = {2634-2645},
Year = {2010},
Month = {October},
url = {http://www.ncbi.nlm.nih.gov/pubmed/20435541},
Abstract = {Inpainting is the art of modifying an image in a form that
is not detectable by an ordinary observer. There are
numerous and very different approaches to tackle the
inpainting problem, though as explained in this paper, the
most successful algorithms are based upon one or two of the
following three basic techniques: copy-and-paste texture
synthesis, geometric partial differential equations (PDEs),
and coherence among neighboring pixels. We combine these
three building blocks in a variational model, and provide a
working algorithm for image inpainting trying to approximate
the minimum of the proposed energy functional. Our
experiments show that the combination of all three terms of
the proposed energy works better than taking each term
separately, and the results obtained are within the
state-of-the-art.},
Doi = {10.1109/tip.2010.2049240},
Key = {fds265079}
}

@article{Bronstein2010,
Author = {Bronstein, AM and Bronstein, MM and Kimmel, R and Mahmoudi, M and Sapiro, G},
Title = {A gromov-hausdorff framework with diffusion geometry for
topologically-robust non-rigid shape matching},
Journal = {International Journal of Computer Vision},
Volume = {89},
Number = {2-3},
Pages = {266-286},
Publisher = {Springer Nature},
Year = {2010},
Month = {September},
ISSN = {0920-5691},
url = {http://dx.doi.org/10.1007/s11263-009-0301-6},
Abstract = {In this paper, the problem of non-rigid shape recognition is
studied from the perspective of metric geometry. In
particular, we explore the applicability of diffusion
distances within the Gromov-Hausdorff framework. While the
traditionally used geodesic distance exploits the shortest
path between points on the surface, the diffusion distance
averages all paths connecting the points. The diffusion
distance constitutes an intrinsic metric which is robust, in
particular, to topological changes. Such changes in the form
of shortcuts, holes, and missing data may be a result of
natural non-rigid deformations as well as acquisition and
representation noise due to inaccurate surface construction.
The presentation of the proposed framework is complemented
with examples demonstrating that in addition to the
relatively low complexity involved in the computation of the
diffusion distances between surface points, its recognition
and matching performances favorably compare to the classical
geodesic distances in the presence of topological changes
between the non-rigid shapes. © 2009 Springer
Doi = {10.1007/s11263-009-0301-6},
Key = {Bronstein2010}
}

@article{fds265067,
Author = {Ramirez, I and Sprechmann, P and Sapiro, G},
Title = {Classification and clustering via dictionary learning with
structured incoherence and shared features},
Journal = {Proceedings of the Ieee Computer Society Conference on
Computer Vision and Pattern Recognition},
Pages = {3501-3508},
Publisher = {IEEE},
Year = {2010},
Month = {August},
ISSN = {1063-6919},
url = {http://dx.doi.org/10.1109/CVPR.2010.5539964},
Abstract = {A clustering framework within the sparse modeling and
dictionary learning setting is introduced in this work.
Instead of searching for the set of centroid that best fit
the data, as in k-means type of approaches that model the
data as distributions around discrete points, we optimize
for a set of dictionaries, one for each cluster, for which
the signals are best reconstructed in a sparse coding
manner. Thereby, we are modeling the data as a union of
learned low dimensional subspaces, and data points
associated to subspaces spanned by just a few atoms of the
same learned dictionary are clustered together. An
incoherence promoting term encourages dictionaries
associated to different classes to be as independent as
possible, while still allowing for different classes to
share features. This term directly acts on the dictionaries,
thereby being applicable both in the supervised and
unsupervised settings. Using learned dictionaries for
classification and clustering makes this method robust and
well suited to handle large datasets. The proposed framework
uses a novel measurement for the quality of the sparse
representation, inspired by the robustness of the
ℓ1regularization term in sparse coding. In the case of
unsupervised classification and/or clustering, a new
initialization based on combining sparse coding with
spectral clustering is proposed. This initialization
clusters the dictionary atoms, and therefore is based on
solving a low dimensional eigen-decomposition problem, being
applicable to large datasets. We first illustrate the
proposed framework with examples on standard image and
speech datasets in the supervised classification setting,
obtaining results comparable to the state-of-the-art with
this simple approach. We then present experiments for fully
unsupervised clustering on extended standard datasets and
texture images, obtaining excellent performance. ©2010
IEEE.},
Doi = {10.1109/CVPR.2010.5539964},
Key = {fds265067}
}

@article{fds265066,
Author = {Caruyer, E and Aganj, I and Muetzel, RL and Lenglet, C and Sapiro, G and Deriche, R},
Title = {Online orientation distribution function reconstrugtion in
constant solid angle and its application to motion detection
in HARDI},
Journal = {2010 7th Ieee International Symposium on Biomedical Imaging:
From Nano to Macro, Isbi 2010 Proceedings},
Pages = {812-815},
Publisher = {IEEE},
Year = {2010},
Month = {August},
url = {http://dx.doi.org/10.1109/ISBI.2010.5490052},
Abstract = {The diffusion orientation distribution function (ODF) can be
reconstructed from q-ball imaging (QBI) to map the complex
intravoxel structure of water diffusion. As acquisition time
is particularly large for high angular resolution diffusion
imaging (HARDI), fast estimation algorithms have recently
been proposed, as an on-line feedback on the reconstruction
accuracy. Thus the acquisition could be stopped or continued
on demand. We adapt these real-time algorithms to the
mathematically correct definition of ODF in constant solid
angle (CSA), and develop a motion detection algorithm upon
this reconstruction. Results of improved fiber crossing
detection by CSA ODF are shown, and motion detection was
implemented and tested in vivo. © 2010 IEEE.},
Doi = {10.1109/ISBI.2010.5490052},
Key = {fds265066}
}

@article{fds264808,
Author = {Sapiro, G},
Title = {Globalization and cultural diversity in the book market: The
case of literary translations in the US and in
France},
Journal = {Poetics},
Volume = {38},
Number = {4},
Pages = {419-439},
Publisher = {Elsevier BV},
Year = {2010},
Month = {August},
ISSN = {0304-422X},
Doi = {10.1016/j.poetic.2010.05.001},
Key = {fds264808}
}

@article{fds265078,
Author = {Aganj, I and Lenglet, C and Sapiro, G and Yacoub, E and Ugurbil, K and Harel, N},
Title = {Reconstruction of the orientation distribution function in
single- and multiple-shell q-ball imaging within constant
solid angle.},
Journal = {Magnetic Resonance in Medicine},
Volume = {64},
Number = {2},
Pages = {554-566},
Year = {2010},
Month = {August},
url = {http://www.ncbi.nlm.nih.gov/pubmed/20535807},
Abstract = {q-Ball imaging is a high-angular-resolution diffusion
imaging technique that has been proven very successful in
resolving multiple intravoxel fiber orientations in MR
images. The standard computation of the orientation
distribution function (the probability of diffusion in a
given direction) from q-ball data uses linear radial
projection, neglecting the change in the volume element
along each direction. This results in spherical
distributions that are different from the true orientation
distribution functions. For instance, they are neither
normalized nor as sharp as expected and generally require
postprocessing, such as artificial sharpening. In this
paper, a new technique is proposed that, by considering the
solid angle factor, uses the mathematically correct
definition of the orientation distribution function and
results in a dimensionless and normalized orientation
distribution function expression. Our model is flexible
enough so that orientation distribution functions can be
estimated either from single q-shell datasets or by
exploiting the greater information available from multiple
q-shell acquisitions. We show that the latter can be
achieved by using a more accurate multiexponential model for
the diffusion signal. The improved performance of the
proposed method is demonstrated on artificial examples and
high-angular-resolution diffusion imaging data acquired on a
7-T magnet.},
Doi = {10.1002/mrm.22365},
Key = {fds265078}
}

@article{fds265064,
Author = {Sprechmann, P and Ramirez, I and Sapiro, G and Eldar,
Y},
Title = {Collaborative hierarchical sparse modeling},
Journal = {2010 44th Annual Conference on Information Sciences and
Systems, Ciss 2010},
Publisher = {IEEE},
Year = {2010},
Month = {June},
url = {http://dx.doi.org/10.1109/CISS.2010.5464845},
Abstract = {Sparse modeling is a powerful framework for data analysis
and processing. Traditionally, encoding in this framework is
done by solving an ℓ1-regularized linear regression
problem, usually called Lasso. In this work we first combine
the sparsity-inducing property of the Lasso model, at the
individual feature level, with the block-sparsity property
of the group Lasso model, where sparse groups of features
are jointly encoded, obtaining a sparsity pattern
hierarchically structured. This results in the hierarchical
Lasso, which shows important practical modeling advantages.
We then extend this approach to the collaborative case,
where a set of simultaneously coded signals share the same
sparsity pattern at the higher (group) level but not
necessarily at the lower one. Signals then share the same
active groups, or classes, but not necessarily the same
active set. This is very well suited for applications such
as source separation. An efficient optimization procedure,
which guarantees convergence to the global optimum, is
developed for these new models. The underlying presentation
of the new framework and optimization approach is
complemented with experimental examples and preliminary
Doi = {10.1109/CISS.2010.5464845},
Key = {fds265064}
}

@article{fds265077,
Author = {Wright, J and Ma, Y and Mairal, J and Sapiro, G and Huang, TS and Yan,
S},
Title = {Sparse representation for computer vision and pattern
recognition},
Journal = {Proceedings of the Ieee},
Volume = {98},
Number = {6},
Pages = {1031-1044},
Publisher = {Institute of Electrical and Electronics Engineers
(IEEE)},
Year = {2010},
Month = {June},
ISSN = {0018-9219},
url = {http://dx.doi.org/10.1109/JPROC.2010.2044470},
Abstract = {Techniques from sparse signal representation are beginning
to see significant impact in computer vision, often on
nontraditional applications where the goal is not just to
obtain a compact high-fidelity representation of the
observed signal, but also to extract semantic information.
The choice of dictionary plays a key role in bridging this
gap: unconventional dictionaries consisting of, or learned
from, the training samples themselves provide the key to
obtaining state-of-the-art results and to attaching semantic
meaning to sparse signal representations. Understanding the
good performance of such unconventional dictionaries in turn
demands new algorithmic and analytical techniques. This
review paper highlights a few representative examples of how
the interaction between sparse signal representation and
computer vision can enrich both fields, and raises a number
of open questions for further study. © 2010
IEEE.},
Doi = {10.1109/JPROC.2010.2044470},
Key = {fds265077}
}

@article{fds265075,
Author = {Lecumberry, F and Pardo, A and Sapiro, G},
Title = {Simultaneous object classification and segmentation with
high-order multiple shape models.},
Journal = {Ieee Transactions on Image Processing : a Publication of the
Ieee Signal Processing Society},
Volume = {19},
Number = {3},
Pages = {625-635},
Year = {2010},
Month = {March},
url = {http://www.ncbi.nlm.nih.gov/pubmed/20028636},
Abstract = {Shape models (SMs), capturing the common features of a set
of training shapes, represent a new incoming object based on
its projection onto the corresponding model. Given a set of
learned SMs representing different objects classes, and an
image with a new shape, this work introduces a joint
classification-segmentation framework with a twofold goal.
First, to automatically select the SM that best represents
the object, and second, to accurately segment the image
taking into account both the image information and the
features and variations learned from the online selected
model. A new energy functional is introduced that
simultaneously accomplishes both goals. Model selection is
performed based on a shape similarity measure, online
determining which model to use at each iteration of the
steepest descent minimization, allowing for model switching
and adaptation to the data. High-order SMs are used in order
to deal with very similar object classes and natural
variability within them. Position and transformation
invariance is included as part of the modeling as well. The
presentation of the framework is complemented with examples
for the difficult task of simultaneously classifying and
segmenting closely related shapes, such as stages of human
activities, in images with severe occlusions.},
Doi = {10.1109/tip.2009.2038759},
Key = {fds265075}
}

@article{fds265063,
Author = {Mairal, J and Bach, F and Ponce, J and Sapiro, G},
Title = {Online learning for matrix factorization and sparse
coding},
Journal = {Journal of Machine Learning Research},
Volume = {11},
Pages = {19-60},
Year = {2010},
Month = {February},
ISSN = {1532-4435},
Abstract = {Sparse coding-that is, modelling data vectors as sparse
linear combinations of basis elements-is widely used in
machine learning, neuroscience, signal processing, and
statistics. This paper focuses on the large-scale matrix
factorization problem that consists of learning the basis
set in order to adapt it to specific data. Variations of
this problem include dictionary learning in signal
processing, non-negative matrix factorization and sparse
principal component analysis. In this paper, we propose to
algorithm, based on stochastic approximations, which scales
up gracefully to large data sets with millions of training
samples, and extends naturally to various matrix
factorization formulations, making it suitable for a wide
range of learning problems. A proof of convergence is
presented, along with experiments with natural images and
genomic data demonstrating that it leads to state-of-the-art
performance in terms of speed and optimization for both
small and large data sets. © 2010 Julien Mairal, Francis
Bach, Jean Ponce and Guillermo Sapiro.},
Key = {fds265063}
}

@article{fds264795,
Author = {Sapiro, G and Kaniuk, Y},
Title = {THE LAST JEW},
Journal = {Quinzaine Litteraire},
Number = {1008},
Pages = {12-13},
Year = {2010},
Month = {February},
ISSN = {0048-6493},
Key = {fds264795}
}

@article{fds265076,
Author = {White, T and Su, S and Schmidt, M and Kao, C-Y and Sapiro,
G},
Title = {The development of gyrification in childhood and
Journal = {Brain and Cognition},
Volume = {72},
Number = {1},
Pages = {36-45},
Year = {2010},
Month = {February},
url = {http://www.ncbi.nlm.nih.gov/pubmed/19942335},
Abstract = {Gyrification is the process by which the brain undergoes
changes in surface morphology to create sulcal and gyral
regions. The period of greatest development of brain
gyrification is during the third trimester of pregnancy, a
period of time in which the brain undergoes considerable
growth. Little is known about changes in gyrification during
childhood and adolescence, although considering the changes
in gray matter volume and thickness during this time period,
it is conceivable that alterations in the brain surface
morphology could also occur during this period of
development. The formation of gyri and sulci in the brain
allows for compact wiring that promotes and enhances
efficient neural processing. If cerebral function and form
are linked through the organization of neural connectivity,
then alterations in neural connectivity, i.e., synaptic
pruning, may also alter the gyral and sulcal patterns of the
brain. This paper reviews developmental theories of
gyrification, computational techniques for measuring
gyrification, and the potential interaction between
gyrification and neuronal connectivity. We also present
recent findings involving alterations in gyrification during
Doi = {10.1016/j.bandc.2009.10.009},
Key = {fds265076}
}

@article{fds264819,
Author = {Shema-Didi, L and Sela, S and Geron, R and Sapiro, G and Ore, L and Kristal, B},
Title = {The Beneficial Effects of One Year Pomegranate Juice
for Cardiovascular Diseases},
Journal = {Free Radical Biology and Medicine},
Volume = {49},
Pages = {S198-S198},
Publisher = {Elsevier BV},
Year = {2010},
Month = {January},
ISSN = {0891-5849},
Key = {fds264819}
}

@article{fds264869,
Author = {Aganj, I and Lenglet, C and Sapiro, G},
Title = {ODF maxima extraction in spherical harmonic representation
via analytical search space reduction.},
Journal = {Medical Image Computing and Computer Assisted Intervention :
Miccai ... International Conference on Medical Image
Computing and Computer Assisted Intervention},
Volume = {13},
Number = {Pt 2},
Pages = {84-91},
Year = {2010},
Month = {January},
url = {http://www.ncbi.nlm.nih.gov/pubmed/20879302},
Abstract = {By revealing complex fiber structure through the orientation
distribution function (ODF), q-ball imaging has recently
become a popular reconstruction technique in
diffusion-weighted MRI. In this paper, we propose an
analytical dimension reduction approach to ODF maxima
extraction. We show that by expressing the ODF, or any
antipodally symmetric spherical function, in the common
fourth order real and symmetric spherical harmonic basis,
the maxima of the two-dimensional ODF lie on an analytically
derived one-dimensional space, from which we can detect the
ODF maxima. This method reduces the computational complexity
of the maxima detection, without compromising the accuracy.
We demonstrate the performance of our technique on both
artificial and human brain data.},
Key = {fds264869}
}

@article{fds265074,
Author = {Fiori, M and Musé, P and Aguirre, S and Sapiro, G},
Title = {Automatic colon polyp flagging via geometric and texture
features.},
Journal = {Conference Proceedings : ... Annual International Conference
of the Ieee Engineering in Medicine and Biology Society.
Ieee Engineering in Medicine and Biology Society. Annual
Conference},
Volume = {2010},
Pages = {3170-3173},
Year = {2010},
Month = {January},
ISSN = {1557-170X},
url = {http://www.ncbi.nlm.nih.gov/pubmed/21096596},
Abstract = {Computer Tomographic Colonography, combined with
computer-aided detection (CAD), is a promising emerging
technique for colonic polyp analysis. We present a CAD
scheme for polyp flagging based on new texture and geometric
features that consider both the information in the candidate
polyp location and its immediate surrounding area, testing
multiple sizes. The proposed algorithm is tested with ground
truth data, including flat and small polyps, with very
promising results.},
Doi = {10.1109/iembs.2010.5627185},
Key = {fds265074}
}

@article{fds264713,
Author = {Fiori, M and Musé, P and Aguirre, S and Sapiro, G},
Title = {Automatic colon polyp flagging via geometric and texture
features.},
Journal = {Conference Proceedings : ... Annual International Conference
of the Ieee Engineering in Medicine and Biology Society.
Ieee Engineering in Medicine and Biology Society. Annual
Conference},
Pages = {3170-3173},
Year = {2010},
ISSN = {1557-170X},
url = {http://dx.doi.org/10.1109/IEMBS.2010.5627185},
Abstract = {Computer Tomographic Colonography, combined with
computer-aided detection (CAD), is a promising emerging
technique for colonic polyp analysis. We present a CAD
scheme for polyp flagging based on new texture and geometric
features that consider both the information in the candidate
polyp location and its immediate surrounding area, testing
multiple sizes. The proposed algorithm is tested with ground
truth data, including flat and small polyps, with very
promising results.},
Doi = {10.1109/IEMBS.2010.5627185},
Key = {fds264713}
}

@article{fds264790,
Author = {Sapiro, G},
Title = {Punish the violence of words: the French intellectual
process at the end of World War II},
Journal = {Esprit Createur},
Volume = {50},
Number = {4},
Pages = {4-19},
Year = {2010},
ISSN = {0014-0767},
Key = {fds264790}
}

@article{fds264846,
Author = {Sprechmann, P and Ramírez, I and Sapiro, G and Eldar,
YC},
Title = {Collaborative hierarchical sparse modeling.},
Journal = {Ciss},
Pages = {1-6},
Publisher = {IEEE},
Year = {2010},
ISBN = {978-1-4244-7416-5},
url = {http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5456462},
Doi = {10.1109/CISS.2010.5464845},
Key = {fds264846}
}

@article{fds264857,
Author = {Mairal, J and Bach, FR and Ponce, J and Sapiro, G},
Title = {Online Learning for Matrix Factorization and Sparse
Coding.},
Journal = {Journal of Machine Learning Research},
Volume = {11},
Pages = {19-60},
Year = {2010},
url = {http://dx.doi.org/10.1145/1756006.1756008},
Doi = {10.1145/1756006.1756008},
Key = {fds264857}
}

@article{fds264861,
Author = {Ramírez, I and Sapiro, G},
Title = {Sparse coding and dictionary learning based on the MDL
principle},
Journal = {Corr},
Volume = {abs/1010.4751},
Year = {2010},
Key = {fds264861}
}

@article{fds264862,
Author = {Ramírez, I and Sapiro, G},
Title = {Universal Sparse Modeling},
Journal = {Corr},
Volume = {abs/1003.2941},
Year = {2010},
Key = {fds264862}
}

@article{fds264863,
Author = {Sapiro, G},
Title = {Introduction to the Special Section on Optimization in
Imaging Sciences.},
Journal = {Siam J. Imaging Sciences},
Volume = {3},
Pages = {1047-1047},
Year = {2010},
Key = {fds264863}
}

@article{fds265065,
Author = {Passalacqua, P and Trung, TD and Foufoula-Georgiou, E and Sapiro, G and Dietrich, WE},
Title = {A geometric framework for channel network extraction from
lidar: Nonlinear diffusion and geodesic paths},
Journal = {Journal of Geophysical Research: Earth Surface},
Volume = {115},
Number = {1},
Publisher = {American Geophysical Union (AGU)},
Year = {2010},
ISSN = {2169-9011},
url = {http://dx.doi.org/10.1029/2009jf001254},
Abstract = {[1] A geometric framework for the automatic extraction of
channels and channel networks from high-resolution digital
elevation data is introduced in this paper. The proposed
approach incorporates nonlinear diffusion for the
preprocessing of the data, both to remove noise and to
enhance features that are critical to the network
extraction. Following this preprocessing, channels are
defined as curves of minimal effort, or geodesies, where the
effort is measured on the basis of fundamental
geomorphological characteristics such as flow accumulation
area and isoheight contours curvature. The merits of the
proposed methodology, and especially the computational
efficiency and accurate localization of the extracted
channels, are demonstrated using light detection and ranging
(lidar) data of the Skunk Creek, a tributary of the South
Fork Eel River basin in northern California. Copyright 2010
by the American Geophysical Union.},
Doi = {10.1029/2009jf001254},
Key = {fds265065}
}

@article{fds265072,
Author = {Fiori, M and Musé, P and Aguirre, S and Sapiro, G},
Title = {Automatic colon polyp flagging via geometric and texture
features},
Journal = {2010 Annual International Conference of the Ieee Engineering
in Medicine and Biology Society, Embc'10},
Pages = {3170-3173},
Year = {2010},
url = {http://dx.doi.org/10.1109/IEMBS.2010.5627185},
Abstract = {Computer Tomographic Colonography, combined with
computer-aided detection (CAD), is a promising emerging
technique for colonic polyp analysis. We present a CAD
scheme for polyp flagging based on new texture and geometric
features that consider both the information in the candidate
polyp location and its immediate surrounding area, testing
multiple sizes. The proposed algorithm is tested with ground
truth data, including flat and small polyps, with very
Doi = {10.1109/IEMBS.2010.5627185},
Key = {fds265072}
}

@article{fds265047,
Author = {Szlam, A and Sapiro, G},
Title = {Discriminative k-metrics},
Journal = {Proceedings of the 26th International Conference on Machine
Learning, Icml 2009},
Pages = {1009-1016},
Year = {2009},
Month = {December},
Abstract = {The k q-flats algorithm is a generalization of the popular
k-means algorithm where q dimensional best fit affine sets
replace centroids as the cluster prototypes. In this work, a
modification of the k q-flats framework for pattern
classification is introduced. The basic idea is to replace
the original reconstruction only energy, which is optimized
to obtain the k affine spaces, by a new energy that
incorporates discriminative terms. This way, the actual
classification task is introduced as part of the design and
optimization. The presentation of the proposed framework is
complemented with experimental results, showing that the
method is computationally very efficient and gives excellent
results on standard supervised learning benchmarks.},
Key = {fds265047}
}

@article{fds265051,
Author = {Aganj, I and Lenglet, C and Sapiro, G and Yacoub, E and Ugurbil, K and Harel, N},
Title = {Multiple Q-shell ODF reconstruction in Q-ball
imaging},
Journal = {Lecture Notes in Computer Science (Including Subseries
Lecture Notes in Artificial Intelligence and Lecture Notes
in Bioinformatics)},
Volume = {5762 LNCS},
Number = {PART 2},
Pages = {423-431},
Publisher = {Springer Berlin Heidelberg},
Year = {2009},
Month = {December},
ISSN = {0302-9743},
url = {http://dx.doi.org/10.1007/978-3-642-04271-3_52},
Abstract = {Q-ball imaging (QBI) is a high angular resolution diffusion
imaging (HARDI) technique which has been proven very
successful in resolving multiple intravoxel fiber
orientations in MR images. The standard computation of the
orientation distribution function (ODF, the probability of
diffusion in a given direction) from q-ball uses linear
radial projection, neglecting the change in the volume
element along the ray, thereby resulting in distributions
different from the true ODFs. A new technique has been
recently proposed that, by considering the solid angle
factor, uses the mathematically correct definition of the
ODF and results in a dimensionless and normalized ODF
expression from a single q-shell. In this paper, we extend
this technique in order to exploit HARDI data from multiple
q-shells. We consider the more flexible multi-exponential
model for the diffusion signal, and show how to efficiently
compute the ODFs in constant solid angle. We describe our
method and demonstrate its improved performance on both
artificial and real HARDI data. © 2009 Springer-Verlag.},
Doi = {10.1007/978-3-642-04271-3_52},
Key = {fds265051}
}

@article{fds265052,
Author = {Raḿrez, I and Lecumberry, F and Sapiro, G},
Title = {Universal priors for sparse modeling},
Journal = {Camsap 2009 2009 3rd Ieee International Workshop on
Processing},
Pages = {197-200},
Publisher = {IEEE},
Year = {2009},
Month = {December},
url = {http://dx.doi.org/10.1109/CAMSAP.2009.5413302},
Abstract = {Sparse data models, where data is assumed to be well
represented as a linear combination of a few elements from a
dictionary, have gained considerable attention in recent
years, and their use has led to state-of-the-art results in
many signal and image processing tasks. It is now well
understood that the choice of the sparsity regularization
term is critical in the success of such models. In this
work, we use tools from information theory to propose a
sparsity regularization term which has several theoretical
and practical advantages over the more standard ℓ0 or ℓ1
ones, and which leads to improved coding performance and
accuracy in reconstruction tasks. We also briefly report on
further improvements obtained by imposing low mutual
coherence and Gram matrix norm on the learned dictionaries.
Doi = {10.1109/CAMSAP.2009.5413302},
Key = {fds265052}
}

@article{fds265054,
Author = {Mairal, J and Bach, F and Ponce, J and Sapiro, G and Zisserman,
A},
Title = {Supervised dictionary learning},
Journal = {Advances in Neural Information Processing Systems 21
Proceedings of the 2008 Conference},
Pages = {1033-1040},
Year = {2009},
Month = {December},
Abstract = {It is now well established that sparse signal models are
well suited for restoration tasks and can be effectively
learned from audio, image, and video data. Recent research
has been aimed at learning discriminative sparse models
instead of purely reconstructive ones. This paper proposes a
new step in that direction, with a novel sparse
representation for signals belonging to different classes in
terms of a shared dictionary and discriminative class
models. The linear version of the proposed model admits a
simple probabilistic interpretation, while its most general
variant admits an interpretation in terms of kernels. An
optimization framework for learning all the components of
the proposed model is presented, along with experimental
results on standard handwritten digit and texture
Key = {fds265054}
}

@article{fds265055,
Author = {Mairal, J and Bach, F and Ponce, J and Sapiro, G and Zisserman,
A},
Title = {Non-local sparse models for image restoration},
Journal = {Proceedings of the Ieee International Conference on Computer
Vision},
Pages = {2272-2279},
Publisher = {IEEE},
Year = {2009},
Month = {December},
url = {http://dx.doi.org/10.1109/ICCV.2009.5459452},
Abstract = {We propose in this paper to unify two different approaches
to image restoration: On the one hand, learning a basis set
(dictionary) adapted to sparse signal descriptions has
proven to be very effective in image reconstruction and
classification tasks. On the other hand, explicitly
exploiting the self-similarities of natural images has led
to the successful non-local means approach to image
restoration. We propose simultaneous sparse coding as a
framework for combining these two approaches in a natural
manner. This is achieved by jointly decomposing groups of
similar signals on subsets of the learned dictionary.
Experimental results in image denoising and demosaicking
tasks with synthetic and real noise show that the proposed
method outperforms the state of the art, making it possible
to effectively restore raw images from digital cameras at a
reasonable speed and memory cost. ©2009
IEEE.},
Doi = {10.1109/ICCV.2009.5459452},
Key = {fds265055}
}

@article{fds265124,
Author = {Zhou, M and Chen, H and Paisley, J and Ren, L and Sapiro, G and Carin,
L},
Title = {Non-parametric Bayesian dictionary learning for sparse image
representations},
Journal = {Advances in Neural Information Processing Systems 22
Proceedings of the 2009 Conference},
Pages = {2295-2303},
Year = {2009},
Month = {December},
Abstract = {Non-parametric Bayesian techniques are considered for
learning dictionaries for sparse image representations, with
applications in denoising, inpainting and com-pressive
sensing (CS). The beta process is employed as a prior for
learning the dictionary, and this non-parametric method
naturally infers an appropriate dictionary size. The
Dirichlet process and a probit stick-breaking process are
also considered to exploit structure within an image. The
proposed method can learn a sparse dictionary in situ;
training images may be exploited if available, but they are
not required. Further, the noise variance need not be known,
and can be non-stationary. Another virtue of the proposed
method is that sequential inference can be readily employed,
thereby allowing scaling to large images. Several example
results are presented, using both Gibbs and variational
Bayesian inference, with comparisons to other
state-of-the-art approaches.},
Key = {fds265124}
}

@article{fds265049,
Author = {Szlam, A and Sapiro, G},
Title = {Discriminative k metrics and the Chan-Vese model for object
detection and segmentation},
Journal = {Smart Structures and Materials 2005: Active Materials:
Behavior and Mechanics},
Volume = {7446},
Publisher = {SPIE},
Year = {2009},
Month = {November},
ISSN = {0277-786X},
url = {http://dx.doi.org/10.1117/12.825800},
Abstract = {In this work, a modification of the k q-flats framework for
pattern classification introduced in [9] is used for
pixelwise object detection. We include a preliminary
discussion of augmenting this method is with a
SPIE.},
Doi = {10.1117/12.825800},
Key = {fds265049}
}

@article{fds265045,
Author = {Jahanshad, N and Lee, AD and Lepore, N and Chou, YY and Brun, C and Barysheva, M and Toga, AW and McMahon, KL and Zubicaray, GID and Wright,
MJ and Sapiro, G and Lenglet, C and Thompson, PM},
Title = {Reducing structural variation to determine the genetics of
white matter integrity across hemispheres - a dti study of
100 twins},
Journal = {Proceedings 2009 Ieee International Symposium on Biomedical
Imaging: From Nano to Macro, Isbi 2009},
Pages = {819-822},
Publisher = {IEEE},
Year = {2009},
Month = {November},
url = {http://dx.doi.org/10.1109/ISBI.2009.5193175},
Abstract = {Studies of cerebral asymmetry can open doors to
understanding the functional specialization of each brain
hemisphere, and how this is altered in disease. Here we
examined hemispheric asymmetries in fiber architecture using
diffusion tensor imaging (DTI) in 100 subjects, using
high-dimensional fluid warping to disentangle shape
differences from measures sensitive to myelination.
Confounding effects of purely structural asymmetries were
reduced by using co-registered structural images to fluidly
warp 3D maps of fiber characteristics (fractional and
geodesic anisotropy) to a structurally symmetric minimal
deformation template (MDT). We performed a quantitative
genetic analysis on 100 subjects to determine whether the
sources of the remaining signal asymmetries were primarily
genetic or environmental. A twin design was used to identify
the heritable features of fiber asymmetry in various regions
of interest, to further assist in the discovery of genes
influencing brain micro-architecture and brain
lateralization. Genetic influences and left/right
asymmetries were detected in the fiber architecture of the
frontal lobes, with minor differences depending on the
choice of registration template. © 2009
IEEE.},
Doi = {10.1109/ISBI.2009.5193175},
Key = {fds265045}
}

@article{fds265048,
Author = {Aganj, I and Lenglet, C and Sapiro, G},
Title = {ODF reconstruction in Q-ball imaging with solid angle
consideration},
Journal = {Proceedings 2009 Ieee International Symposium on Biomedical
Imaging: From Nano to Macro, Isbi 2009},
Pages = {1398-1401},
Publisher = {IEEE},
Year = {2009},
Month = {November},
url = {http://dx.doi.org/10.1109/ISBI.2009.5193327},
Abstract = {Q-ball imaging (QBI) is a high angular resolution diffusion
imaging (HARDI) technique which has been proven very
successful in resolving multiple intravoxel fiber
orientations in MR images. The standard computation of the
orientation distribution function (ODF, the probability of
diffusion in a given direction) from q-ball uses linear
radial projection, neglecting the change in the volume
element along the ray, thereby resulting in distributions
different from the true ODFs. For instance, they are not
normalized or as sharp as expected, and generally require
post-processing, such as sharpening or spherical
deconvolution. In this paper, we consider the mathematically
correct definition of the ODF and derive a closed-form
expression for it in QBI. The derived ODF is dimensionless
and normalized, and can be efficiently computed from q-ball
acquisition protocols. We describe our proposed method and
demonstrate its significantly improved performance on
artificial data and real HARDI volumes. © 2009
IEEE.},
Doi = {10.1109/ISBI.2009.5193327},
Key = {fds265048}
}

@article{fds265043,
Author = {Arias, P and Caselles, V and Sapiro, G},
Title = {A variational framework for non-local image
inpainting},
Journal = {Lecture Notes in Computer Science (Including Subseries
Lecture Notes in Artificial Intelligence and Lecture Notes
in Bioinformatics)},
Volume = {5681 LNCS},
Pages = {345-358},
Publisher = {Springer Berlin Heidelberg},
Year = {2009},
Month = {November},
ISSN = {0302-9743},
url = {http://dx.doi.org/10.1007/978-3-642-03641-5_26},
Abstract = {Non-local methods for image denoising and inpainting have
gained considerable attention in recent years. This is in
part due to their superior performance in textured images, a
known weakness of purely local methods. Local methods on the
other hand have demonstrated to be very appropriate for the
recovering of geometric structure such as image edges. The
synthesis of both types of methods is a trend in current
research. Variational analysis in particular is an
appropriate tool for a unified treatment of local and
non-local methods. In this work we propose a general
variational framework for the problem of non-local image
inpainting, from which several previous inpainting schemes
explicitly study some of these, relating them to previous
work and showing results on synthetic and real images. ©
2009 Springer.},
Doi = {10.1007/978-3-642-03641-5_26},
Key = {fds265043}
}

@article{fds265044,
Author = {Wirth, B and Bar, L and Rumpf, M and Sapiro, G},
Title = {Geodesics in shape space via variational time
discretization},
Journal = {Lecture Notes in Computer Science (Including Subseries
Lecture Notes in Artificial Intelligence and Lecture Notes
in Bioinformatics)},
Volume = {5681 LNCS},
Pages = {288-302},
Publisher = {Springer Berlin Heidelberg},
Year = {2009},
Month = {November},
ISSN = {0302-9743},
url = {http://dx.doi.org/10.1007/978-3-642-03641-5_22},
Abstract = {A variational approach to defining geodesics in the space of
implicitly described shapes is introduced in this paper. The
proposed framework is based on the time discretization of a
geodesic path as a sequence of pairwise matching problems,
which is strictly invariant with respect to rigid body
motions and ensures a 1-1 property of the induced flow in
shape space. For decreasing time step size, the proposed
model leads to the minimization of the actual geodesic
length, where the Hessian of the pairwise matching energy
reflects the chosen Riemannian metric on the shape space.
Considering shapes as boundary contours, the proposed shape
metric is identical to a physical dissipation in a viscous
fluid model of optimal transportation. If the pairwise shape
correspondence is replaced by the volume of the shape
mismatch as a penalty functional, for decreasing time step
size one obtains an additional optical flow term controlling
the transport of the shape by the underlying motion field.
The implementation of the proposed approach is based on a
level set representation of shapes, which allows topological
transitions along the geodesic path. For the spatial
discretization a finite element approximation is employed
both for the pairwise deformations and for the level set
representation. The numerical relaxation of the energy is
performed via an efficient multi-scale procedure in space
and time. Examples for 2D and 3D shapes underline the
effectiveness and robustness of the proposed approach. ©
2009 Springer.},
Doi = {10.1007/978-3-642-03641-5_22},
Key = {fds265044}
}

@article{fds265046,
Author = {Facciolo, G and Arias, P and Caselles, V and Sapiro,
G},
Title = {Exemplar-based interpolation of sparsely sampled
images},
Journal = {Lecture Notes in Computer Science (Including Subseries
Lecture Notes in Artificial Intelligence and Lecture Notes
in Bioinformatics)},
Volume = {5681 LNCS},
Pages = {331-344},
Publisher = {Springer Berlin Heidelberg},
Year = {2009},
Month = {November},
ISSN = {0302-9743},
url = {http://dx.doi.org/10.1007/978-3-642-03641-5_25},
Abstract = {A nonlocal variational formulation for interpolating a
sparsely sampled image is introduced in this paper. The
proposed variational formulation, originally motivated by
image inpainting problems, encourages the transfer of
information between similar image patches, following the
paradigm of exemplar-based methods. Contrary to the
classical inpainting problem, no complete patches are
available from the sparse image samples, and the patch
similarity criterion has to be redefined as here proposed.
Initial experimental results with the proposed framework, at
very low sampling densities, are very encouraging. We also
explore some departures from the variational setting,
showing a remarkable ability to recover textures at low
Doi = {10.1007/978-3-642-03641-5_25},
Key = {fds265046}
}

@article{fds265062,
Author = {Aganj, I and Sapiro, G and Parikshak, N and Madsen, SK and Thompson,
PM},
Title = {Measurement of cortical thickness from MRI by minimum line
integrals on soft-classified tissue.},
Journal = {Human Brain Mapping},
Volume = {30},
Number = {10},
Pages = {3188-3199},
Year = {2009},
Month = {October},
url = {http://www.ncbi.nlm.nih.gov/pubmed/19219850},
Abstract = {Estimating the thickness of the cerebral cortex is a key
step in many brain imaging studies, revealing valuable
information on development or disease progression. In this
work, we present a framework for measuring the cortical
thickness, based on minimizing line integrals over the
probability map of the gray matter in the MRI volume. We
first prepare a probability map that contains the
probability of each voxel belonging to the gray matter.
Then, the thickness is basically defined for each voxel as
the minimum line integral of the probability map on line
segments centered at the point of interest. In contrast to
our approach, previous methods often perform a binary-valued
hard segmentation of the gray matter before measuring the
cortical thickness. Because of image noise and partial
volume effects, such a hard classification ignores the
underlying tissue class probabilities assigned to each
voxel, discarding potentially useful information. We
describe our proposed method and demonstrate its performance
on both artificial volumes and real 3D brain MRI data from
subjects with Alzheimer's disease and healthy
individuals.},
Doi = {10.1002/hbm.20740},
Key = {fds265062}
}

@article{fds265041,
Author = {Szlam, A and Sapiro, G},
Title = {Discriminative k-metrics},
Journal = {Acm International Conference Proceeding Series},
Volume = {382},
Publisher = {ACM Press},
Year = {2009},
Month = {September},
url = {http://dx.doi.org/10.1145/1553374.1553503},
Abstract = {The k q-flats algorithm is a generalization of the popular
k-means algorithm where q dimensional best fit affine sets
replace centroids as the cluster prototypes. In this work, a
modification of the k q-flats framework for pattern
classification is introduced. The basic idea is to replace
the original reconstruction only energy, which is optimized
to obtain the k affine spaces, by a new energy that
incorporates discriminative terms. This way, the actual
classification task is introduced as part of the design and
optimization. The presentation of the proposed framework is
complemented with experimental results, showing that the
method is computationally very efficient and gives excellent
results on standard supervised learning benchmarks.
Doi = {10.1145/1553374.1553503},
Key = {fds265041}
}

@article{fds265042,
Author = {Mairal, J and Bach, F and Ponce, J and Sapiro, G},
Title = {Online dictionary learning for sparse coding},
Journal = {Acm International Conference Proceeding Series},
Volume = {382},
Publisher = {ACM Press},
Year = {2009},
Month = {September},
url = {http://dx.doi.org/10.1145/1553374.1553463},
Abstract = {Sparse coding - that is, modelling data vectors as sparse
linear combinations of basis elements - is widely used in
machine learning, neuroscience, signal processing, and
statistics. This paper focuses on learning the basis set,
also called dictionary, to adapt it to specific data, an
approach that has recently proven to be very effective for
signal reconstruction and classification in the audio and
image processing domains. This paper proposes a new online
optimization algorithm for dictionary learning, based on
stochastic approximations, which scales up gracefully to
large datasets with millions of training samples. A proof of
convergence is presented, along with experiments with
natural images demonstrating that it leads to faster
performance and better dictionaries than classical batch
algorithms for both small and large datasets. Copyright
2009.},
Doi = {10.1145/1553374.1553463},
Key = {fds265042}
}

@article{fds264859,
Author = {Rother, D and Sapiro, G},
Title = {Seeing 3D objects in a single 2D image},
Journal = {2009 Ieee 12th International Conference on Computer
Vision},
Pages = {1819-1826},
Publisher = {IEEE},
Year = {2009},
Month = {September},
ISBN = {9781424444205},
url = {http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5453389},
Doi = {10.1109/iccv.2009.5459405},
Key = {fds264859}
}

@article{fds265061,
Author = {Sundaramoorthi, G and Yezzi, A and Mennucci, AC and Sapiro,
G},
Title = {New possibilities with Sobolev active contours},
Journal = {International Journal of Computer Vision},
Volume = {84},
Number = {2},
Pages = {113-129},
Publisher = {Springer Nature},
Year = {2009},
Month = {August},
ISSN = {0920-5691},
url = {http://dx.doi.org/10.1007/s11263-008-0133-9},
Abstract = {Recently, the Sobolev metric was introduced to define
gradient flows of various geometric active contour energies.
It was shown that the Sobolev metric outperforms the
traditional metric for the same energy in many cases such as
for tracking where the coarse scale changes of the contour
are important. Some interesting properties of Sobolev
gradient flows include that they stabilize certain unstable
traditional flows, and the order of the evolution PDEs are
same energies. In this paper, we explore new possibilities
for active contours made possible by Sobolev metrics. The
Sobolev method allows one to implement new energy-based
active contour models that were not otherwise considered
because the traditional minimizing method render them
ill-posed or numerically infeasible. In particular, we
exploit the stabilizing and the order reducing properties of
new energies. We give examples of this class of energies,
which include some simple geometric priors and new
edge-based energies. We also show that these energies can be
quite useful for segmentation and tracking. We also show
either ill-posed or numerically difficult to implement, and
then show that the flows can be implemented in a stable and
Doi = {10.1007/s11263-008-0133-9},
Key = {fds265061}
}

@article{fds265060,
Author = {Bai, X and Wang, J and Simons, D and Sapiro, G},
Title = {Video SnapCut: Robust video object cutout using localized
classifiers},
Journal = {Acm Transactions on Graphics},
Volume = {28},
Number = {3},
Pages = {1-1},
Publisher = {Association for Computing Machinery (ACM)},
Year = {2009},
Month = {July},
ISSN = {0730-0301},
url = {http://dx.doi.org/10.1145/1531326.1531376},
Abstract = {Although tremendous success has been achieved for
interactive object cutout in still images, accurately
extracting dynamic objects in video remains a very
challenging problem. Previous video cutout systems present
two major limitations: (1) reliance on global statistics,
thus lacking the ability to deal with complex and diverse
scenes; and (2) treating segmentation as a global
optimization, thus lacking a practical workflow that can
guarantee the convergence of the systems to the desired
results. We present Video SnapCut, a robust video object
cutout system that significantly advances the
state-of-the-art. In our system segmentation is achieved by
the collaboration of a set of local classifiers, each
adaptively integrating multiple local image features. We
show how this segmentation paradigm naturally supports local
user editing and propagates them across time. The object
cutout system is completed with a novel coherent video
matting technique. A comprehensive evaluation and comparison
is presented, demonstrating the effectiveness of the
proposed system at achieving high quality results, as well
as the robustness of the system against various types of
Doi = {10.1145/1531326.1531376},
Key = {fds265060}
}

@article{fds265040,
Author = {Duarte-Carvajalino, JM and Sapiro, G},
Title = {Learning to sense sparse signals: simultaneous sensing
matrix and sparsifying dictionary optimization.},
Journal = {Ieee Transactions on Image Processing : a Publication of the
Ieee Signal Processing Society},
Volume = {18},
Number = {7},
Pages = {1395-1408},
Year = {2009},
Month = {July},
ISSN = {1057-7149},
url = {http://www.ncbi.nlm.nih.gov/pubmed/19497818},
Abstract = {Sparse signal representation, analysis, and sensing have
received a lot of attention in recent years from the signal
processing, optimization, and learning communities. On one
hand, learning overcomplete dictionaries that facilitate a
sparse representation of the data as a liner combination of
a few atoms from such dictionary leads to state-of-the-art
results in image and video restoration and classification.
On the other hand, the framework of compressed sensing (CS)
has shown that sparse signals can be recovered from far less
samples than those required by the classical Shannon-Nyquist
Theorem. The samples used in CS correspond to linear
projections obtained by a sensing projection matrix. It has
been shown that, for example, a nonadaptive random sampling
matrix satisfies the fundamental theoretical requirements of
CS, enjoying the additional benefit of universality. On the
other hand, a projection sensing matrix that is optimally
designed for a certain class of signals can further improve
the reconstruction accuracy or further reduce the necessary
number of samples. In this paper, we introduce a framework
for the joint design and optimization, from a set of
training images, of the nonparametric dictionary and the
sensing matrix. We show that this joint optimization
outperforms both the use of random sensing matrices and
those matrices that are optimized independently of the
learning of the dictionary. Particular cases of the proposed
framework include the optimization of the sensing matrix for
a given dictionary as well as the optimization of the
dictionary for a predefined sensing environment. The
presentation of the framework and its efficient numerical
optimization is complemented with numerous examples on
classical image datasets.},
Doi = {10.1109/tip.2009.2022459},
Key = {fds265040}
}

@article{fds265059,
Author = {Bai, X and Sapiro, G},
Title = {Geodesic matting: A framework for fast interactive image and
video segmentation and matting},
Journal = {International Journal of Computer Vision},
Volume = {82},
Number = {2},
Pages = {113-132},
Publisher = {Springer Nature},
Year = {2009},
Month = {April},
ISSN = {0920-5691},
url = {http://dx.doi.org/10.1007/s11263-008-0191-z},
Abstract = {An interactive framework for soft segmentation and matting
of natural images and videos is presented in this paper. The
proposed technique is based on the optimal, linear time,
computation of weighted geodesic distances to user-provided
scribbles, from which the whole data is automatically
segmented. The weights are based on spatial and/or temporal
gradients, considering the statistics of the pixels
scribbled by the user, without explicit optical flow or any
advanced and often computationally expensive feature
detectors. These could be naturally added to the proposed
framework as well if desired, in the form of weights in the
geodesic distances. An automatic localized refinement step
follows this fast segmentation in order to further improve
the results and accurately compute the corresponding matte
function. Additional constraints into the distance
definition permit to efficiently handle occlusions such as
people or objects crossing each other in a video sequence.
The presentation of the framework is complemented with
numerous and diverse examples, including extraction of
moving foreground from dynamic background in video, natural
and 3D medical images, and comparisons with the recent
literature.},
Doi = {10.1007/s11263-008-0191-z},
Key = {fds265059}
}

@article{fds264752,
Author = {Matonti, F and Sapiro, G},
Title = {The commitment of intellectuals: new viewpoints},
Journal = {Actes De La Recherche En Sciences Sociales},
Number = {176-77},
Pages = {4-7},
Year = {2009},
Month = {March},
ISSN = {0335-5322},
Key = {fds264752}
}

@article{fds264782,
Author = {Sapiro, G},
Title = {Intellectuals and politics: A Typology},
Journal = {Actes De La Recherche En Sciences Sociales},
Number = {176-77},
Pages = {8-+},
Year = {2009},
Month = {March},
ISSN = {0335-5322},
Key = {fds264782}
}

@article{fds265056,
Author = {Pollick, FE and Maoz, U and Handzel, AA and Giblin, PJ and Sapiro, G and Flash, T},
Title = {Three-dimensional arm movements at constant equi-affine
speed.},
Journal = {Cortex; a Journal Devoted to the Study of the Nervous System
and Behavior},
Volume = {45},
Number = {3},
Pages = {325-339},
Year = {2009},
Month = {March},
ISSN = {0010-9452},
url = {http://www.ncbi.nlm.nih.gov/pubmed/18678364},
Abstract = {It has long been acknowledged that planar hand drawing
movements conform to a relationship between movement speed
and shape, such that movement speed is inversely
proportional to the curvature to the power of one-third.
Previous literature has detailed potential explanations for
the power law's existence as well as systematic deviations
from it. However, the case of speed-shape relations for
three-dimensional (3D) drawing movements has remained
largely unstudied. In this paper we first derive a
generalization of the planar power law to 3D movements,
which is based on the principle that this power law implies
motion at constant equi-affine speed. This generalization
results in a 3D power law where speed is inversely related
to the one-third power of the curvature multiplied by the
one-sixth power of the torsion. Next, we present data from
human 3D scribbling movements, and compare the obtained
speed-shape relation to that predicted by the 3D power law.
Our results indicate that the introduction of the torsion
term into the 3D power law accounts for significantly more
of the variance in speed-shape relations of the movement
data and that the obtained exponents are very close to the
predicted values.},
Doi = {10.1016/j.cortex.2008.03.010},
Key = {fds265056}
}

@article{fds265058,
Author = {Lenglet, C and Campbell, JSW and Descoteaux, M and Haro, G and Savadjiev, P and Wassermann, D and Anwander, A and Deriche, R and Pike,
GB and Sapiro, G and Siddiqi, K and Thompson, PM},
Title = {Mathematical methods for diffusion MRI processing.},
Journal = {Neuroimage},
Volume = {45},
Number = {1 Suppl},
Pages = {S111-S122},
Year = {2009},
Month = {March},
url = {http://www.ncbi.nlm.nih.gov/pubmed/19063977},
computational methods for the processing of diffusion
Magnetic Resonance Images, including state-of-the-art
reconstruction of diffusion models, cerebral white matter
connectivity analysis, and segmentation techniques. We focus
on Diffusion Tensor Images (DTI) and Q-Ball Images
(QBI).},
Doi = {10.1016/j.neuroimage.2008.10.054},
Key = {fds265058}
}

@article{fds265057,
Author = {Ivry, T and Michal, S and Avihoo, A and Sapiro, G and Barash,
D},
Title = {An image processing approach to computing distances between
RNA secondary structures dot plots.},
Journal = {Algorithms for Molecular Biology : Amb},
Volume = {4},
Pages = {4},
Year = {2009},
Month = {February},
url = {http://www.ncbi.nlm.nih.gov/pubmed/19203377},
Abstract = {Computing the distance between two RNA secondary structures
can contribute in understanding the functional relationship
between them. When used repeatedly, such a procedure may
lead to finding a query RNA structure of interest in a
database of structures. Several methods are available for
computing distances between RNAs represented as strings or
graphs, but none utilize the RNA representation with dot
plots. Since dot plots are essentially digital images, there
is a clear motivation to devise an algorithm for computing
the distance between dot plots based on image processing
methods.We have developed a new metric dubbed
'DoPloCompare', which compares two RNA structures. The
method is based on comparing dot plot diagrams that
represent the secondary structures. When analyzing two
diagrams and motivated by image processing, the distance is
based on a combination of histogram correlations and a
geometrical distance measure. We introduce, describe, and
illustrate the procedure by two applications that utilize
this metric on RNA sequences. The first application is the
RNA design problem, where the goal is to find the nucleotide
sequence for a given secondary structure. Examples where our
proposed distance measure outperforms others are given. The
second application locates peculiar point mutations that
induce significant structural alternations relative to the
wild type predicted secondary structure. The approach
reported in the past to solve this problem was tested on
several RNA sequences with known secondary structures to
affirm their prediction, as well as on a data set of
ribosomal pieces. These pieces were computationally cut from
a ribosome for which an experimentally derived secondary
structure is available, and on each piece the prediction
conveys similarity to the experimental result. Our newly
proposed distance measure shows benefit in this problem as
well when compared to standard methods used for assessing
the distance similarity between two RNA secondary
structures.Inspired by image processing and the dot plot
representation for RNA secondary structure, we have managed
to provide a conceptually new and potentially beneficial
metric for comparing two RNA secondary structures. We
illustrated our approach on the RNA design problem, as well
as on an application that utilizes the distance measure to
detect conformational rearranging point mutations in an RNA
sequence.},
Doi = {10.1186/1748-7188-4-4},
Key = {fds265057}
}

@article{fds264696,
Author = {Castrodad, A and Ramirez, I and Sapiro, G and Sprechmann, P and Yu,
G},
Title = {Second-generation sparse modeling: Structured and
collaborative signal analysis},
Pages = {65-87},
Publisher = {Cambridge University Press},
Year = {2009},
Month = {January},
url = {http://dx.doi.org/10.1017/CBO9780511794308.003},
Abstract = {© Cambridge University Press 2012. In this chapter the
collaborative structured sparsity to add stability and prior
information to the representation. In structured sparse
modeling, instead of considering the dictionary atoms as
singletons, the atoms are partitioned in groups, and a few
groups are selected at a time for the signal encoding. A
complementary way of adding structure, stability, and prior
information to a model is via collaboration. Here, multiple
signals, which are known to follow the same model, are
allowed to collaborate in the coding. The first studied
framework connects sparse modeling with Gaussian Mixture
Models and leads to state-of-the-art image restoration. The
second framework derives a hierarchical structure on top of
the collaboration and is well fitted for source separation.
Both models enjoy very important theoretical virtues as
well. Introduction In traditional sparse modeling, it is
assumed that a signal can be accurately represented by a
sparse linear combination of atoms from a (learned)
dictionary. A large class of signals, including most natural
images and sounds, is well described by this model, as
demonstrated by numerous state-of-the-art results in various
signal processing applications. From a data modeling point
of view, sparsity can be seen as a form of regularization,
that is, as a device to restrict or control the set of
coefficient values which are allowed in the model to produce
an estimate of the data.},
Doi = {10.1017/CBO9780511794308.003},
Key = {fds264696}
}

@article{fds264865,
Author = {Bar, L and Sapiro, G},
Title = {Generalized newton-type methods for energy formulations in
image processing},
Journal = {Siam Journal on Imaging Sciences},
Volume = {2},
Number = {2},
Pages = {508-531},
Publisher = {Society for Industrial & Applied Mathematics
(SIAM)},
Year = {2009},
Month = {January},
url = {http://dx.doi.org/10.1137/080722436},
Abstract = {© 2009 Society for Industrial and Applied Mathematics. Many
problems in image processing are addressed via the
minimization of a cost functional. The most prominently used
optimization technique is gradient-descent, often used due
to its simplicity and applicability where other techniques,
e.g., those coming from discrete optimization, cannot be
applied. Yet, gradient-descent suffers from slow
convergence, and often to just local minima which highly
depend on the initialization and the condition number of the
functional Hessian. Newton-type methods, on the other hand,
are known to have a faster, quadratic convergence. In its
classical form, the Newton method relies on the L2-type norm
to define the descent direction. In this paper, we
generalize and reformulate this very important optimization
method by introducing Newton-type methods based on more
general norms. Such norms are introduced both in the descent
computation (Newton step) and in the corresponding
stabilizing trust-region. This generalization opens up new
possibilities in the extraction of the Newton step,
including benefits such as mathematical stability and the
incorporation of smoothness constraints. We first present
the derivation of the modified Newton step in the calculus
of variation framework needed for image processing. Then, we
demonstrate the method with two common objective
functionals: variational image deblurring and geometric
active contours for image segmentation. We show that in
problem at hand yield different and superior
results.},
Doi = {10.1137/080722436},
Key = {fds264865}
}

@article{fds264868,
Author = {Aganj, I and Lenglet, C and Sapiro, G and Yacoub, E and Ugurbil, K and Harel, N},
Title = {Multiple Q-shell ODF reconstruction in Q-ball
imaging.},
Journal = {Medical Image Computing and Computer Assisted Intervention :
Miccai ... International Conference on Medical Image
Computing and Computer Assisted Intervention},
Volume = {12},
Number = {Pt 2},
Pages = {423-431},
Year = {2009},
Month = {January},
url = {http://www.ncbi.nlm.nih.gov/pubmed/20426140},
Abstract = {Q-ball imaging (QBI) is a high angular resolution diffusion
imaging (HARDI) technique which has been proven very
successful in resolving multiple intravoxel fiber
orientations in MR images. The standard computation of the
orientation distribution function (ODF, the probability of
diffusion in a given direction) from q-ball uses linear
radial projection, neglecting the change in the volume
element along the ray, thereby resulting in distributions
different from the true ODFs. A new technique has been
recently proposed that, by considering the solid angle
factor, uses the mathematically correct definition of the
ODF and results in a dimensionless and normalized ODF
expression from a single q-shell. In this paper, we extend
this technique in order to exploit HARDI data from multiple
q-shells. We consider the more flexible multi-exponential
model for the diffusion signal, and show how to efficiently
compute the ODFs in constant solid angle. We describe our
method and demonstrate its improved performance on both
artificial and real HARDI data.},
Key = {fds264868}
}

@article{fds265038,
Author = {Mahmoudi, M and Sapiro, G},
Title = {Three-dimensional point cloud recognition via distributions
of geometric distances},
Journal = {Graphical Models},
Volume = {71},
Number = {1},
Pages = {22-31},
Publisher = {Elsevier BV},
Year = {2009},
Month = {January},
ISSN = {1524-0703},
url = {http://dx.doi.org/10.1016/j.gmod.2008.10.002},
Abstract = {A geometric framework for the recognition of
three-dimensional objects represented by point clouds is
introduced in this paper. The proposed approach is based on
comparing distributions of intrinsic measurements on the
point cloud. In particular, intrinsic distances are
exploited as signatures for representing the point clouds.
The first signature we introduce is the histogram of
pairwise diffusion distances between all points on the shape
surface. These distances represent the probability of
traveling from one point to another in a fixed number of
random steps, the average intrinsic distances of all
possible paths of a given number of steps between the two
points. This signature is augmented by the histogram of the
actual pairwise geodesic distances in the point cloud, the
distribution of the ratio between these two distances, as
well as the distribution of the number of times each point
lies on the shortest paths between other points. These
signatures are not only geometric but also invariant to
bends. We further augment these signatures by the
distribution of a curvature function and the distribution of
a curvature weighted distance. These histograms are compared
using the χ 2 or other common distance metrics for
distributions. The presentation of the framework is
accompanied by theoretical and geometric justification and
state-of-the-art experimental results with the standard
Princeton 3D shape benchmark, ISDB, and nonrigid 3D
datasets. We also present a detailed analysis of the
particular relevance of each one of the different proposed
histogram-based signatures. Finally, we briefly discuss a
more local approach where the histograms are computed for a
number of overlapping patches from the object rather than
the whole shape, thereby opening the door to partial shape
comparisons. © 2008 Elsevier Inc. All rights
reserved.},
Doi = {10.1016/j.gmod.2008.10.002},
Key = {fds265038}
}

@article{fds265050,
Author = {Mairal, J and Bach, F and Ponce, J and Sapiro, G},
Title = {Online dictionary learning for sparse coding},
Journal = {Proceedings of the 26th International Conference on Machine
Learning, Icml 2009},
Pages = {689-696},
Year = {2009},
Month = {January},
Abstract = {Sparse coding - that is, modelling data vectors as sparse
linear combinations of basis elements - is widely used in
machine learning, neuroscience, signal processing, and
statistics. This paper focuses on learning the basis set,
also called dictionary, to adapt it to specific data, an
approach that has recently proven to be very effective for
signal reconstruction and classification in the audio and
image processing domains. This paper proposes a new online
optimization algorithm for dictionary learning, based on
stochastic approximations, which scales up gracefully to
large datasets with millions of training samples. A proof of
convergence is presented, along with experiments with
natural images demonstrating that it leads to faster
performance and better dictionaries than classical batch
algorithms for both small and large datasets.},
Key = {fds265050}
}

@article{fds265053,
Author = {Lecumberry, F and Pardo, A and Sapiro, G},
Title = {Multiple shape models for simultaneous object classification
and segmentation},
Journal = {Proceedings International Conference on Image Processing,
Icip},
Pages = {3001-3004},
Publisher = {IEEE},
Year = {2009},
Month = {January},
ISSN = {1522-4880},
url = {http://dx.doi.org/10.1109/ICIP.2009.5414596},
Abstract = {Shape models (SMs), capturing the common features of a set
of training shapes, represent a new incoming object based on
its projection onto the corresponding model. Given a set of
learned SMs representing different objects, and an image
with a new shape, this work introduces a joint
classification- segmentation framework with a twofold goal.
First, to automatically select the SM that best represents
the object, and second, to accurately segment the image
taking into account both the image information and the
features and variations learned from the on-line selected
model. A new energy functional is introduced that
simultaneously accomplishes both goals. Model selection is
performed based on a shape similarity measure, determining
which model to use at each iteration of the steepest descent
minimization, allowing for model switching and adaptation to
the data. High-order SMs are used in order to deal with very
similar object classes and natural variability within them.
The presentation of the framework is complemented with
examples for the difficult task of simultaneously
classifying and segmenting closely related shapes, stages of
human activities, in images with severe occlusions. ©2009
IEEE.},
Doi = {10.1109/ICIP.2009.5414596},
Key = {fds265053}
}

@article{fds264760,
Author = {Sapiro, G},
Title = {Un héritage symbolique détourné ? La nouvelle revue
française des années noires},
Journal = {Études Littéraires},
Volume = {40},
Number = {1},
Pages = {97-97},
Publisher = {Consortium Erudit},
Year = {2009},
ISSN = {0014-214X},
Doi = {10.7202/037901ar},
Key = {fds264760}
}

@article{fds264768,
Author = {Lecumberry, F and Pardo, A and Sapiro, G and IEEE},
Title = {MULTIPLE SHAPE MODELS FOR SIMULTANEOUS OBJECT CLASSIFICATION
AND SEGMENTATION},
Journal = {2009 16th Ieee International Conference on Image Processing,
Vols 1 6},
Pages = {3001-+},
Year = {2009},
ISBN = {978-1-4244-5653-6},
Doi = {10.1109/ICIP.2009.5414596},
Key = {fds264768}
}

@article{fds264778,
Author = {Ramirez, I and Lecumberry, F and Sapiro, G and IEEE},
Title = {Universal Priors for Sparse Modeling},
Journal = {2009 3rd Ieee International Workshop on Computational
2009)},
Pages = {197-+},
Year = {2009},
ISBN = {978-1-4244-5179-1},
Doi = {10.1109/CAMSAP.2009.5413302},
Key = {fds264778}
}

@article{fds265029,
Author = {Bar, L and Sapiro, G},
Title = {Generalized newton methods for energy formulations in image
processing},
Journal = {Proceedings International Conference on Image Processing,
Icip},
Pages = {809-812},
Publisher = {IEEE},
Year = {2008},
Month = {December},
ISSN = {1522-4880},
url = {http://dx.doi.org/10.1109/ICIP.2008.4711878},
Abstract = {Many problems in image processing are solved via the
minimization of a cost functional. The most widely used
optimization technique is the gradient descent, often used
due to its simplicity and applicability where other
optimization techniques, e.g., those coming from discrete
optimization, can not be used. Yet, gradient descent suffers
from a slow convergence, and often to just local minima
which highly depends on the condition number of the
functional Hessian. Newton-type methods, on the other hand,
are known to have a rapid (quadratic) convergence. In its
classical form, the Newton method relies on the L2-type norm
to define the descent direction. In this paper, we
generalize and reformulate this very important optimization
method by introducing a novel Newton method based on general
norms. This generalization opens up new possibilities in the
extraction of the Newton step, including benefits such as
mathematical stability and smoothness constraints. We first
present the derivation of the modified Newton step in the
calculus of variation framework. Then we demonstrate the
method with two common objective functionals: variational
image deblurring and geodesic active contours. We show that
in addition to the fast convergence, different selections
norm yield different and superior results. © 2008
IEEE.},
Doi = {10.1109/ICIP.2008.4711878},
Key = {fds265029}
}

@article{fds265039,
Author = {Haro, G and Randall, G and Sapiro, G},
Title = {Translated poisson mixture model for stratification
learning},
Journal = {International Journal of Computer Vision},
Volume = {80},
Number = {3},
Pages = {358-374},
Publisher = {Springer Nature},
Year = {2008},
Month = {December},
ISSN = {0920-5691},
url = {http://dx.doi.org/10.1007/s11263-008-0144-6},
Abstract = {A framework for the regularized and robust estimation of
non-uniform dimensionality and density in high dimensional
noisy data is introduced in this work. This leads to
learning stratifications, that is, mixture of manifolds
representing different characteristics and complexities in
the data set. The basic idea relies on modeling the high
dimensional sample points as a process of translated Poisson
mixtures, with regularizing restrictions, leading to a model
which includes the presence of noise. The translated Poisson
distribution is useful to model a noisy counting process,
and it is derived from the noise-induced translation of a
regular Poisson distribution. By maximizing the
log-likelihood of the process counting the points falling
into a local ball, we estimate the local dimension and
density. We show that the sequence of all possible local
countings in a point cloud formed by samples of a
stratification can be modeled by a mixture of different
translated Poisson distributions, thus allowing the presence
of mixed dimensionality and densities in the same data set.
With this statistical model, the parameters which best
describe the data, estimated via expectation maximization,
divide the points in different classes according to both
dimensionality and density, together with an estimation of
these quantities for each class. Theoretical asymptotic
results for the model are presented as well. The
presentation of the theoretical framework is complemented
with artificial and real examples showing the importance of
regularized stratification learning in high dimensional data
analysis in general and computer vision and image analysis
LLC.},
Doi = {10.1007/s11263-008-0144-6},
Key = {fds265039}
}

@article{fds265037,
Author = {Narasimha, R and Aganj, I and Bennett, AE and Borgnia, MJ and Zabransky,
D and Sapiro, G and McLaughlin, SW and Milne, JLS and Subramaniam,
S},
Title = {Evaluation of denoising algorithms for biological electron
tomography.},
Journal = {Journal of Structural Biology},
Volume = {164},
Number = {1},
Pages = {7-17},
Year = {2008},
Month = {October},
url = {http://www.ncbi.nlm.nih.gov/pubmed/18585059},
Abstract = {Tomograms of biological specimens derived using transmission
electron microscopy can be intrinsically noisy due to the
use of low electron doses, the presence of a "missing wedge"
in most data collection schemes, and inaccuracies arising
during 3D volume reconstruction. Before tomograms can be
interpreted reliably, for example, by 3D segmentation, it is
essential that the data be suitably denoised using
procedures that can be individually optimized for specific
data sets. Here, we implement a systematic procedure to
compare various nonlinear denoising techniques on tomograms
recorded at room temperature and at cryogenic temperatures,
and establish quantitative criteria to select a denoising
approach that is most relevant for a given tomogram. We
demonstrate that using an appropriate denoising algorithm
facilitates robust segmentation of tomograms of HIV-infected
macrophages and Bdellovibrio bacteria obtained from
specimens at room and cryogenic temperatures, respectively.
We validate this strategy of automated segmentation of
optimally denoised tomograms by comparing its performance
with manual extraction of key features from the same
tomograms.},
Doi = {10.1016/j.jsb.2008.04.006},
Key = {fds265037}
}

@article{fds265027,
Author = {Mairal, J and Bach, F and Ponce, J and Sapiro, G and Zisserman,
A},
Title = {Discriminative learned dictionaries for local image
analysis},
Journal = {26th Ieee Conference on Computer Vision and Pattern
Recognition, Cvpr},
Publisher = {IEEE},
Year = {2008},
Month = {September},
url = {http://dx.doi.org/10.1109/CVPR.2008.4587652},
Abstract = {Sparse signal models have been the focus of much recent
research, leading to (or improving upon) state-of-the-art
results in signal, image, and video restoration. This
article extends this line of research into a novel framework
for local image discrimination tasks, proposing an energy
formulation with both sparse reconstruction and class
discrimination components, jointly optimized during
dictionary learning. This approach improves over the state
of the art in texture segmentation experiments using the
Brodatz database, and it paves the way for a novel scene
analysis and recognition framework based on simultaneously
learning discriminative and reconstructive dictionaries.
Preliminary results in this direction using examples from
the Pascal VOC06 and Graz02 datasets are presented as well.
Doi = {10.1109/CVPR.2008.4587652},
Key = {fds265027}
}

@article{fds265025,
Author = {Rother, D and Williams, L and Sapiro, G},
Title = {Super-resolution texturing for online virtual
globes},
Journal = {2008 Ieee Computer Society Conference on Computer Vision and
Pattern Recognition Workshops, Cvpr Workshops},
Publisher = {IEEE},
Year = {2008},
Month = {September},
url = {http://dx.doi.org/10.1109/CVPRW.2008.4562961},
Abstract = {Online virtual globe applications such as Google Earth and
Maps, Microsoft Virtual Earth, and Yahoo! Maps, allow users
to explore realistic models of the Earth. To provide the
ground-level detail of interest to users, it is necessary to
serve and render high resolution images. For planetary
coverage at high resolution, a very large number of images
need to be acquired, stored, and transmitted, with
consequent high costs and difficulty for the application
provider, often resulting in lower than expected
performance. In this work we propose a supplementary
approach to render appropriate visual information in these
applications. Using super-resolution techniques based on the
combination and extension of known texture transfer and
synthesis algorithms, we develop a system to efficiently
synthesize fine detail consistent with the textures served.
This approach dramatically reduces the operational cost of
virtual globe displays, which are among the most
image-intensive applications on the Internet, while at the
same time improving their appearance. The proposed framework
is fast and preserves the coherence between corresponding
images at different resolutions, allowing consistent and
responsive interactive zooming and panning operations. The
framework is capable of adapting a library of multiscale
textures to pre-segmented regions in the highest-resolution
texture maps available. We also describe a simple interface
to obtain class label information from contributing users.
The presentation of the constituent techniques is
complemented with examples simulating our framework embedded
Doi = {10.1109/CVPRW.2008.4562961},
Key = {fds265025}
}

@article{fds265026,
Author = {Mahmoudi, M and Sapiro, G},
Title = {Three-dimensional point cloud recognition via distributions
of geometric distances},
Journal = {2008 Ieee Computer Society Conference on Computer Vision and
Pattern Recognition Workshops, Cvpr Workshops},
Publisher = {IEEE},
Year = {2008},
Month = {September},
url = {http://dx.doi.org/10.1109/CVPRW.2008.4563031},
Abstract = {A geometric framework for the recognition of
three-dimensional objects represented by point clouds is
introduced in this paper The proposed approach is based on
comparing distributions of intrinsic measurements on the
point cloud. In particular, intrinsic distances are
exploited as signatures for representing the point clouds.
The first signature we introduce is the histogram of
pairwise diffusion distances between all points on the shape
surface. These distances represent the probability of
traveling from one point to another in a fixed number of
random steps, the average intrinsic distances of all
possible paths of a given number of steps between the two
points. This signature is augmented by the histogram of the
actual pairwise geodesic distances, as well as the
distribution of the ratio between these two distances. These
signatures are not only geometric but also invariant to
bends. We further augment these signatures by the
distribution of a curvature function and the distribution of
a curvature weighted distance. These histograms are compared
using the χ 2 or other common distance metrics for
distributions. The presentation of the framework is
accompanied by theoretical justification and
state-of-the-art experimental results with the standard
Princeton 3D shape benchmark and ISDB datasets, as well as a
detailed analysis of the particular relevance of each one of
the different histogram-based signatures. Finally, we
briefly discuss a more local approach where the histograms
are computed for a number of overlapping patches from the
object rather than the whole shape, thereby opening the door
to partial shape comparisons. © 2008 IEEE.},
Doi = {10.1109/CVPRW.2008.4563031},
Key = {fds265026}
}

@article{fds265028,
Author = {Rother, D and Patwardhan, K and Aganj, I and Sapiro,
G},
Title = {3D priors for scene learning from a single
view},
Journal = {2008 Ieee Computer Society Conference on Computer Vision and
Pattern Recognition Workshops, Cvpr Workshops},
Publisher = {IEEE},
Year = {2008},
Month = {September},
url = {http://dx.doi.org/10.1109/CVPRW.2008.4563034},
Abstract = {A framework for scene learning from a single still video
camera is presented in this work. In particular, the camera
transformation and the direction of the shadows are learned
using information extracted from pedestrians walking in the
scene. The proposed approach poses the scene learning
estimation as a likelihood maximization problem, efficiently
solved via factorization and dynamic programming, and
amenable to an online implementation. We introduce a 3D
prior to model the pedestrian's appearance from any
viewpoint, and learn it using a standard off-the-shelf
consumer video camera and the Radon transform. This 3D prior
or "appearance model" is used to quantify the agreement
between the tentative parameters and the actual video
observations, taking into account not only the pixels
occupied by the pedestrian, but also those occupied by the
his shadows and/or reflections. The presentation of the
framework is complemented with an example of a casual video
scene showing the importance of the learned 3D pedestrian
prior and the accuracy of the proposed approach. © 2008
IEEE.},
Doi = {10.1109/CVPRW.2008.4563034},
Key = {fds265028}
}

@article{fds265021,
Author = {Liao, HY and Sapiro, G},
Title = {Sparse representations for limited data tomography},
Journal = {2008 5th Ieee International Symposium on Biomedical Imaging:
From Nano to Macro, Proceedings, Isbi},
Pages = {1375-1378},
Publisher = {IEEE},
Year = {2008},
Month = {September},
url = {http://dx.doi.org/10.1109/ISBI.2008.4541261},
Abstract = {In limited data tomography, with applications such as
electron microscopy and medical imaging, the scanning views
are within an angular range that is often both limited and
sparsely sampled. In these situations, standard algorithms
produce reconstructions with notorious artifacts. We show in
this paper that a sparsity image representation principle,
based on learning dictionaries for sparse representations of
image patches, leads to significantly improved
reconstructions of the unknown density from its limited
angle projections. The presentation of the underlying
framework is complemented with illustrative results on
artificial and real data. ©2008 IEEE.},
Doi = {10.1109/ISBI.2008.4541261},
Key = {fds265021}
}

@article{fds265023,
Author = {Aganj, I and Sapiro, G and Parikshak, N and Madsen, SK and Thompson,
PM},
Title = {Segmentation-free measurement of cortical thickness from
MRI},
Journal = {2008 5th Ieee International Symposium on Biomedical Imaging:
From Nano to Macro, Proceedings, Isbi},
Pages = {1625-1628},
Publisher = {IEEE},
Year = {2008},
Month = {September},
url = {http://dx.doi.org/10.1109/ISBI.2008.4541324},
Abstract = {Estimating the thickness of cerebral cortex is a key step in
many MR brain imaging studies, revealing valuable
information on development or disease progression. In this
work we present a new approach to measure the cortical
thickness, based on minimizing line integrals over the
probability map of the gray matter in the MRI volume.
Previous methods often perform a binary-valued segmentation
of the gray matter before measuring the thickness. Because
of image noise and partial voluming, such a hard
classification ignores the underlying class probabilities
assigned to each voxel, discarding potentially useful
information. We describe our proposed method and demonstrate
its performance on both artificial volumes and real 3D brain
MRI data from subjects with Alzheimer's disease and healthy
Doi = {10.1109/ISBI.2008.4541324},
Key = {fds265023}
}

@article{fds265024,
Author = {Haro, G and Lenglet, C and Sapiro, G and Thompson,
P},
Title = {On the non-uniform complexity of brain connectivity},
Journal = {2008 5th Ieee International Symposium on Biomedical Imaging:
From Nano to Macro, Proceedings, Isbi},
Pages = {887-890},
Publisher = {IEEE},
Year = {2008},
Month = {September},
url = {http://dx.doi.org/10.1109/ISBI.2008.4541139},
Abstract = {A stratification and manifold learning approach for
analyzing High Angular Resolution Diffusion Imaging (HARDI)
data is introduced in this paper. HARDI data provides
high-dimensional signals measuring the complex
microstructure of biological tissues, such as the cerebral
white matter. We show that these high-dimensional spaces may
be understood as unions of manifolds of varying
dimensions/complexity and densities. With such analysis, we
use clustering to characterize the structural complexity of
the white matter. We briefly present the underlying
framework and numerical experiments illustrating this
original and promising approach. ©2008 IEEE.},
Doi = {10.1109/ISBI.2008.4541139},
Key = {fds265024}
}

@article{fds265035,
Author = {Caselles, V and Haro, G and Sapiro, G and Verdera,
J},
Title = {On geometric variational models for inpainting surface
holes},
Journal = {Computer Vision and Image Understanding},
Volume = {111},
Number = {3},
Pages = {351-373},
Publisher = {Elsevier BV},
Year = {2008},
Month = {September},
ISSN = {1077-3142},
url = {http://dx.doi.org/10.1016/j.cviu.2008.01.002},
Abstract = {Geometric approaches for filling-in surface holes are
introduced and studied in this paper. The basic principle is
to choose the completing surface as one which minimizes a
power of the mean curvature. We interpret this principle in
a level set formulation, that is, we represent the surface
of interest in implicit form and we construct an energy
functional for the embedding function u. We first explore
two different formulations (which can be considered as
alternative) inspired by the above principle: in the first
one we write the mean curvature as the divergence of the
normal vector field θ to the isosurfaces of u; in the
second one we used the signed distance function D to the
surface as embedding function and we write the mean
curvature in terms of it. Then we solve the Euler-Lagrange
equations of these functionals which consist of a system of
second order partial differential equations (PDEs) for u and
θ, in the first case, or a fourth order PDE for D in the
second case. Then, simpler methods based on second order
elliptic PDEs, like Laplace equation or the absolutely
minimizing Lipschitz extension, are also proposed and
compared with the above higher order methods. The
theoretical and computational framework, as well as examples
with synthetic and real data, are presented in this paper.
Doi = {10.1016/j.cviu.2008.01.002},
Key = {fds265035}
}

@article{fds265036,
Author = {Liu, J and Bartesaghi, A and Borgnia, MJ and Sapiro, G and Subramaniam,
S},
Title = {Molecular architecture of native HIV-1 gp120
trimers.},
Journal = {Nature},
Volume = {455},
Number = {7209},
Pages = {109-113},
Year = {2008},
Month = {September},
url = {http://www.ncbi.nlm.nih.gov/pubmed/18668044},
Abstract = {The envelope glycoproteins (Env) of human and simian
immunodeficiency viruses (HIV and SIV, respectively) mediate
virus binding to the cell surface receptor CD4 on target
cells to initiate infection. Env is a heterodimer of a
transmembrane glycoprotein (gp41) and a surface glycoprotein
(gp120), and forms trimers on the surface of the viral
membrane. Using cryo-electron tomography combined with
three-dimensional image classification and averaging, we
report the three-dimensional structures of trimeric Env
displayed on native HIV-1 in the unliganded state, in
complex with the broadly neutralizing antibody b12 and in a
ternary complex with CD4 and the 17b antibody. By fitting
the known crystal structures of the monomeric gp120 core in
the b12- and CD4/17b-bound conformations into the density
maps derived by electron tomography, we derive molecular
models for the native HIV-1 gp120 trimer in unliganded and
CD4-bound states. We demonstrate that CD4 binding results in
a major reorganization of the Env trimer, causing an outward
rotation and displacement of each gp120 monomer. This
appears to be coupled with a rearrangement of the gp41
region along the central axis of the trimer, leading to
closer contact between the viral and target cell membranes.
Our findings elucidate the structure and conformational
changes of trimeric HIV-1 gp120 relevant to antibody
neutralization and attachment to target cells.},
Doi = {10.1038/nature07159},
Key = {fds265036}
}

@article{fds265033,
Author = {Duarte-Carvajalino, JM and Sapiro, G and Vélez-Reyesvelez-Reyes,
M and Castillo, PE},
Title = {Multiscale representation and segmentation of hyperspectral
imagery using geometric partial differential equations and
algebraic multigrid methods},
Journal = {Ieee Transactions on Geoscience and Remote
Sensing},
Volume = {46},
Number = {8},
Pages = {2418-2434},
Publisher = {Institute of Electrical and Electronics Engineers
(IEEE)},
Year = {2008},
Month = {August},
ISSN = {0196-2892},
url = {http://dx.doi.org/10.1109/TGRS.2008.916478},
Abstract = {A fast algorithm for multiscale representation and
segmentation of hyperspectral imagery is introduced in this
paper. The multiscale/scale-space representation is obtained
by solving a nonlinear diffusion partial differential
equation (PDE) for vector-valued images. We use algebraic
multigrid techniques to obtain a fast and scalable solution
of the PDE and to segment the hyperspectral image following
the intrinsic multigrid structure. We test our algorithm on
four standard hyperspectral images that represent different
environments commonly found in remote sensing applications:
agricultural, urban, mining, and marine. The experimental
results show that the segmented images lead to better
classification than using the original data directly, in
spite of the use of simple similarity metrics and piecewise
constant approximations obtained from the segmentation maps.
Doi = {10.1109/TGRS.2008.916478},
Key = {fds265033}
}

@article{fds264792,
Author = {Sapiro, G},
Title = {Translation and the field of publishing},
Journal = {Translation Studies},
Volume = {1},
Number = {2},
Pages = {154-166},
Publisher = {Informa UK Limited},
Year = {2008},
Month = {July},
ISSN = {1478-1700},
Doi = {10.1080/14781700802113473},
Key = {fds264792}
}

@article{fds265032,
Author = {Singh, G and Memoli, F and Ishkhanov, T and Sapiro, G and Carlsson, G and Ringach, DL},
Title = {Topological analysis of population activity in visual
cortex.},
Journal = {Journal of Vision},
Volume = {8},
Number = {8},
Pages = {11.1-1118},
Year = {2008},
Month = {June},
url = {http://www.ncbi.nlm.nih.gov/pubmed/18831634},
Abstract = {Information in the cortex is thought to be represented by
the joint activity of neurons. Here we describe how
fundamental questions about neural representation can be
cast in terms of the topological structure of population
activity. A new method, based on the concept of persistent
homology, is introduced and applied to the study of
population activity in primary visual cortex (V1). We found
that the topological structure of activity patterns when the
cortex is spontaneously active is similar to those evoked by
natural image stimulation and consistent with the topology
of a two sphere. We discuss how this structure could emerge
from the functional organization of orientation and spatial
frequency maps and their mutual relationship. Our findings
extend prior results on the relationship between spontaneous
and evoked activity in V1 and illustrates how computational
topology can help tackle elementary questions about the
representation of information in the nervous
system.},
Doi = {10.1167/8.8.11},
Key = {fds265032}
}

@article{fds265020,
Author = {Duarte-Carvajalino, JM and Sapiro, G and Velez-Reyes,
M},
Title = {Unsupervised spectral-spatial classification of
hyperspectral imagery using real and complex features and
generalized histograms},
Journal = {Smart Structures and Materials 2005: Active Materials:
Behavior and Mechanics},
Volume = {6966},
Publisher = {SPIE},
Year = {2008},
Month = {June},
ISSN = {0277-786X},
url = {http://dx.doi.org/10.1117/12.779142},
Abstract = {In this work, we study unsupervised classification
algorithms for hyperspectral images based on band-by-band
scalar histograms and vector-valued generalized histograms,
obtained by vector quantization. The corresponding
histograms are compared by dissimilarity metrics such as the
chi-square, Kolmogorov-Smirnorv, and earth mover's
distances. The histograms are constructed from homogeneous
regions in the images identified by a pre-segmentation
algorithm and distance metrics between pixels. We compare
C-means and ISODATA, versus spectral-spatial segmentation
algorithms such as unsupervised ECHO and a novel
segmentation algorithm based on scale-space concepts. We
also evaluate the use of complex features consisting of the
real spectrum and its derivative as the imaginary part. The
comparison between the different segmentation algorithms and
distance metrics is based on their unsupervised
classification accuracy using three real hyperspectral
images with known ground truth.},
Doi = {10.1117/12.779142},
Key = {fds265020}
}

@article{fds265031,
Author = {Bartesaghi, A and Sprechmann, P and Liu, J and Randall, G and Sapiro, G and Subramaniam, S},
Title = {Classification and 3D averaging with missing wedge
correction in biological electron tomography.},
Journal = {Journal of Structural Biology},
Volume = {162},
Number = {3},
Pages = {436-450},
Year = {2008},
Month = {June},
url = {http://www.ncbi.nlm.nih.gov/pubmed/18440828},
Abstract = {Strategies for the determination of 3D structures of
biological macromolecules using electron crystallography and
single-particle electron microscopy utilize powerful tools
for the averaging of information obtained from 2D projection
images of structurally homogeneous specimens. In contrast,
electron tomographic approaches have often been used to
study the 3D structures of heterogeneous, one-of-a-kind
objects such as whole cells where image-averaging strategies
are not applicable. Complex entities such as cells and
viruses, nevertheless, contain multiple copies of numerous
macromolecules that can individually be subjected to 3D
averaging. Here we present a complete framework for
alignment, classification, and averaging of volumes derived
by electron tomography that is computationally efficient and
effectively accounts for the missing wedge that is inherent
to limited-angle electron tomography. Modeling the missing
data as a multiplying mask in reciprocal space we show that
the effect of the missing wedge can be accounted for
seamlessly in all alignment and classification operations.
We solve the alignment problem using the convolution theorem
in harmonic analysis, thus eliminating the need for
approaches that require exhaustive angular search, and adopt
an iterative approach to alignment and classification that
does not require the use of external references. We
demonstrate that our method can be successfully applied for
3D classification and averaging of phantom volumes as well
as experimentally obtained tomograms of GroEL where the
outcomes of the analysis can be quantitatively compared
against the expected results.},
Doi = {10.1016/j.jsb.2008.02.008},
Key = {fds265031}
}

@article{fds265034,
Author = {Liu, J and Bartesaghi, A and Borgnia, MJ and Sapiro, G and Subramaniam,
S},
Title = {Molecular Architecture of Native HIV-1 gp 120
Trimers},
Journal = {Chemtracts},
Volume = {21},
Number = {6},
Pages = {227-228},
Year = {2008},
Month = {June},
ISSN = {1431-9268},
Abstract = {A critical step in human and simian immunodeficiency virus
(HIV and SIV, respectively) pathogenesis is entry into the
target cell. The process of infection is mediated by
envelope glycoproteins, Env, which assemble in a trim- eric
form on the surface of the virus and bind exposed CD4
molecules on target cells. A monomeric Env is composed of a
transmembrane subunit, gp41, and a surface subunit, gpl20.
Although there are crystal structures of monomeric Env
associated with Fab fragments from the neutralizing antibody
bl2 in the CD4 bound and free forms, the structural
elucidation of the biologically relevant native trimeric
state has been a challenge. In this study, the authors used
cryoelectron tomography, in conjunction with the crystallo-
graphic information from monomers, to create a model of the
trimeric HIV- 1 gpl20 with or without CD4 (Fig. 1).
Essentially, the strategy involved fitting of the crystal
structure of the monomers into the density of the trimer
obtained from cryoelectron tomography. From the ligand-bound
and ligand- free molecular models, a schematic of the
conformational changes that occur upon CD4 binding was
presented (Fig. 1). Briefly, the trimer sticks out of the
viral membrane, referred to as the spike, as anchored by
gp41 (blue) and with each gpl20 (red) having an exposed CD4
binding site (orange). Upon binding of the ligand, CD4
(yellow), the gpl20 monomers undergo a conformational change
to expose the V3 loop, shown as a green patch on Figure le.
Such conformational change orients the trimer for proper
recognition of a chemokine receptor, as depicted in Figure
If. © 2008 Data Trace Publishing Company.},
Key = {fds265034}
}

@article{fds265030,
Author = {Patwardhan, KA and Sapiro, G and Morellas, V},
Title = {Robust foreground detection in video using pixel
layers.},
Journal = {Ieee Transactions on Pattern Analysis and Machine
Intelligence},
Volume = {30},
Number = {4},
Pages = {746-751},
Year = {2008},
Month = {April},
ISSN = {0162-8828},
url = {http://www.ncbi.nlm.nih.gov/pubmed/18276979},
Abstract = {A framework for robust foreground detection that works under
difficult conditions such as dynamic background and
moderately moving camera is presented in this paper. The
proposed method includes two main components: coarse scene
representation as the union of pixel layers, and foreground
detection in video by propagating these layers using a
maximum-likelihood assignment. We first cluster into
"layers" those pixels that share similar statistics. The
entire scene is then modeled as the union of such
non-parametric layer-models. An in-coming pixel is detected
of the background. A principled way of computing thresholds
is used to achieve robust detection performance with a
pre-specified number of false alarms. Correlation between
pixels in the spatial vicinity is exploited to deal with
camera motion without precise registration or optical flow.
The proposed technique adapts to changes in the scene, and
allows to automatically convert persistent foreground
objects to background and re-convert them to foreground when
they become interesting. This simple framework addresses the
important problem of robust foreground and unusual region
detection, at about 10 frames per second on a standard
laptop computer. The presentation of the proposed approach
is complemented by results on challenging real data and
comparisons with other standard techniques.},
Doi = {10.1109/tpami.2007.70843},
Key = {fds265030}
}

@article{fds265018,
Author = {Rother, D and Sapiro, G and Pande, V},
Title = {Statistical characterization of protein ensembles.},
Journal = {Ieee/Acm Transactions on Computational Biology and
Bioinformatics},
Volume = {5},
Number = {1},
Pages = {42-55},
Year = {2008},
Month = {January},
ISSN = {1545-5963},
url = {http://www.ncbi.nlm.nih.gov/pubmed/18245874},
Abstract = {When accounting for structural fluctuations or measurement
errors, a single rigid structure may not be sufficient to
represent a protein. One approach to solve this problem is
to represent the possible conformations as a discrete set of
observed conformations, an ensemble. In this work, we follow
a different richer approach, and introduce a framework for
estimating probability density functions in very high
dimensions, and then apply it to represent ensembles of
folded proteins. This proposed approach combines techniques
such as kernel density estimation, maximum likelihood,
cross-validation, and bootstrapping. We present the
underlying theoretical and computational framework and apply
it to artificial data and protein ensembles obtained from
molecular dynamics simulations. We compare the results with
those obtained experimentally, illustrating the potential
Doi = {10.1109/tcbb.2007.1061},
Key = {fds265018}
}

@article{fds265022,
Author = {Mairal, J and Elad, M and Sapiro, G},
Title = {Sparse representation for color image restoration.},
Journal = {Ieee Transactions on Image Processing : a Publication of the
Ieee Signal Processing Society},
Volume = {17},
Number = {1},
Pages = {53-69},
Year = {2008},
Month = {January},
ISSN = {1057-7149},
url = {http://www.ncbi.nlm.nih.gov/pubmed/18229804},
Abstract = {Sparse representations of signals have drawn considerable
interest in recent years. The assumption that natural
signals, such as images, admit a sparse decomposition over a
redundant dictionary leads to efficient algorithms for
handling such sources of data. In particular, the design of
well adapted dictionaries for images has been a major
challenge. The K-SVD has been recently proposed for this
task and shown to perform very well for various grayscale
problem of learning dictionaries for color images and extend
the K-SVD-based grayscale image denoising algorithm that
appears in. This work puts forward ways for handling
nonhomogeneous noise and missing information, paving the way
to state-of-the-art results in applications such as color
image denoising, demosaicing, and inpainting, as
demonstrated in this paper.},
Doi = {10.1109/tip.2007.911828},
Key = {fds265022}
}

@article{Mairal2008,
Author = {Mairal, J and Sapiro, G and Elad, M},
Title = {Learning multiscale sparse representations for image and
video restoration},
Journal = {Multiscale Modeling & Simulation},
Volume = {7},
Number = {1},
Pages = {214-241},
Publisher = {Society for Industrial & Applied Mathematics
(SIAM)},
Year = {2008},
Month = {January},
ISSN = {1540-3459},
url = {http://dx.doi.org/10.1137/070697653},
Abstract = {This paper presents a framework for learning multiscale
sparse representations of color images and video with
overcomplete dictionaries. A single-scale K-SVD algorithm
was introduced in [M. Aharon, M. Elad, and A. M. Bruckstein,
IEEE Trans. Signal Process., 54 (2006), pp. 4311-4322],
formulating sparse dictionary learning for grayscale image
representation as an optimization problem, efficiently
solved via orthogonal matching pursuit (OMP) and singular
value decomposition (SVD). Following this work, we propose a
multiscale learned representation, obtained by using an
efficient quadtree decomposition of the learned dictionary
and overlapping image patches. The proposed framework
provides an alternative to predefined dictionaries such as
wavelets and is shown to lead to state-of-the-art results in
a number of image and video enhancement and restoration
applications. This paper describes the proposed framework
and accompanies it by numerous examples demonstrating its
strength. © 2008 Society for Industrial and applied
Mathematics.},
Doi = {10.1137/070697653},
Key = {Mairal2008}
}

@article{fds264767,
Author = {Bar, L and Sapiro, G and IEEE},
Title = {GENERALIZED NEWTON METHODS FOR ENERGY FORMULATIONS IN IMAGE
PROCESSING},
Journal = {Proceedings International Conference on Image Processing,
Icip},
Pages = {813-816},
Year = {2008},
ISBN = {978-1-4244-1764-3},
ISSN = {1522-4880},
Key = {fds264767}
}

@article{fds264835,
Author = {Mairal, J and Bach, FR and Ponce, J and Sapiro, G and Zisserman,
A},
Title = {Supervised Dictionary Learning.},
Journal = {Nips},
Pages = {1033-1040},
Publisher = {Curran Associates, Inc.},
Editor = {Koller, D and Schuurmans, D and Bengio, Y and Bottou,
L},
Year = {2008},
Key = {fds264835}
}

@article{fds264852,
Author = {Rother, D and Sapiro, G and Pande, V},
Title = {Statistical Characterization of Protein Ensembles.},
Journal = {Ieee/Acm Trans. Comput. Biology Bioinform.},
Volume = {5},
Pages = {42-55},
Year = {2008},
url = {http://dx.doi.org/10.1145/1343571.1343575},
Doi = {10.1145/1343571.1343575},
Key = {fds264852}
}

@article{fds264864,
Author = {Sapiro, G},
Title = {Message from the Editor-in-Chief.},
Journal = {Siam J. Imaging Sciences},
Volume = {1},
Pages = {1-1},
Year = {2008},
Key = {fds264864}
}

@article{fds265009,
Author = {Sundaramoorthi, G and Yezzi, A and Mennucci, AC and Sapiro,
G},
Title = {New possibilities with Sobolev active contours},
Journal = {Lecture Notes in Computer Science (Including Subseries
Lecture Notes in Artificial Intelligence and Lecture Notes
in Bioinformatics)},
Volume = {4485 LNCS},
Pages = {153-164},
Year = {2007},
Month = {December},
ISSN = {0302-9743},
Abstract = {Recently, the Sobolev metric was introduced to define
gradient flows of various geometric active contour energies.
It was shown that the Sobolev metric out-performs the
traditional metric for the same energy in many cases such as
for tracking where the coarse scale changes of the contour
are important. Some interesting properties of Sobolev
gradient flows are that they stabilize certain unstable
traditional flows, and the order of the evolution PDEs are
same energies. In this paper, we explore new possibilities
for active contours made possible by Sobolev active
contours. The Sobolev method allows one to implement new
energy-based active contour models that were not otherwise
considered because the traditional minimizing method cannot
be used. In particular, we exploit the stabilizing and the
order reducing properties of Sobolev gradients to implement
the gradient descent of these new energies. We give examples
of this class of energies, which include some simple
geometric priors and new edge-based energies. We will show
that these energies can be quite useful for segmentation and
tracking. We will show that the gradient flows using the
traditional metric are either ill-posed or numerically
difficult to implement, and then show that the flows can be
implemented in a stable and numerically feasible manner
Heidelberg 2007.},
Key = {fds265009}
}

@article{fds264997,
Author = {Xue, B and Guillermo, S},
Title = {Distancecut: Interactive segmentation and matting of images
and videos},
Journal = {Proceedings International Conference on Image Processing,
Icip},
Volume = {2},
Pages = {II249-II252},
Publisher = {IEEE},
Year = {2007},
Month = {December},
ISSN = {1522-4880},
url = {http://dx.doi.org/10.1109/ICIP.2007.4379139},
Abstract = {An interactive algorithm for soft segmentation and matting
of natural Images and videos is presented in this paper. The
technique follows and extends [10], where the user first
roughly scribbles/labels different regions of interest, and
from them the whole data is automatically segmented. The
segmentation and alpha matte are obtained from the fast,
linear complexity, computation of weighted distances to the
user-provided scribbles. These weighted distances assign
probabilities to each labeled class for every pixel. The
weights are derived from models of the image regions
obtained from the user provided scribbles via kernel density
estimation. The matting results follow from combining this
density and the computed weighted distances. We present the
underlying framework and examples showing the capability of
the algorithm to segment and compute alpha mattes, in
interactive real time, for difficult natural data. © 2007
IEEE.},
Doi = {10.1109/ICIP.2007.4379139},
Key = {fds264997}
}

@article{fds265010,
Author = {Rother, D and Patwardhan, KA and Sapiro, G},
Title = {What can casual walkers tell us about a 3D
scene?},
Journal = {Proceedings of the Ieee International Conference on Computer
Vision},
Publisher = {IEEE},
Year = {2007},
Month = {December},
url = {http://dx.doi.org/10.1109/ICCV.2007.4409083},
Abstract = {An approach for incremental learning of a 3D scene from a
single static video camera is presented in this paper. In
particular, we exploit the presence of casual people walking
in the scene to infer relative depth, learn shadows, and
segment the critical ground structure. Considering that this
type of video data is so ubiquitous, this work provides an
important step towards 3D scene analysis from single cameras
in readily available ordinary videos and movies. On-line 3D
scene learning, as presented here, is very important for
applications such as scene analysis, foreground refinement,
tracking, biometrics, automated camera collaboration,
activity analysis, identification, and real-time
computer-graphics applications. The main contributions of
this work are then two-fold. First, we use the people in the
scene to continuously learn and update the 3D scene
parameters using an incremental robust (L1) error
minimization. Secondly, models of shadows in the scene are
learned using a statistical framework. A symbiotic
relationship between the shadow model and the estimated
scene geometry is exploited towards incremental mutual
improvement. We illustrate the effectiveness of the proposed
framework with applications in foreground refinement,
automatic segmentation as well as relative depth mapping of
the floor/ground, and estimation of 3D trajectories of
people in the scene. ©2007 IEEE.},
Doi = {10.1109/ICCV.2007.4409083},
Key = {fds265010}
}

@article{fds265011,
Author = {Bai, X and Sapiro, G},
Title = {A geodesic framework for fast interactive image and video
segmentation and matting},
Journal = {Proceedings of the Ieee International Conference on Computer
Vision},
Publisher = {IEEE},
Year = {2007},
Month = {December},
url = {http://dx.doi.org/10.1109/ICCV.2007.4408931},
Abstract = {An interactive framework for soft segmentation and matting
of natural images and videos is presented in this paper. The
proposed technique is based on the optimal, linear time,
computation of weighted geodesic distances to the
user-provided scribbles, from which the whole data is
automatically segmented. The weights are based on spatial
and/or temporal gradients, without explicit optical flow or
any advanced and often computationally expensive feature
detectors. These could be naturally added to the proposed
framework as well if desired, in the form of weights in the
geodesic distances. A localized refinement step follows this
fast segmentation in order to accurately compute the
corresponding matte function. Additional constraints into
the distance definition permit to efficiently handle
occlusions such as people or objects crossing each other in
a video sequence. The presentation of the framework is
complemented with numerous and diverse examples, including
extraction of moving foreground from dynamic background, and
comparisons with the recent literature. ©2007
IEEE.},
Doi = {10.1109/ICCV.2007.4408931},
Key = {fds265011}
}

@article{fds265012,
Author = {Haro, G and Randall, G and Sapiro, G},
Title = {Stratification learning: Detecting mixed density and
dimensionality in high dimensional point
clouds},
Journal = {Advances in Neural Information Processing
Systems},
Pages = {553-560},
Year = {2007},
Month = {December},
ISSN = {1049-5258},
Abstract = {The study of point cloud data sampled from a stratification,
a collection of manifolds with possible different
dimensions, is pursued in this paper. We present a technique
for simultaneously soft clustering and estimating the mixed
dimensionality and density of such structures. The framework
is based on a maximum likelihood estimation of a Poisson
mixture model. The presentation of the approach is completed
with artificial and real examples demonstrating the
importance of extending manifold learning to stratification
learning.},
Key = {fds265012}
}

@article{fds265013,
Author = {Bar, L and Rumpf, M and Berkels, B and Sapiro, G},
Title = {A variational framework for simultaneous motion estimation
and restoration of motion-blurred video},
Journal = {Proceedings of the Ieee International Conference on Computer
Vision},
Publisher = {IEEE},
Year = {2007},
Month = {December},
url = {http://dx.doi.org/10.1109/ICCV.2007.4409009},
Abstract = {The problem of motion estimation and restoration of objects
in a blurred video sequence is addressed in this paper. Fast
movement of the objects, together with the aperture time of
the camera, result in a motion-blurred image. The direct
velocity estimation from this blurred video is inaccurate.
On the other hand, an accurate estimation of the velocity of
the moving objects is critical for restoration of
motion-blurred video. Therefore, restoration needs accurate
motion estimation and vice versa, and a joint process is
called for. To address this problem we derive a novel model
of the blurring process and propose a Mumford-Shah type of
variational framework, acting on consecutive frames, for
joint object deblurring and velocity estimation. The
proposed procedure distinguishes between the moving object
and the background and is accurate also close to the
boundary of the moving object. Experimental results both on
simulated and real data show the importance of this joint
estimation and its superior performance when compared to the
independent estimation of motion and restoration. ©2007
IEEE.},
Doi = {10.1109/ICCV.2007.4409009},
Key = {fds265013}
}

@article{fds265004,
Author = {Bartesaghi, A and Sprechmann, P and Randall, G and Sapiro, G and Subramaniam, S},
Title = {Classification, averaging and reconstruction of
macromolecules in electron tomography},
Journal = {2007 4th Ieee International Symposium on Biomedical Imaging:
From Nano to Macro Proceedings},
Pages = {244-247},
Publisher = {IEEE},
Year = {2007},
Month = {November},
url = {http://dx.doi.org/10.1109/ISBI.2007.356834},
Abstract = {Electron tomography provides opportunities to determine
diree-dimensional cellular architecture at resolutions high
enough to identify individual macromolecules such as
proteins. Image analysis of such data poses a challenging
problem due to the extremely low signal-to-noise ratios that
makes individual volumes simply too noisy to allow reliable
structural interpretation. This requires using averaging
techniques to boost the signal-to-noise ratios, a common
practice in electron microscopy single particle analysis
where they have proven to be very powerful in elucidating
high resolution structure. Although there are significant
similarities in the way data is processed, several new
problems arise in the tomography case that have to be
properly dealt with. Such problems involve dealing with the
missing wedge characteristic of limited angle tomography,
the need for robust and efficient 3D alignment routines, and
design of mediods that account for diverse conformations
through the use of classification. We present a framework
for reconstruction via alignment, classification and
averaging of volumes obtained from limited angle electron
tomography, providing a powerful tool for high resolution
structure determination and description of conformational
variability in a biological context. © 2007
IEEE.},
Doi = {10.1109/ISBI.2007.356834},
Key = {fds265004}
}

@article{fds265007,
Author = {Aganj, I and Bartesaghi, A and Borgnia, M and Liao, HY and Sapiro, G and Subramaniam, S},
Title = {Regularization for inverting the radon transform with wedge
consideration},
Journal = {2007 4th Ieee International Symposium on Biomedical Imaging:
From Nano to Macro Proceedings},
Pages = {217-220},
Publisher = {IEEE},
Year = {2007},
Month = {November},
url = {http://dx.doi.org/10.1109/ISBI.2007.356827},
Abstract = {In limited angle tomography, with applications such as
electron microscopy, medical imaging, and industrial
testing, the object of interest is scanned over a limited
angular range, which is less than the full 180°
mathematically required for density reconstruction. The use
of standard full-range reconstruction algorithms produces
results widi notorious "butterfly" or "wedge" artifacts. In
this work we propose a reconstruction technique with a
regularization term that takes into account the orientation
of the missing angular range, also denoted as missing wedge.
We show that a regularization diat penalizes non-uniformly
in the orientation space produces reconstructions with less
artifacts, thereby improving the recovery of the "invisible"
edges due to the missing wedge. We present the underlying
framework and results for a challenging phantom and real
cryo-electron microscopy data. © 2007 IEEE.},
Doi = {10.1109/ISBI.2007.356827},
Key = {fds265007}
}

@article{fds265008,
Author = {Narasimha, R and Aganj, I and Borgnia, M and Sapiro, G and McLaughlin,
S and Milne, J and Subramaniam, S},
Title = {From gigabytes to bytes: Automated denoising and feature
identification in electron tomograms of intact bacterial
cells},
Journal = {2007 4th Ieee International Symposium on Biomedical Imaging:
From Nano to Macro Proceedings},
Pages = {304-307},
Publisher = {IEEE},
Year = {2007},
Month = {November},
url = {http://dx.doi.org/10.1109/ISBI.2007.356849},
Abstract = {Advances in automated data acquisition in electron
tomography have led to an explosion in the amount of data
that can be obtained about the spatial architecture of a
variety of biologically and medically relevant objects with
resolutions in the "nano" range of 10-1000 nm. The
development of methods to automatically analyze the vast
amounts of information contained in these tomograms is a
major challenge since the electron tomograms are
intrinsically very noisy. A fundamental step in the
automatic analysis of large amounts of data for statistical
inference is to segment relevant 3D features in cellular
tomograms. Procedures for segmentation must work robustly
and rapidly in spite of the low signal to noise ratios
inherent to biological electron microscopy. This work first
evaluates various non-linear denoising techniques on
tomograms recorded at cryogenic temperatures. Using datasets
of bacterial tomograms as an example, we demonstrate that
non-linear diffusion techniques significantly improve the
fidelity of automated feature extraction. Our approach
represents an important step in automating the efficient
extraction of useful information from large datasets in
biological tomography, and facilitates the overall goal of
speeding up the process of reducing gigabyte-sized tomograms
to relevant byte-sized data. © 2007 IEEE.},
Doi = {10.1109/ISBI.2007.356849},
Key = {fds265008}
}

@article{fds265005,
Author = {Duarte-Carvajalino, JM and Sapiro, G and Vélez-Reyes, M and Castillo, P},
Title = {Fast multi-scale regularization and segmentation of
hyperspectral imagery via anisotropic diffusion and
Algebraic Multigrid solvers},
Journal = {Smart Structures and Materials 2005: Active Materials:
Behavior and Mechanics},
Volume = {6565},
Publisher = {SPIE},
Year = {2007},
Month = {November},
ISSN = {0277-786X},
url = {http://dx.doi.org/10.1117/12.721036},
Abstract = {This paper presents an algorithm that generates a
scale-space representation of hyperspectral imagery using
Algebraic Multigrid (AMG) solvers. The scale-space
representation is obtained by solving with AMG a
vector-valued anisotropic diffusion equation, with the
hyperspectral image as its initial condition. AMG also
provides the necessary structure to obtain a hierarchical
segmentation of the image. The scale space representation of
the hyperspectral image can be segmented in linear time
complexity. Results in the paper show that improved
segmentation is achieved. The proposed methodology to solve
vector PDEs can be used to extend a number of techniques
currently being developed for the fast computation of
geometric PDEs and its application for the processing of
hyperspectral and multispectral imagery.},
Doi = {10.1117/12.721036},
Key = {fds265005}
}

@article{fds265002,
Author = {Haro, G and Randall, G and Sapiro, G},
Title = {Regularized mixed dimensionality and density learning in
computer vision},
Journal = {Proceedings of the Ieee Computer Society Conference on
Computer Vision and Pattern Recognition},
Publisher = {IEEE},
Year = {2007},
Month = {October},
ISSN = {1063-6919},
url = {http://dx.doi.org/10.1109/CVPR.2007.383401},
Abstract = {A framework for the regularized estimation of non-uniform
dimensionality and density in high dimensional data is
introduced in this work. This leads to learning
stratifications, that is, mixture of manifolds representing
different characteristics and complexities in the data set.
The basic idea relies on modeling the high dimensional
sample points as a process of Poisson mixtures, with
regularizing restrictions and spatial continuity
constraints. Theoretical asymptotic results for the model
are presented as well, The presentation of the framework is
complemented with artificial and real examples showing the
importance of regularized stratification learning in
computer vision applications. © 2007 IEEE.},
Doi = {10.1109/CVPR.2007.383401},
Key = {fds265002}
}

@article{fds265006,
Author = {Arias, P and Randall, G and Sapiro, G},
Title = {Connecting the out-of-sample and pre-image problems in
Kernel methods},
Journal = {Proceedings of the Ieee Computer Society Conference on
Computer Vision and Pattern Recognition},
Publisher = {IEEE},
Year = {2007},
Month = {October},
ISSN = {1063-6919},
url = {http://dx.doi.org/10.1109/CVPR.2007.383038},
Abstract = {Kernel methods have been widely studied in the field of
pattern recognition. These methods implicitly map, "the
kernel trick," the data into a space which is more
appropriate for analysis. Many manifold learning and
dimensionality reduction techniques are simply kernel
methods for which the mapping is explicitly computed. In
such cases, two problems related with the mapping arise: The
out-of-sample extension and the pre-image computation. In
this paper we propose a new pre-image method based on the
Nyström formulation for the out-of-sample extension,
showing the connections between both problems. We also
address the importance of normalization in the feature
space, which has been ignored by standard pre-image
algorithms. As an example, we apply these ideas to the
Gaussian kernel, and relate our approach to other popular
pre-image methods. Finally, we show the application of these
techniques in the study of dynamic shapes. © 2007
IEEE.},
Doi = {10.1109/CVPR.2007.383038},
Key = {fds265006}
}

@article{fds264776,
Author = {Sapiro, G},
Title = {I never learned to write - The paths of creation},
Journal = {Actes De La Recherche En Sciences Sociales},
Number = {168},
Pages = {12-+},
Year = {2007},
Month = {June},
ISSN = {0335-5322},
Key = {fds264776}
}

@article{fds264806,
Author = {Sapiro, G},
Title = {The artistic vocation between donation and
self-donation},
Journal = {Actes De La Recherche En Sciences Sociales},
Number = {168},
Pages = {4-11},
Year = {2007},
Month = {June},
ISSN = {0335-5322},
Key = {fds264806}
}

@article{fds264825,
Author = {Sapiro, G},
Title = {The ideology of the donation},
Journal = {Actes De La Recherche En Sciences Sociales},
Number = {168},
Pages = {31-33},
Year = {2007},
Month = {June},
ISSN = {0335-5322},
Key = {fds264825}
}

@article{fds265019,
Author = {Moenning, C and Mémoli, F and Sapiro, G and Dyn, N and Dodgson,
NA},
Title = {Meshless geometric subdivision},
Journal = {Graphical Models},
Volume = {69},
Number = {3-4},
Pages = {160-179},
Publisher = {Elsevier BV},
Year = {2007},
Month = {May},
ISSN = {1524-0703},
url = {http://dx.doi.org/10.1016/j.gmod.2006.11.001},
Abstract = {Point-based surface processing has developed into an
attractive alternative to mesh-based processing tools for a
number of geometric modeling applications. By working with
point clouds directly, processing is based on the raw data
and its underlying geometry rather than any arbitrary
intermediate representations and generally artificial
connectivity relations. We extend this principle into the
area of subdivision surfaces by introducing the notion of
meshless geometric subdivision. Our approach replaces the
role of mesh connectivity with intrinsic point proximity
thereby avoiding a number of limitations of mesh-based
surface subdivision schemes. Apart from introducing this
idea of meshless subdivision, we put forward a first
intrinsic meshless subdivision scheme and present a new
method for the computation of intrinsic means on Euclidean
manifolds. © 2006 Elsevier Inc. All rights
reserved.},
Doi = {10.1016/j.gmod.2006.11.001},
Key = {fds265019}
}

@article{fds264805,
Author = {Kao, C-Y and Hofer, M and Sapiro, G and Stern, J and Rehm, K and Rottenberg, DA},
Title = {A geometric method for automatic extraction of sulcal
fundi},
Journal = {Ieee Transactions on Medical Imaging},
Volume = {26},
Number = {4},
Pages = {530-540},
Year = {2007},
Month = {April},
ISSN = {0278-0062},
Doi = {10.1109/TMI.2006.8868},
Key = {fds264805}
}

@article{fds265014,
Author = {Kao, C-Y and Hofer, M and Sapiro, G and Stem, J and Rehm, K and Rottenberg,
DA},
Title = {A geometric method for automatic extraction of sulcal
fundi.},
Journal = {Ieee Transactions on Medical Imaging},
Volume = {26},
Number = {4},
Pages = {530-540},
Year = {2007},
Month = {April},
ISSN = {0278-0062},
url = {http://www.ncbi.nlm.nih.gov/pubmed/17427740},
Abstract = {Sulcal fundi are 3-D curves that lie in the depths of the
cerebral cortex and, in addition to their intrinsic value in
brain research, are often used as landmarks for downstream
computations in brain imaging. In this paper, we present a
geometric algorithm that automatically extracts the sulcal
fundi from magnetic resonance images and represents them as
spline curves lying on the extracted triangular mesh
representing the cortical surface. The input to our
algorithm is a triangular mesh representation of an
extracted cortical surface as computed by one of several
available software packages for performing automated and
semi-automated cortical surface extraction. Given this input
we first compute a geometric depth measure for each triangle
on the cortical surface mesh, and based on this information
we extract sulcal regions by checking for connected regions
exceeding a depth threshold. We then identify endpoints of
each region and delineate the fundus by thinning the
connected region while keeping the endpoints fixed. The
curves, thus, defined are regularized using weighted splines
on the surface mesh to yield high-quality representations of
the sulcal fundi. We present the geometric framework and
validate it with real data from human brains. Comparisons
with expert-labeled sulcal fundi are part of this validation
process.},
Doi = {10.1109/tmi.2006.886810},
Key = {fds265014}
}

@article{fds265016,
Author = {Protiere, A and Sapiro, G},
Title = {Interactive image segmentation via adaptive weighted
distances.},
Journal = {Ieee Transactions on Image Processing : a Publication of the
Ieee Signal Processing Society},
Volume = {16},
Number = {4},
Pages = {1046-1057},
Year = {2007},
Month = {April},
ISSN = {1057-7149},
url = {http://www.ncbi.nlm.nih.gov/pubmed/17405436},
Abstract = {An interactive algorithm for soft segmentation of natural
images is presented in this paper. The user first roughly
scribbles different regions of interest, and from them, the
whole image is automatically segmented. This soft
segmentation is obtained via fast, linear complexity
computation of weighted distances to the user-provided
scribbles. The adaptive weights are obtained from a series
of Gabor filters, and are automatically computed according
to the ability of each single filter to discriminate between
the selected regions of interest. We present the underlying
framework and examples showing the capability of the
algorithm to segment diverse images.},
Doi = {10.1109/tip.2007.891796},
Key = {fds265016}
}

@article{fds265017,
Author = {Mohan, A and Sapiro, G and Bosch, E},
Title = {Spatially coherent nonlinear dimensionality reduction and
segmentation of hyperspectral images},
Journal = {Ieee Geoscience and Remote Sensing Letters},
Volume = {4},
Number = {2},
Pages = {206-210},
Publisher = {Institute of Electrical and Electronics Engineers
(IEEE)},
Year = {2007},
Month = {April},
ISSN = {1545-598X},
url = {http://dx.doi.org/10.1109/LGRS.2006.888105},
Abstract = {The nonlinear dimensionality reduction and its effects on
vector classification and segmentation of hyperspectral
images are investigated in this letter. In particular, the
way dimensionality reduction influences and helps
classification and segmentation is studied. The proposed
framework takes into account the nonlinear nature of
high-dimensional hyperspectral images and projects onto a
lower dimensional space via a novel spatially coherent
locally linear embedding technique. The spatial coherence is
introduced by comparing pixels based on their local
surrounding structure in the image domain and not just on
their individual values as classically done. This spatial
coherence in the image domain across the multiple bands
defines the high-dimensional local neighborhoods used for
the dimensionality reduction. This spatial coherence concept
is also extended to the segmentation and classification
stages that follow the dimensionality reduction, introducing
a modified vector angle distance. We present the underlying
concepts of the proposed framework and experimental results
showing the significant classification improvements. © 2007
IEEE.},
Doi = {10.1109/LGRS.2006.888105},
Key = {fds265017}
}

@article{fds265015,
Author = {Patwardhan, KA and Sapiro, G and Bertalmío, M},
Title = {Video inpainting under constrained camera
motion.},
Journal = {Ieee Transactions on Image Processing : a Publication of the
Ieee Signal Processing Society},
Volume = {16},
Number = {2},
Pages = {545-553},
Year = {2007},
Month = {February},
ISSN = {1057-7149},
url = {http://www.ncbi.nlm.nih.gov/pubmed/17269646},
Abstract = {A framework for inpainting missing parts of a video sequence
recorded with a moving or stationary camera is presented in
this work. The region to be inpainted is general: it may be
still or moving, in the background or in the foreground, it
may occlude one object and be occluded by some other object.
The algorithm consists of a simple preprocessing stage and
two steps of video inpainting. In the preprocessing stage,
we roughly segment each frame into foreground and
background. We use this segmentation to build three image
mosaics that help to produce time consistent results and
also improve the performance of the algorithm by reducing
the search space. In the first video inpainting step, we
reconstruct moving objects in the foreground that are
"occluded" by the region to be inpainted. To this end, we
fill the gap as much as possible by copying information from
the moving foreground in other frames, using a
priority-based scheme. In the second step, we inpaint the
remaining hole with the background. To accomplish this, we
first align the frames and directly copy when possible. The
remaining pixels are filled in by extending spatial texture
synthesis techniques to the spatiotemporal domain. The
proposed framework has several advantages over
state-of-the-art algorithms that deal with similar types of
data and constraints. It permits some camera motion, is
simple to implement, fast, does not require statistical
models of background nor foreground, works well in the
presence of rich and cluttered backgrounds, and the results
show that there is no visible blurring or motion artifacts.
A number of real examples taken with a consumer hand-held
camera are shown supporting these findings.},
Doi = {10.1109/tip.2006.888343},
Key = {fds265015}
}

@article{fds264784,
Author = {Bartesaghi, A and Sprechmann, P and Randall, G and Sapiro, G and Subramaniam, S},
Title = {A framework for classification and averaging of 3D
tomographic volumes},
Journal = {Biophysical Journal},
Pages = {509A-509A},
Publisher = {BIOPHYSICAL SOCIETY},
Year = {2007},
Month = {January},
ISSN = {0006-3495},
Key = {fds264784}
}

@article{fds264998,
Author = {Patwardhan, KA and Sapiro, G and Morellas, V},
Title = {A graph-based foreground representation and its application
in example based people matching in video},
Journal = {Proceedings International Conference on Image Processing,
Icip},
Volume = {5},
Pages = {V37-V40},
Publisher = {IEEE},
Year = {2007},
Month = {January},
ISSN = {1522-4880},
url = {http://dx.doi.org/10.1109/ICIP.2007.4379759},
Abstract = {In this work, we propose a framework for foreground
representation, in video and illustrate it with a
multi-camera people matching application. We first decompose
the video into foreground and back-ground. A low-level
coarse segmentation of the foreground is then used to
generate a simple graph representation. A vertex in the
graph represents the "appearance" of a corresponding segment
in the foreground, while the relationship between, two
segments is encoded by an edge between the corresponding
vertices. This provides a simple yet powerful and general
representation, of the foreground, which can be very useful
in problems such as people detection and tracking. We
illustrate the effectiveness of this model using an "example
based query" type of application for people matching in
videos. Matching results are provided in multiple-camera
situations and also under occlusion. © 2007
IEEE.},
Doi = {10.1109/ICIP.2007.4379759},
Key = {fds264998}
}

@article{fds264758,
Author = {Mairal, J and Sapiro, G and Elad, M and IEEE},
Title = {Multiscale sparse image representation with learned
dictionaries},
Journal = {Proceedings International Conference on Image Processing,
Icip},
Pages = {1233-+},
Year = {2007},
ISBN = {978-1-4244-1436-9},
ISSN = {1522-4880},
Key = {fds264758}
}

@article{fds264780,
Author = {Bai, X and Sapiro, G and IEEE},
Title = {Distancecut: Interactive segmentation and matting of images
and videos},
Journal = {Proceedings International Conference on Image Processing,
Icip},
Pages = {813-816},
Year = {2007},
ISBN = {978-1-4244-1436-9},
ISSN = {1522-4880},
Key = {fds264780}
}

@article{fds264824,
Author = {Patwardhan, KA and Sapiro, G and Morellas, V and IEEE},
Title = {A graph-based foreground representation and its application
in example based people matching in video},
Journal = {Proceedings International Conference on Image Processing,
Icip},
Pages = {2289-+},
Year = {2007},
ISBN = {978-1-4244-1436-9},
ISSN = {1522-4880},
Key = {fds264824}
}

@article{fds264847,
Author = {Rother, D and Patwardhan, KA and Sapiro, G},
Title = {What Can Casual Walkers Tell Us About A 3D
Scene?},
Journal = {2007 Ieee 11th International Conference on Computer
Vision},
Pages = {1-8},
Publisher = {IEEE},
Year = {2007},
ISBN = {9781424416301},
url = {http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=4408818},
Doi = {10.1109/iccv.2007.4409082},
Key = {fds264847}
}

@article{fds264724,
Author = {Bertalmío, M and Caselles, V and Haro, G and Sapiro,
G},
Title = {PDE-based image and surface inpainting},
Pages = {33-61},
Publisher = {Springer Verlag},
Year = {2006},
Month = {December},
url = {http://dx.doi.org/10.1007/0-387-28831-7_3},
Abstract = {Inpainting, the technique of modifying an image in an
undetectable form, is as ancient as art itself. The goals
and applications of inpainting are numerous, from the
restoration of damaged paintings, photographs and films, to
the removal of selected undesirable objects. This chapter is
intended to present an overview of PDE based image
inpainting algorithms, with emphasis in models developed by
the authors. These models are based on the propagation of
information along the image isophotes and on the
minimization of an energy functional which follows a
relaxation of the Elastica model. This last variational
formulation can be easily extended to 3D to fill holes in
surfaces, a problem closely related to image inpainting.
Basic PDE-based approaches to inpainting share the
shortcoming that they cannot restore texture, so
combinations of these algorithms with texture synthesis
techniques are also discussed. Some results are shown for
applications such as removal of text, restoration of
scratched photographs, removal of selected objects and
reconstruction of 3D surfaces with holes. Other recent
approaches to the image inpainting problem are also briefly
Inc.},
Doi = {10.1007/0-387-28831-7_3},
Key = {fds264724}
}

@article{fds264854,
Author = {Hofer, M and Sapiro, G and Wallner, J},
Title = {Fair polyline networks for constrained smoothing of digital
terrain elevation data},
Journal = {Ieee Transactions on Geoscience and Remote
Sensing},
Volume = {44},
Number = {10},
Pages = {2983-2990},
Publisher = {Institute of Electrical and Electronics Engineers
(IEEE)},
Year = {2006},
Month = {December},
url = {http://dx.doi.org/10.1109/TGRS.2006.875451},
Abstract = {In this paper, a framework for smoothing gridlike digital
terrain elevation data, which achieves a fair shape bymeans
of minimizing an energy functional, is presented. The
minimization is performed under the side condition of hard
constraints, which comes from available horizontal and
vertical accuracy bounds in the standard elevation
specification. In this paper, the framework is introduced,
and the suitability of this method for the tasks of
accuracy-constrained smoothing, feature-preserving
smoothing, and filling of data voids is demonstrated. ©
2006 IEEE.},
Doi = {10.1109/TGRS.2006.875451},
Key = {fds264854}
}

@article{fds264995,
Author = {Mairal, J and Sapiro, G and Elad, M},
Title = {Multiscale sparse image representation with learned
dictionaries},
Journal = {Proceedings International Conference on Image Processing,
Icip},
Volume = {3},
Pages = {III105-III108},
Publisher = {IEEE},
Year = {2006},
Month = {December},
ISSN = {1522-4880},
url = {http://dx.doi.org/10.1109/ICIP.2007.4379257},
Abstract = {This paper introduces a new framework for learning
multiscale sparse representations of natural images with
overcomplete dictionaries. Our work extends the K-SVD
algorithm [1], which learns sparse single-scale dictionaries
for natural images. Recent work has shown that the K-SVD can
lead to state-of-the-art image restoration results [2, 3].
We show that these are further improved with a multiscale
approach, based on a Quadtree decomposition. Our framework
provides an alternative to multiscale pre-defined
dictionaries such as wavelets, curvelets, and contourlets,
with dictionaries optimized for the data and application
Doi = {10.1109/ICIP.2007.4379257},
Key = {fds264995}
}

@article{fds264996,
Author = {Kao, CY and Hofer, M and Sapiro, G and Stern, J and Rottenberg,
DA},
Title = {A geometric method for automatic extraction of sulcal
fundi},
Journal = {2006 3rd Ieee International Symposium on Biomedical Imaging:
From Nano to Macro Proceedings},
Volume = {2006},
Pages = {1168-1171},
Year = {2006},
Month = {November},
Abstract = {Sulcal fundi are 3D curves that lie in the depths of the
cerebral cortex and are often used as landmarks for
downstream computations in brain imaging. We present a
sequence of geometric algorithms which automatically extract
the sulcal fundi from magnetic resonance images and
represent them as smooth polygons lying on the cortical
surface. First we compute a geometric depth measure for each
point on the cortical surface, and based on this information
we extract sulcal regions by checking the connectivity above
a depth threshold. We then extract the endpoints of each
fundus and delineate the fundus by thinning each connected
region keeping the endpoints fixed. The curves thus defined
are smoothed using weighted splines on the gray-matter
surface to yield high-quality representations of the sulcal
Key = {fds264996}
}

@article{fds264761,
Author = {Sapiro, G},
Title = {JANE F. FULCHER. The Composer as Intellectual: Music and
Ideology in France 1914-1940. New York: Oxford University
Press. 2005. Pp. xiv, 473. \$74.00},
Journal = {American Historical Review},
Volume = {111},
Number = {4},
Pages = {1261-1262},
Publisher = {Oxford University Press (OUP)},
Year = {2006},
Month = {October},
ISSN = {0002-8762},
Doi = {10.1086/ahr.111.4.1261},
Key = {fds264761}
}

@article{fds265003,
Author = {Greer, JB and Bertozzi, AL and Sapiro, G},
Title = {Fourth order partial differential equations on general
geometries},
Journal = {Journal of Computational Physics},
Volume = {216},
Number = {1},
Pages = {216-246},
Publisher = {Elsevier BV},
Year = {2006},
Month = {July},
ISSN = {0021-9991},
url = {http://dx.doi.org/10.1016/j.jcp.2005.11.031},
Abstract = {We extend a recently introduced method for numerically
solving partial differential equations on implicit surfaces
[M. Bertalmío, L.T. Cheng, S. Osher, G. Sapiro. Variational
problems and partial differential equations on implicit
surfaces, J. Comput. Phys. 174 (2) (2001) 759-780] to fourth
order PDEs including the Cahn-Hilliard equation and a
lubrication model for curved surfaces. By representing a
surface in RN as the level set of a smooth function,
φ{symbol}, we compute the PDE using only finite differences
on a standard Cartesian mesh in RN. The higher order
equations introduce a number of challenges that are of less
concern when applying this method to first and second order
PDEs. Many of these problems, such as time-stepping
restrictions and large stencil sizes, are shared by standard
fourth order equations in Euclidean domains, but others are
caused by the extreme degeneracy of the PDEs that result
from this method and the general geometry. We approach these
difficulties by applying convexity splitting methods, ADI
schemes, and iterative solvers. We discuss in detail the
differences between computing these fourth order equations
and computing the first and second order PDEs considered in
earlier work. We explicitly derive schemes for the linear
fourth order diffusion, the Cahn-Hilliard equation for phase
transition in a binary alloy, and surface tension driven
flows on complex geometries. Numerical examples validating
our methods are presented for these flows for data on
general surfaces. © 2005 Elsevier Inc. All rights
reserved.},
Doi = {10.1016/j.jcp.2005.11.031},
Key = {fds265003}
}

@article{fds264783,
Author = {Sapiro, G},
Title = {The composer as intellectual. Music and ideology in France
(1914-1940)},
Journal = {Quinzaine Litteraire},
Number = {926},
Pages = {26-26},
Year = {2006},
Month = {July},
ISSN = {0048-6493},
Key = {fds264783}
}

@article{fds265001,
Author = {Lee, M and Lloyd, P and Zhang, X and Schallhorn, JM and Sugimoto, K and Leach, AG and Sapiro, G and Houk, KN},
Title = {Shapes of antibody binding sites: qualitative and
quantitative analyses based on a geomorphic classification
scheme.},
Journal = {The Journal of Organic Chemistry},
Volume = {71},
Number = {14},
Pages = {5082-5092},
Year = {2006},
Month = {July},
ISSN = {0022-3263},
url = {http://www.ncbi.nlm.nih.gov/pubmed/16808494},
Abstract = {The topography of antibody binding sites has been classified
into five types that evoke familiar geomorphic features of
the Earth. The 229 antibody crystal structures from the
Protein Data Bank were analyzed and classified into these
classes. Relationships to previous topography
classifications by Rees et al., who defined three classes,
and Thornton et al., who defined four classes, are
identified. An algorithm was developed to identify the
antibody binding site class automatically based on the
definition and the shape of the binding site. A
three-dimensional convex hull was formed around the
complementarity determining regions (CDRs) of the antibody.
The convex hull was then "trimmed" to fit the binding site
by using distance criteria and morphological techniques.
Once the program identified the binding site shape, a
statistical and distance based analysis was performed to
classify automatically the antibody into one of the five
geomorphic classes. The five antibody topography classes are
as follows: cave (mostly hapten binders), crater (mostly
protein and peptide/carbohydrate/nucleic acid binders),
canyon, valley, and plain (mostly protein binders).
Comparisons of the binding sites of empty and of complexed
antibody binding sites gave an indication of how the shape
of the binding site is influenced by binding of the
antigen.},
Doi = {10.1021/jo052659z},
Key = {fds265001}
}

@article{fds265000,
Author = {Yatziv, L and Sapiro, G},
Title = {Fast image and video colorization using chrominance
blending.},
Journal = {Ieee Transactions on Image Processing : a Publication of the
Ieee Signal Processing Society},
Volume = {15},
Number = {5},
Pages = {1120-1129},
Year = {2006},
Month = {May},
ISSN = {1057-7149},
url = {http://www.ncbi.nlm.nih.gov/pubmed/16671293},
Abstract = {Colorization, the task of coloring a grayscale image or
video, involves assigning from the single dimension of
intensity or luminance a quantity that varies in three
dimensions, such as red, green, and blue channels. Mapping
between intensity and color is, therefore, not unique, and
colorization is ambiguous in nature and requires some amount
of human interaction or external information. A
computationally simple, yet effective, approach of
colorization is presented in this paper. The method is fast
and it can be conveniently used "on the fly," permitting the
user to interactively get the desired results promptly after
providing a reduced set of chrominance scribbles. Based on
the concepts of luminance-weighted chrominance blending and
fast intrinsic distance computations, high-quality
colorization results for still images and video are obtained
at a fraction of the complexity and computational cost of
previously reported techniques. Possible extensions of the
algorithm introduced here included the capability of
changing the colors of an existing color image or video, as
well as changing the underlying luminance, and many other
special effects demonstrated here.},
Doi = {10.1109/tip.2005.864231},
Key = {fds265000}
}

@article{fds264993,
Author = {Rathi, Y and Olver, P and Sapiro, G and Tannenbaum,
A},
Title = {Affine invariant surface evolutions for 3D image
segmentation},
Journal = {Smart Structures and Materials 2005: Active Materials:
Behavior and Mechanics},
Volume = {6064},
Publisher = {SPIE},
Year = {2006},
Month = {April},
ISSN = {0277-786X},
url = {http://dx.doi.org/10.1117/12.640282},
Abstract = {In this paper we present an algorithm for 3D medical image
segmentation based on an affine invariant flow. The
algorithm is simple to implement and semi-automatic. The
technique is based on active contours evolving in time
according to intrinsic geometric measures of the image. The
surface flow is obtained by minimizing a global energy with
respect to an affine invariant metric. Affine invariant edge
detectors for 3-dimensional objects are also computed which
have the same qualitative behavior as the Euclidean edge
detectors. Results on artificial and real MRI images show
that the algorithm performs well, both in terms of accuracy
and robustness to noise. © 2006 SPIE-IS&T.},
Doi = {10.1117/12.640282},
Key = {fds264993}
}

@article{fds264771,
Author = {Sapiro, G},
Title = {The price of independence},
Journal = {Quinzaine Litteraire},
Number = {919},
Pages = {6-8},
Year = {2006},
Month = {March},
ISSN = {0048-6493},
Key = {fds264771}
}

@article{fds264999,
Author = {Yatziv, L and Bartesaghi, A and Sapiro, G},
Title = {O(N) implementation of the fast marching
algorithm},
Journal = {Journal of Computational Physics},
Volume = {212},
Number = {2},
Pages = {393-399},
Publisher = {Elsevier BV},
Year = {2006},
Month = {March},
url = {http://dx.doi.org/10.1016/j.jcp.2005.08.005},
Abstract = {In this note we present an implementation of the fast
marching algorithm for solving Eikonal equations that in
practice reduces the original run-time from O(N log N) to
linear. This lower run-time cost is obtained while keeping
an error bound of the same order of magnitude as the
original algorithm. This improvement is achieved introducing
the straight forward untidy priority queue, obtained via a
quantization of the priorities in the marching computation.
We present the underlying framework, estimations on the
error, and examples showing the usefulness of the proposed
approach. © 2005 Elsevier Inc. All rights
reserved.},
Doi = {10.1016/j.jcp.2005.08.005},
Key = {fds264999}
}

@article{fds264992,
Author = {Hershkovitz, E and Sapiro, G and Tannenbaum, A and Williams,
LD},
Title = {Statistical analysis of RNA backbone.},
Journal = {Ieee/Acm Transactions on Computational Biology and
Bioinformatics},
Volume = {3},
Number = {1},
Pages = {33-46},
Year = {2006},
Month = {January},
ISSN = {1545-5963},
url = {http://www.ncbi.nlm.nih.gov/pubmed/17048391},
Abstract = {Local conformation is an important determinant of RNA
catalysis and binding. The analysis of RNA conformation is
particularly difficult due to the large number of degrees of
freedom (torsion angles) per residue. Proteins, by
comparison, have many fewer degrees of freedom per residue.
In this work, we use and extend classical tools from
statistics and signal processing to search for clusters in
RNA conformational space. Results are reported both for
scalar analysis, where each torsion angle is separately
studied, and for vectorial analysis, where several angles
are simultaneously clustered. Adapting techniques from
vector quantization and clustering to the RNA structure, we
find torsion angle clusters and RNA conformational motifs.
We validate the technique using well-known conformational
motifs, showing that the simultaneous study of the total
torsion angle space leads to results consistent with known
motifs reported in the literature and also to the finding of
new ones.},
Doi = {10.1109/TCBB.2006.13},
Key = {fds264992}
}

@article{fds264994,
Author = {Mohan, A and Bartesaghi, A and Sapiro, G},
Title = {Constrained regularization of digital terrain elevation
data},
Journal = {Ieee Geoscience and Remote Sensing Letters},
Volume = {3},
Number = {1},
Pages = {59-62},
Publisher = {Institute of Electrical and Electronics Engineers
(IEEE)},
Year = {2006},
Month = {January},
ISSN = {1545-598X},
url = {http://dx.doi.org/10.1109/LGRS.2005.856702},
Abstract = {A framework for geometric regularization of elevation maps
is introduced in this letter. The framework takes into
account errors in the data, which form part of standard
elevation maps specifications, as well as possible
algorithm is based on adapting the theory of geometric
active surfaces to the problem of regularizing elevation
maps. We present the underlying concepts and numerical
experiments showing the effectiveness and potential of this
Doi = {10.1109/LGRS.2005.856702},
Key = {fds264994}
}

@article{fds264750,
Author = {Sapiro, G and Gobille, B},
Title = {Proprietor or intellectual worker ? French writers in search
for a status.},
Journal = {Le Mouvement Social},
Number = {214},
Pages = {113-+},
Year = {2006},
ISSN = {0027-2671},
Key = {fds264750}
}

@article{fds264796,
Author = {Sapiro, G},
Title = {Emigrants in New York. The French intellectuals in Manhattan
(1940-1944).},
Journal = {Le Mouvement Social},
Number = {214},
Pages = {174-175},
Year = {2006},
ISSN = {0027-2671},
Key = {fds264796}
}

@article{fds264807,
Author = {Sapiro, G},
Title = {The intellectual professions between the State,
entrepreneurship and the industry},
Journal = {Le Mouvement Social},
Number = {214},
Pages = {3-18},
Year = {2006},
ISSN = {0027-2671},
Key = {fds264807}
}

@article{fds264822,
Author = {Sapiro, G},
Title = {Intellectuals. The genre in the history of
intellectuals.},
Journal = {Le Mouvement Social},
Number = {214},
Pages = {168-170},
Year = {2006},
ISSN = {0027-2671},
Key = {fds264822}
}

@article{fds264851,
Author = {Hershkovitz, E and Sapiro, G and Tannenbaum, AR and Williams,
LD},
Title = {Statistical Analysis of RNA Backbone.},
Journal = {Ieee/Acm Trans. Comput. Biology Bioinform.},
Volume = {3},
Pages = {33-46},
Year = {2006},
url = {http://dx.doi.org/10.1145/1113896.1113900},
Doi = {10.1145/1113896.1113900},
Key = {fds264851}
}

@article{fds264731,
Author = {Caselles, V and Kimmel, R and Sapiro, G},
Title = {Geometric Active Contours for Image Segmentation},
Pages = {613-627},
Publisher = {Elsevier},
Year = {2005},
Month = {December},
url = {http://dx.doi.org/10.1016/B978-012119792-6/50099-1},
Abstract = {This chapter deals with an efficient and accurate approach
in image segmentation: active contours. The general idea
behind this technique is to apply partial differential
equations (PDEs) to deform a curve or a surface toward the
boundaries of the objects of interest in the image. The
deformation is driven by the forces that use information
about the objects of interest in the data. In particular,
this chapter describes the ideas that have emerged from the
geodesic active contours framework, focusing on some of the
main models and referring to the literature for other
applications. This is an example of using PDEs for image
processing and analysis. In this case, such PDEs are derived
as gradient-descent processes from geometric integral
measures. This research field considers images as continuous
geometric structures and enables the use of continuous
mathematics such as PDEs and differential geometry. The
chapter also discusses the computer image processing
algorithms that are actually the numeric implementations of
the resulting equations. © 2005 Elsevier Inc. All rights
reserved.},
Doi = {10.1016/B978-012119792-6/50099-1},
Key = {fds264731}
}

@article{fds264984,
Author = {Breen, D and Kirby, M and Lefohn, A and Museth, K and Preusser, T and Sapiro, G and Whitaker, R},
Title = {Level set and PDE methods for visualization},
Journal = {Proceedings of the Ieee Visualization Conference},
Pages = {125},
Publisher = {IEEE},
Year = {2005},
Month = {December},
url = {http://dx.doi.org/10.1109/VIS.2005.65},
Abstract = {Level set methods, an important class of partial
differential equation (PDE) methods, define dynamic surfaces
implicitly as the level set (iso-surface) of a sampled,
evolving nD function. This course is targeted for
researchers interested in learning about level set and other
PDE-based methods, and their application to visualization.
The course material will be presented by several of the
recognized experts in the field, and will include
introductory concepts, practical considerations and
extensive details on a variety of level set/PDE
applications. The course will begin with preparatory
material that introduces the concept of using partial
differential equations to solve problems in visualization.
This will include the structure and behavior of several
different types of differential equations, e.g. the level
set, heat and reaction-diffusion equations, as well as a
general approach to developing PDE-based applications. The
second stage of the course will describe the numerical
methods and algorithms needed to implement the mathematics
and methods presented in the first stage, including
information on implementing the algorithms on GPUs.
Throughout the course the technical material will be tied to
applications, e.g. image processing, geometric modeling,
dataset segmentation, model processing, surface
reconstruction, anisotropic geometric diffusion, flow field
post-processing and vector visualization.},
Doi = {10.1109/VIS.2005.65},
Key = {fds264984}
}

@article{fds264985,
Author = {Sapiro, G},
Title = {Inpainting the colors},
Journal = {Proceedings International Conference on Image Processing,
Icip},
Volume = {2},
Pages = {698-701},
Publisher = {IEEE},
Year = {2005},
Month = {December},
ISSN = {1522-4880},
url = {http://dx.doi.org/10.1109/ICIP.2005.1530151},
Abstract = {A framework for automatic image colorization, the art of
adding color to a monochrome image or movie, is presented in
this paper. The approach is based on considering the
geometry and structure of the monochrome luminance input,
given by its gradient information, as representing the
geometry and structure of the whole colored version. The
color is then obtained by solving a partial differential
equation that propagates a few color scribbles provided by
the user or by side information, while considering the
gradient information brought in by the monochrome data. This
way, the color is inpainted, constrained both by the
monochrome image geometry and the provided color samples. We
present the underlying framework and examples for still
images and movies. © 2005 IEEE.},
Doi = {10.1109/ICIP.2005.1530151},
Key = {fds264985}
}

@article{fds264986,
Author = {Bartesaghi, A and Sapiro, G},
Title = {Tracking of moving objects under severe and total
occlusions},
Journal = {Proceedings International Conference on Image Processing,
Icip},
Volume = {1},
Pages = {301-304},
Publisher = {IEEE},
Year = {2005},
Month = {December},
ISSN = {1522-4880},
url = {http://dx.doi.org/10.1109/ICIP.2005.1529747},
Abstract = {We present an algorithm for tracking moving objects using
intrinsic minimal surfaces which handles particularly well
the presence of severe and total occlusions even in the
presence of weak object boundaries. We adopt an edge based
approach and find the segmentation as a minimal surface in
3D space-time, the metric being dictated by the image
gradient. Object boundaries are represented implicitly as
the level set of a higher dimensional function, and no
particular object model is assumed. We also avoid explicit
estimation of a dynamic model since the problem is regarded
as one of static energy minimization. A set of interior
points provided by the user is used to constrain the
optimization, which basically corresponds to selecting the
object of interest within the video sequence. The
constraints are such that they restrict the resulting
surface to be star-shaped in the 3D spatio-temporal space.
We present some challenging examples that show the
robustness of the technique. © 2005 IEEE.},
Doi = {10.1109/ICIP.2005.1529747},
Key = {fds264986}
}

@article{fds264987,
Author = {Patwardhan, KA and Sapiro, G and Benalmio, M},
Title = {Video inpainting of occluding and occluded
objects},
Journal = {Proceedings International Conference on Image Processing,
Icip},
Volume = {2},
Pages = {69-72},
Publisher = {IEEE},
Year = {2005},
Month = {December},
ISSN = {1522-4880},
url = {http://dx.doi.org/10.1109/ICIP.2005.1529993},
Abstract = {We present a basic technique to fill-in missing parts of a
video sequence taken from a static camera. Two important
cases are considered. The first case is concerned with the
removal of non-stationary objects that occlude stationary
background. We use a priority based spatio-temporal
synthesis scheme for inpainting the stationary background.
The second and more difficult case involves filling-in
moving objects when they are partially occluded. For this,
we propose a priority scheme to first inpaint the occluded
moving objects and then fill-in the remaining area with
stationary background using the method proposed for the
first case. We use as input an optical-flow based mask,
which tells if an undamaged pixel is moving or is
stationary. The moving object is inpainted by copying
patches from undamaged frames, and this copying is
independent of the background of the moving object in either
frame. This work has applications in a variety of different
areas, including video special effects and restoration and
enhancement of damaged videos. The examples shown in the
paper illustrate these ideas. © 2005 IEEE.},
Doi = {10.1109/ICIP.2005.1529993},
Key = {fds264987}
}

@article{fds264990,
Author = {Martín, A and Sapiro, G and Seroussi, G},
Title = {Is image steganography natural?},
Journal = {Ieee Transactions on Image Processing : a Publication of the
Ieee Signal Processing Society},
Volume = {14},
Number = {12},
Pages = {2040-2050},
Year = {2005},
Month = {December},
ISSN = {1057-7149},
url = {http://www.ncbi.nlm.nih.gov/pubmed/16370457},
Abstract = {Steganography is the art of secret communication. Its
purpose is to hide the presence of information, using, for
example, images as covers. We experimentally investigate if
stego-images, bearing a secret message, are statistically
"natural." For this purpose, we use recent results on the
statistics of natural images and investigate the effect of
some popular steganography techniques. We found that these
fundamental statistics of natural images are, in fact,
generally altered by the hidden "nonnatural" information.
Frequently, the change is consistently biased in a given
direction. However, for the class of natural images
considered, the change generally falls within the intrinsic
variability of the statistics, and, thus, does not allow for
reliable detection, unless knowledge of the data hiding
process is taken into account. In the latter case,
significant levels of detection are demonstrated.},
Doi = {10.1109/tip.2005.859370},
Key = {fds264990}
}

@article{fds264991,
Author = {Mahmoudi, M and Sapiro, G},
Title = {Fast image and video denoising via nonlocal means of similar
neighborhoods},
Journal = {Ieee Signal Processing Letters},
Volume = {12},
Number = {12},
Pages = {839-842},
Publisher = {Institute of Electrical and Electronics Engineers
(IEEE)},
Year = {2005},
Month = {December},
ISSN = {1070-9908},
url = {http://dx.doi.org/10.1109/LSP.2005.859509},
Abstract = {In this letter, improvements to the nonlocal means image
denoising method introduced by Buades et al. are presented.
The original nonlocal means method replaces a noisy pixel by
the weighted average of pixels with related surrounding
neighborhoods. While producing state-of-the-art denoising
results, this method is computationally impractical. In
order to accelerate the algorithm, we introduce filters that
eliminate unrelated neighborhoods from the weighted average.
These filters are based on local average gray values and
gradients, preclassifying neighborhoods and thereby reducing
the original quadratic complexity to a linear one and
reducing the influence of less-related areas in the
denoising of a given pixel. We present the underlying
framework and experimental results for gray level and color
images as well as for video. © 2005 IEEE.},
Doi = {10.1109/LSP.2005.859509},
Key = {fds264991}
}

@article{fds264982,
Author = {Mémoli, F and Sapiro, G},
Title = {Distance functions and geodesics on submanifolds of ℝ
d and point clouds},
Journal = {Siam Journal on Applied Mathematics},
Volume = {65},
Number = {4},
Pages = {1227-1260},
Publisher = {Society for Industrial & Applied Mathematics
(SIAM)},
Year = {2005},
Month = {September},
ISSN = {0036-1399},
url = {http://dx.doi.org/10.1137/S003613990342877X},
Abstract = {A theoretical and computational framework for computing
intrinsic distance functions and geodesics on submanifolds
of ℝ d given by point clouds is introduced and developed
in this paper. The basic idea is that, as shown here,
intrinsic distance functions and geodesics on general
co-dimension submanifolds of ℝ d can be accurately
approximated by extrinsic Euclidean ones computed inside a
thin offset band surrounding the manifold. This permits the
use of computationally optimal algorithms for computing
distance functions in Cartesian grids. We use these
algorithms, modified to deal with spaces with boundaries,
and obtain a computationally optimal approach also for the
case of intrinsic distance functions on submanifolds of ℝ
d. For point clouds, the offset band is constructed without
the need to explicitly find the underlying manifold, thereby
computing intrinsic distance functions and geodesics on
point clouds while skipping the manifold reconstruction
step. The case of point clouds representing noisy samples of
a submanifold of Euclidean space is studied as well. All the
underlying theoretical results are presented along with
experimental examples for diverse applications and
comparisons to graph-based distance algorithms. © 2005
Society for Industrial and Applied Mathematics.},
Doi = {10.1137/S003613990342877X},
Key = {fds264982}
}

@article{fds264989,
Author = {Bartesaghi, A and Sapiro, G and Subramaniam, S},
Title = {An energy-based three-dimensional segmentation approach for
the quantitative interpretation of electron
tomograms.},
Journal = {Ieee Transactions on Image Processing : a Publication of the
Ieee Signal Processing Society},
Volume = {14},
Number = {9},
Pages = {1314-1323},
Year = {2005},
Month = {September},
ISSN = {1057-7149},
url = {http://www.ncbi.nlm.nih.gov/pubmed/16190467},
Abstract = {Electron tomography allows for the determination of the
three-dimensional structures of cells and tissues at
resolutions significantly higher than that which is possible
with optical microscopy. Electron tomograms contain, in
principle, vast amounts of information on the locations and
architectures of large numbers of subcellular assemblies and
organelles. The development of reliable quantitative
approaches for the analysis of features in tomograms is an
important problem, and a challenging prospect due to the low
signal-to-noise ratios that are inherent to biological
electron microscopic images. This is, in part, a consequence
of the tremendous complexity of biological specimens. We
report on a new method for the automated segmentation of HIV
particles and selected cellular compartments in electron
tomograms recorded from fixed, plastic-embedded sections
derived from HIV-infected human macrophages. Individual
features in the tomogram are segmented using a novel robust
algorithm that finds their boundaries as global minimal
surfaces in a metric space defined by image features. The
optimization is carried out in a transformed spherical
domain with the center an interior point of the particle of
interest, providing a proper setting for the fast and
accurate minimization of the segmentation energy. This
method provides tools for the semi-automated detection and
statistical evaluation of HIV particles at different stages
of assembly in the cells and presents opportunities for
correlation with biochemical markers of HIV infection. The
segmentation algorithm developed here forms the basis of the
automated analysis of electron tomograms and will be
especially useful given the rapid increases in the rate of
data acquisition. It could also enable studies of much
larger data sets, such as those which might be obtained from
the tomographic analysis of HIV-infected cells from studies
of large populations.},
Doi = {10.1109/TIP.2005.852467},
Key = {fds264989}
}

@article{fds264988,
Author = {Bartesaghi, A and Sapiro, G and Malzbender, T and Gelb,
D},
Title = {Three-dimensional shape rendering from multiple
images},
Journal = {Graphical Models},
Volume = {67},
Number = {4},
Pages = {332-346},
Publisher = {Elsevier BV},
Year = {2005},
Month = {July},
url = {http://dx.doi.org/10.1016/j.gmod.2005.02.002},
Abstract = {A paradigm for automatic three-dimensional shape and
geometry rendering from multiple images is introduced in
this paper. In particular, non-photorealistic rendering
(NPR) techniques in the style of pen-and-ink illustrations
are addressed, while the underlying presented ideas can be
used in other modalities, such as halftoning, as well.
Existing NPR approaches can be categorized in two groups
depending on the type of input they use: image based and
object based. Using multiple images as input to the NPR
scheme, we propose a novel hybrid model that simultaneously
uses information from the image and object domains. The
benefit not only comes from combining the features of each
approach, it also minimizes the need for manual or user
assisted tasks in extracting scene features and geometry, as
employed in virtually all state-of-the-art NPR approaches.
As particular examples we use input images from binocular
stereo and multiple-light photometric stereo systems. From
the image domain we extract the tonal information to be
mimicked by the NPR synthesis algorithm, and from the object
domain we extract the geometry, mainly principal directions,
obtained from the image set without explicitly using 3D
models, to convey shape to the drawings. We describe a
particular implementation of such an hybrid system and
present a number of automatically generated pen-and-ink
style drawings. This work then shows how to use and extend
well-developed techniques in computer vision to address
fundamental problems in shape representation and rendering.
Doi = {10.1016/j.gmod.2005.02.002},
Key = {fds264988}
}

@article{Memoli2005,
Author = {Mémoli, F and Sapiro, G},
Title = {A theoretical and computational framework for isometry
invariant recognition of point cloud data},
Journal = {Foundations of Computational Mathematics},
Volume = {5},
Number = {3},
Pages = {313-347},
Publisher = {Springer Nature},
Year = {2005},
Month = {July},
ISSN = {1615-3375},
url = {http://dx.doi.org/10.1007/s10208-004-0145-y},
Abstract = {Point clouds are one of the most primitive and fundamental
manifold representations. Popular sources of point clouds
are three-dimensional shape acquisition devices such as
laser range scanners. Another important field where point
clouds are found is in the representation of
high-dimensional manifolds by samples. With the increasing
popularity and very broad applications of this source of
data, it is natural and important to work directly with this
representation, without having to go through the
intermediate and sometimes impossible and distorting steps
of surface reconstruction. A geometric framework for
comparing manifolds given by point clouds is presented in
this paper. The underlying theory is based on
Gromov-Hausdorff distances, leading to isometry invariant
and completely geometric comparisons. This theory is
embedded in a probabilistic setting as derived from random
sampling of manifolds, and then combined with results on
matrices of pairwise geodesic distances to lead to a
computational implementation of the framework. The
theoretical and computational results presented here are
complemented with experiments for real three-dimensional
Doi = {10.1007/s10208-004-0145-y},
Key = {Memoli2005}
}

@article{fds264803,
Author = {Bartesaghi, A and Sapiro, G},
Title = {Tracking of moving objects under severe and total
occlusions},
Journal = {Proceedings International Conference on Image Processing,
Icip},
Pages = {249-252},
Publisher = {IEEE},
Year = {2005},
Month = {January},
ISBN = {0-7803-9134-9},
ISSN = {1522-4880},
Key = {fds264803}
}

@article{fds264802,
Author = {Sapiro, G and IEEE},
Title = {Inpainting the colors},
Journal = {Proceedings International Conference on Image Processing,
Icip},
Pages = {1265-1268},
Year = {2005},
ISBN = {0-7803-9134-9},
ISSN = {1522-4880},
Key = {fds264802}
}

@article{fds264813,
Author = {Patwardhan, KA and Sapiro, G and Bertalmio, M and IEEE},
Title = {Video inpainting of occluding and occluded
objects},
Journal = {Proceedings International Conference on Image Processing,
Icip},
Pages = {1593-1596},
Year = {2005},
ISBN = {0-7803-9134-9},
ISSN = {1522-4880},
Key = {fds264813}
}

@article{fds264974,
Author = {Bartesaghi, A and Sapiro, G and Lee, S and Lefman, J and Wahl, S and Orenstein, J and Subramaniam, S},
Title = {A new approach for 3D segmentation of cellular tomograms
obtained using three-dimensional electron
microscopy},
Journal = {2004 2nd Ieee International Symposium on Biomedical Imaging:
Macro to Nano},
Volume = {1},
Pages = {5-8},
Year = {2004},
Month = {December},
Abstract = {Electron tomography allows determination of the
three-dimensional structures of cells and tissues at
resolutions significantly higher than is possible with
optical microscopy. Electron tomograms contain, in
principle, vast amounts of information on the locations and
architectures of large numbers of subcellular assemblies and
organelles. The development of reliable quantitative
approaches for interpretation of features in tomograms, is
an important problem, but is a challenging prospect because
of the low signal-to-noise ratios that are inherent to
biological electron microscopic images. As a first step in
this direction, we report methods for the automated
statistical analysis of HIV particles and selected cellular
compartments in electron tomograms recorded from fixed,
plastic-embedded sections derived from HIV-infected human
macrophages. Individual features in the tomogram are
segmented using a novel, robust algorithm that finds their
boundaries as global minimal surfaces in a metric space
defined by image features. Our expectation is that such
methods will provide tools for semi-automated detection and
statistical evaluation of HIV particles at different stages
of assembly in the cells, and present opportunities for
correlation with biochemical markers of HIV infection.
Key = {fds264974}
}

@article{fds264975,
Author = {Yatziv, L and Sapiro, G and Levoy, M},
Title = {Lightfield completion},
Journal = {Proceedings International Conference on Image Processing,
Icip},
Volume = {3},
Pages = {1787-1790},
Publisher = {IEEE},
Year = {2004},
Month = {December},
ISSN = {1522-4880},
url = {http://dx.doi.org/10.1109/ICIP.2004.1421421},
Abstract = {A light field is a 4D function representing radiance as a
function of ray position and direction in 3D space. In this
paper we describe a method for recovering gaps in light
fields of scenes that contain significant occluders. In
these situations, although a large fraction of the scene may
be blocked in any one view, most scene points are visible in
at least some views. As a consequence, although too much
information is missing to employ 2D completion methods that
operate within a single view, it may be possible to recover
the lost information by completion in 4D-the full
dimensionality of the light field. The proposed light field
completion method has three main steps: Registration,
initial estimation, and high dimensional texture synthesis
and/or inpainting. At the registration stage, the set of
images are shifted and re-projected so that the
corresponding pixels from different images are aligned in
the reconstructed light field. Following this, the
estimation step uses a naive technique to fill-in parts of
gaps using the available information from the multiple
images. This serves as the initial condition for the next
and last step, where the missing information is recovered
via high dimensional texture synthesis and/or inpainting.
These two steps of initial condition and completion are
iterated. The algorithm is illustrated with real examples.
Doi = {10.1109/ICIP.2004.1421421},
Key = {fds264975}
}

@article{fds264976,
Author = {Patwardhan, KA and Sapiro, G},
Title = {Automatic image decomposition},
Journal = {Proceedings International Conference on Image Processing,
Icip},
Volume = {1},
Pages = {645-648},
Publisher = {IEEE},
Year = {2004},
Month = {December},
ISSN = {1522-4880},
url = {http://dx.doi.org/10.1109/ICIP.2004.1418837},
Abstract = {The decomposition of an image into its primitive components,
such as cartoon plus texture, is a fundamental problem in
image processing. In [11, 16], the authors proposed a
technique to achieve this decomposition into structure and
texture. These two components are competing ones, and their
proposed model has a critical parameter that controls this
decomposition. In this paper we show how to automatically
select this parameter, and demonstrate with examples the
importance of this optimal selection. ©2004
IEEE.},
Doi = {10.1109/ICIP.2004.1418837},
Key = {fds264976}
}

@article{fds264977,
Author = {Mémoli, F and Sapiro, G},
Title = {Comparing point clouds},
Journal = {Acm International Conference Proceeding Series},
Volume = {71},
Pages = {32-40},
Publisher = {ACM Press},
Year = {2004},
Month = {December},
url = {http://dx.doi.org/10.1145/1057432.1057436},
Abstract = {Point clouds are one of the most primitive and fundamental
surface representations. A popular source of point clouds
are three dimensional shape acquisition devices such as
laser range scanners. Another important field where point
clouds are found is in the representation of
high-dimensional manifolds by samples. With the increasing
popularity and very broad applications of this source of
data, it is natural and important to work directly with this
representation, without having to go to the intermediate and
sometimes impossible and distorting steps of surface
reconstruction. A geometric framework for comparing
manifolds given by point clouds is presented in this paper.
The underlying theory is based on Gromov-Hausdorff
distances, leading to isometry invariant and completely
geometric comparisons. This theory is embedded in a
probabilistic setting as derived from random sampling of
manifolds, and then combined with results on matrices of
pairwise geodesic distances to lead to a computational
implementation of the framework. The theoretical and
computational results here presented are complemented with
experiments for real three dimensional shapes. © The
Eurographics Association 2004.},
Doi = {10.1145/1057432.1057436},
Key = {fds264977}
}

@article{fds264978,
Author = {Bartesaghi, A and Sapiro, G and Malzbender, T and Gelb,
D},
Title = {Non-photorealistic rendering from multiple
images},
Journal = {Proceedings International Conference on Image Processing,
Icip},
Volume = {4},
Pages = {2403-2406},
Publisher = {IEEE},
Year = {2004},
Month = {December},
ISSN = {1522-4880},
url = {http://dx.doi.org/10.1109/ICIP.2004.1421585},
Abstract = {A new paradigm for automatic non-photorealistic rendering
(NPR) is introduced in this paper. Existing NPR approaches
can be categorized in two groups depending on the type of
input they use: image based and object based. Using multiple
images as input to the NPR scheme, we propose a novel hybrid
model that simultaneously uses information from the image
and object domains. The benefit not only comes from
combining the features of each approach, but most important,
it minimizes the need for manual or user assisted tasks in
extracting scene features and geometry, as employed in
virtually all state-of-the-art NPR approaches. We describe a
particular implementation of such an hybrid system and
present a number of automatically generated pen-and-ink
style drawings. This work then shows how to use and extend
well developed techniques in computer vision to address
fundamental problems in image representation and rendering.
Doi = {10.1109/ICIP.2004.1421585},
Key = {fds264978}
}

@article{fds264983,
Author = {Niethammer, M and Betelu, S and Sapiro, G and Tannenbaum, A and Giblin,
PJ},
Title = {Area-Based Medial Axis of Planar Curves.},
Journal = {International Journal of Computer Vision},
Volume = {60},
Number = {3},
Pages = {203-224},
Year = {2004},
Month = {December},
url = {http://dx.doi.org/10.1023/B:VISI.0000036835.28674.d0},
Abstract = {A new definition of affine invariant medial axis of planar
closed curves is introduced. A point belongs to the affine
medial axis if and only if it is equidistant from at least
two points of the curve, with the distance being a minimum
and given by the areas between the curve and its
corresponding chords. The medial axis is robust, eliminating
the need for curve denoising. In a dynamical interpretation
of this affine medial axis, the medial axis points are the
affine shock positions of the affine erosion of the curve.
We propose a simple method to compute the medial axis and
give examples. We also demonstrate how to use this method to
detect affine skew symmetry in real images.},
Doi = {10.1023/B:VISI.0000036835.28674.d0},
Key = {fds264983}
}

@article{fds264979,
Author = {Solé, A and Caselles, V and Sapiro, G and Arándiga,
F},
Title = {Morse description and geometric encoding of digital
elevation maps.},
Journal = {Ieee Transactions on Image Processing : a Publication of the
Ieee Signal Processing Society},
Volume = {13},
Number = {9},
Pages = {1245-1262},
Year = {2004},
Month = {September},
ISSN = {1057-7149},
url = {http://www.ncbi.nlm.nih.gov/pubmed/15449586},
Abstract = {Two complementary geometric structures for the topographic
representation of an image are developed in this work. The
first one computes a description of the Morse-topological
structure of the image, while the second one computes a
simplified version of its drainage structure. The
topographic significance of the Morse and drainage
structures of digital elevation maps (DEMs) suggests that
they can been used as the basis of an efficient encoding
scheme. As an application, we combine this geometric
representation with an interpolation algorithm and lossless
data compression schemes to develop a compression scheme for
DEMs. This algorithm achieves high compression while
controlling the maximum error in the decoded elevation map,
a property that is necessary for the majority of
applications dealing with DEMs. We present the underlying
theory and compression results for standard DEM
data.},
Doi = {10.1109/tip.2004.832864},
Key = {fds264979}
}

@article{fds264980,
Author = {Tsai, YHR and Cheng, LT and Osher, S and Burchard, P and Sapiro,
G},
Title = {Visibility and its dynamics in a PDE based implicit
framework},
Journal = {Journal of Computational Physics},
Volume = {199},
Number = {1},
Pages = {260-290},
Publisher = {Elsevier BV},
Year = {2004},
Month = {September},
url = {http://dx.doi.org/10.1016/j.jcp.2004.02.015},
Abstract = {We investigate the problem of determining visible regions
given a set of (moving) obstacles and a (moving) vantage
point. Our approach to this problem is through an implicit
framework, where the obstacles are represented by a level
set function. The visibility problem is formally formulated
as a boundary value problem (BVP) of a first order partial
differential equation. It is based on the continuation of
values along the given ray field. We propose a one-pass,
multi-level algorithm for the construction of the solution
on a grid. Furthermore, we study the dynamics of shadow
boundaries on the surfaces of the obstacles when the vantage
point moves along a given trajectory. In all of these
situations, topological changes such as merging and breaking
occur in the regions of interest. These are automatically
handled by the level set framework proposed here. Finally,
we obtain additional useful information through simple
operations in the level set framework. © 2004 Elsevier Inc.
Doi = {10.1016/j.jcp.2004.02.015},
Key = {fds264980}
}

@article{fds322692,
Author = {Breen, D and Fedkiw, R and Museth, K and Osher, S and Sapiro, G and Whitaker, R},
Title = {Level set and PDE methods for computer graphics},
Journal = {Acm Siggraph 2004 Course Notes, Siggraph
2004},
Publisher = {ACM Press},
Year = {2004},
Month = {August},
ISBN = {0111456789},
url = {http://dx.doi.org/10.1145/1103900.1103928},
Abstract = {Level set methods, an important class of partial
differential equation (PDE) methods, define dynamic surfaces
implicitly as the level set (iso-surface) of a sampled,
evolving nD function. The course begins with preparatory
material that introduces the concept of using partial
differential equations to solve problems in computer
graphics, geometric modeling and computer vision. This will
include the structure and behavior of several different
types of differential equations, e.g. the level set equation
and the heat equation, as well as a general approach to
developing PDE-based applications. The second stage of the
course will describe the numerical methods and algorithms
needed to actually implement the mathematics and methods
presented in the first stage. The course closes with
detailed presentations on several level set/PDE
applications, including image/video inpainting, pattern
formation, image/volume processing, 3D shape reconstruction,
image/volume segmentation, image/shape morphing, geometric
modeling, anisotropic diffusion, and natural phenomena
simulation.},
Doi = {10.1145/1103900.1103928},
Key = {fds322692}
}

@article{fds264769,
Author = {Sapiro, G},
Title = {Das französische literarische Feld: Struktur, Dynamik und
Formen der Politisierung},
Journal = {Berliner Journal Für Soziologie},
Volume = {14},
Number = {2},
Pages = {157-171},
Publisher = {Springer Science and Business Media LLC},
Year = {2004},
Month = {June},
ISSN = {0863-1808},
Doi = {10.1007/bf03204702},
Key = {fds264769}
}

@article{fds264981,
Author = {Mémoli, F and Sapiro, G and Osher, S},
Title = {Solving variational problems and partial differential
equations mapping into general target manifolds},
Journal = {Journal of Computational Physics},
Volume = {195},
Number = {1},
Pages = {263-292},
Publisher = {Elsevier BV},
Year = {2004},
Month = {March},
url = {http://dx.doi.org/10.1016/j.jcp.2003.10.007},
Abstract = {A framework for solving variational problems and partial
differential equations that define maps onto a given generic
manifold is introduced in this paper. We discuss the
framework for arbitrary target manifolds, while the domain
manifold problem was addressed in [J. Comput. Phys. 174(2)
(2001) 759]. The key idea is to implicitly represent the
target manifold as the level-set of a higher dimensional
function, and then implement the equations in the Cartesian
coordinate system where this embedding function is defined.
In the case of variational problems, we restrict the search
of the minimizing map to the class of maps whose target is
the level-set of interest. In the case of partial
differential equations, we re-write all the equation's
geometric characteristics with respect to the embedding
function. We then obtain a set of equations that, while
defined on the whole Euclidean space, are intrinsic to the
implicitly defined target manifold and map into it. This
permits the use of classical numerical techniques in
Cartesian grids, regardless of the geometry of the target
manifold. The extension to open surfaces and submanifolds is
addressed in this paper as well. In the latter case, the
submanifold is defined as the intersection of two higher
dimensional hypersurfaces, and all the computations are
restricted to this intersection. Examples of the
applications of the framework here described include
harmonic maps in liquid crystals, where the target manifold
is a hypersphere; probability maps, where the target
manifold is a hyperplane; chroma enhancement; texture
mapping; and general geometric mapping between high
dimensional manifolds. © 2003 Elsevier Inc. All rights
reserved.},
Doi = {10.1016/j.jcp.2003.10.007},
Key = {fds264981}
}

@article{fds264777,
Author = {Caselles, V and Sapiro, G and Solé, A and Ballester,
C},
Title = {Morse description and morphological encoding of continuous
data},
Journal = {Multiscale Modeling & Simulation},
Volume = {2},
Number = {2},
Pages = {179-209},
Publisher = {Society for Industrial & Applied Mathematics
(SIAM)},
Year = {2004},
Month = {January},
ISSN = {1540-3459},
Abstract = {© 2004 Society for Industrial and Applied Mathematics. A
geometric representation for images is studied in this work.
This is based on two complementary geometric structures for
the topographic representation of an image. The first one
computes a description of the Morse structure, while the
second one computes a simplified version of drainage
structures. The topographic significance of the Morse and
drainage structures of digital elevation maps (DEMs)
suggests that they can been used as the basis of an
efficient encoding scheme. As an application we then combine
this geometric representation with a consistent
interpolation algorithm and lossless data compression
schemes to develop an efficient compression algorithm for
DEMs. This coding scheme controls the L∞ error in the
decoded elevation map, a property that is necessary for the
majority of applications dealing with DEMs. We present the
underlying theory and some compression results for standard
DEM data.},
Doi = {10.1137/S1540345902416557},
Key = {fds264777}
}

@article{fds264972,
Author = {Mémoli, F and Sapiro, G and Thompson, P},
Title = {Implicit brain imaging.},
Journal = {Neuroimage},
Volume = {23 Suppl 1},
Pages = {S179-S188},
Year = {2004},
Month = {January},
ISSN = {1053-8119},
url = {http://www.ncbi.nlm.nih.gov/pubmed/15501087},
Abstract = {We describe how implicit surface representations can be used
to solve fundamental problems in brain imaging. This kind of
representation is not only natural following the
state-of-the-art segmentation algorithms reported in the
literature to extract the different brain tissues, but it is
also, as shown in this paper, the most appropriate one from
the computational point of view. Examples are provided for
finding constrained special curves on the cortex, such as
sulcal beds, regularizing surface-based measures, such as
cortical thickness, and for computing warping fields between
surfaces such as the brain cortex. All these result from
efficiently solving partial differential equations (PDEs)
and variational problems on surfaces represented in implicit
form. The implicit framework avoids the need to construct
intermediate mappings between 3-D anatomical surfaces and
parametric objects such planes or spheres, a complex step
that introduces errors and is required by many other
cortical processing approaches.},
Doi = {10.1016/j.neuroimage.2004.07.072},
Key = {fds264972}
}

@article{fds264794,
Author = {Sole, A and Caselles, V and Sapiro, G and Arandiga,
F},
Title = {Morse description and geometric encoding of digital
elevation maps},
Journal = {Free Boundary Problems: Theory and Applications},
Volume = {147},
Pages = {303-312},
Year = {2004},
ISBN = {3-7643-2193-8},
Key = {fds264794}
}

@article{fds264961,
Author = {Pichon, E and Niethammer, M and Sapiro, G},
Title = {Color histogram equalization through mesh
deformation},
Journal = {Ieee International Conference on Image Processing},
Volume = {2},
Pages = {117-120},
Year = {2003},
Month = {December},
Abstract = {In this paper we propose an extension of grayscale histogram
equalization for color images. For aesthetic reasons,
previously proposed color histogram equalization techniques
do not generate uniform color histograms. Our method will
always generate an almost uniform color histogram thus
making an optimal use of the color space. This is
particularly interesting for pseudo-color scientific
visualization. The method is based on deforming a mesh in
color space to fit the existing histogram and then map it to
a uniform histogram. It is a natural extension of grayscale
histogram equalization and it can be applied to spatial and
color space of any dimension.},
Key = {fds264961}
}

@article{fds264964,
Author = {Verdera, J and Caselles, V and Bertalmio, M and Sapiro,
G},
Title = {Inpainting surface holes},
Journal = {Ieee International Conference on Image Processing},
Volume = {2},
Pages = {903-906},
Year = {2003},
Month = {December},
Abstract = {An algorithm for filling-in surface holes is introduced in
this paper. The basic idea is to represent the surface of
interest in implicit form, and fill-in the holes with a
system of geometric partial differential equations derived
from image inpainting algorithms. The framework and examples
with synthetic and real data are presented.},
Key = {fds264964}
}

@article{fds264966,
Author = {Solé, A and Caselles, V and Sapiro, G and Arándiga,
F},
Title = {Morse description and geometric encoding of DEM
data},
Journal = {Ieee International Conference on Image Processing},
Volume = {2},
Pages = {235-238},
Year = {2003},
Month = {December},
Abstract = {Two complementary geometric structures for the topographic
representation of an image are developed in this work. The
first one computes a description of the Morse structure of
the image, while the second one computes a simplified
version of its drainage structure. The topographic
significance of the Morse and drainage structures of Digital
Elevation Maps (DEM) suggests that they can been used as the
basis of an efficient encoding scheme. We combine this
geometric representation with an interpolation algorithm and
loss-less data compression schemes to develop a compression
scheme for DEM. This algorithm permits to obtain compression
results while controlling the maximum error in the decoded
elevation map, a property that is necessary for the majority
of applications dealing with DEM.},
Key = {fds264966}
}

@article{fds264962,
Author = {Bertalmio, M and Vese, L and Sapiro, G and Osher,
S},
Title = {Image filling-in in a decomposition space},
Journal = {Ieee International Conference on Image Processing},
Volume = {1},
Pages = {853-855},
Year = {2003},
Month = {December},
Abstract = {An algorithm for the simultaneous filling-in of texture and
structure in regions of missing image information is
presented in this paper. The basic idea is to first
decompose the image into the sum of two functions with
different basic characteristics, and then reconstruct each
one of these functions separately with structure and texture
filling-in algorithms. The first function used in the
decomposition is of bounded variation, representing the
underlying image structure, while the second function
captures the texture and possible noise. The region of
missing information in the bounded variation image is
reconstructed using image inpainting algorithms, while the
same region in the texture image is filled-in with texture
synthesis techniques. The original image is then
reconstructed adding back these two sub-images. The novel
contribution of this paper is then in the combination of
these three previously developed components, image
decomposition with inpainting and texture synthesis, which
permits the simultaneous use of filling-in algorithms that
are suited for different image characteristics. The novelty
in the approach is to perform filling-in in a domain
different from the original given image space. Examples on
real images show the advantages of this proposed
approach.},
Key = {fds264962}
}

@article{fds264963,
Author = {Patwardhan, KA and Sapiro, G},
Title = {Projection based image and video inpainting using
wavelets},
Journal = {Ieee International Conference on Image Processing},
Volume = {1},
Pages = {857-860},
Year = {2003},
Month = {December},
Abstract = {In this paper, we present a technique for automatic color
image inpainting, the art of modifying an image-region in a
non-detectable form. The main algorithm is based on the
theory of projections onto convex sets (POCS). The image and
its wavelet transform are projected onto each other after
applying suitable constraints in each domain. This technique
exploits the frequency-spatial representation provided by
wavelets and utilizes the correlation between the damaged
area in the image and its neighborhood. The resulting
restored area is homogeneous with its surrounding and
preserves the aesthetics of the image. The same technique is
used for simple video restoration problems. Video frames are
stacked and treated as a 3-D volume, making a natural use of
inter-frame correlation.},
Key = {fds264963}
}

@article{fds264763,
Author = {Sapiro, G},
Title = {Forms of politicization in the French literary
field},
Journal = {Theory and Society},
Volume = {32},
Number = {5/6},
Pages = {633-652},
Publisher = {Springer Nature},
Year = {2003},
Month = {December},
ISSN = {0304-2421},
Doi = {10.1023/b:ryso.0000004920.14641.1b},
Key = {fds264763}
}

@article{fds264965,
Author = {Hernandez, M and Frangi, AF and Sapiro, G},
Title = {Three-dimensional segmentation of brain aneurysms in CTA
using non-parametric region-based information and implicit
deformable models: Method and evaluation},
Journal = {Lecture Notes in Computer Science (Including Subseries
Lecture Notes in Artificial Intelligence and Lecture Notes
in Bioinformatics)},
Volume = {2879},
Number = {PART 2},
Pages = {594-602},
Year = {2003},
Month = {December},
ISSN = {0302-9743},
Abstract = {Knowledge of brain aneurysm dimensions is essential in
minimally invasive surgical interventions using Guglielmi
Detachable Coils. These parameters are obtained in clinical
routine using 2D maximum intensity projection images.
Automated quantification of the three dimensional structure
of aneurysms directly from the 3D data set may be used to
provide accurate and objective measurements of the
clinically relevant parameters. In this paper we present an
algorithm devised for the segmentation of brain aneurysms
based on implicit deformable models combined with
non-parametric region-based information. This work also
presents the evaluation of the method in a clinical data
base of 39 cases.},
Key = {fds264965}
}

@article{fds264967,
Author = {Sapiro, G},
Title = {Introduction to Partial Differential Equations and
Variational Formulations in Image Processing},
Journal = {Handbook of Numerical Analysis},
Volume = {11},
Pages = {383-461},
Publisher = {Elsevier},
Year = {2003},
Month = {December},
ISSN = {1570-8659},
url = {http://dx.doi.org/10.1016/S1570-8659(02)11006-4},
Doi = {10.1016/S1570-8659(02)11006-4},
Key = {fds264967}
}

@article{fds264811,
Author = {Sapiro, G},
Title = {The literary field between the state and the
market},
Journal = {Poetics},
Volume = {31},
Number = {5-6},
Pages = {441-464},
Publisher = {Elsevier BV},
Year = {2003},
Month = {October},
ISSN = {0304-422X},
Doi = {10.1016/j.poetic.2003.09.001},
Key = {fds264811}
}

@article{fds264973,
Author = {Gorla, G and Interrante, V and Sapiro, G},
Title = {Texture Synthesis for 3D Shape Representation},
Journal = {Ieee Transactions on Visualization and Computer
Graphics},
Volume = {9},
Number = {4},
Pages = {512-524},
Publisher = {Institute of Electrical and Electronics Engineers
(IEEE)},
Year = {2003},
Month = {October},
url = {http://dx.doi.org/10.1109/TVCG.2003.1260745},
Abstract = {Considerable evidence suggests that a viewer's perception of
the 3D shape of a polygonally-defined object can be
significantly affected (either masked or enhanced) by the
presence of a surface texture pattern. However,
investigations into the specific mechanisms of texture's
effect on shape perception are still ongoing and the
question of how to design and apply a texture pattern to a
surface in order to best facilitate shape perception remains
open. Recently, we have suggested that, for anisotropic
texture patterns, the accuracy of shape judgments may be
significantly affected by the orientation of the surface
texture pattern anisotropy with respect to the principal
directions of curvature over the surface. However, it has
been difficult, until this time, to conduct controlled
studies specifically Investigating the effect of texture
orientation on shape perception because there has been no
simple and reliable method for texturing an arbitrary doubly
curved surface with a specified input pattern such that the
dominant orientation of the pattern everywhere follows a
predefined directional vector field over the surface, while
seams and projective distortion of the pattern are avoided.
In this paper, we present a straightforward and highly
efficient method for achieving such a texture and describe
how it can potentially be used to enhance shape
representation. Specifically, we describe a novel,
efficient, automatic algorithm for seamlessly synthesizing,
from a sample 2D pattern, a high resolution fitted surface
texture in which the dominant orientation of the pattern
locally follows a specified vector field over the surface at
a par-pixel level and In which seams, projective distortion,
and repetition artifacts in the texture pattern are nearly
completely avoided. We demonstrate the robustness of our
method with a variety of texture swatches applied to
standard graphics data sets and we explain how our method
can be used to facilitate research in the perception of
shape from texture.},
Doi = {10.1109/TVCG.2003.1260745},
Key = {fds264973}
}

@article{fds264959,
Author = {Hernandez, M and Barrena, R and Hernandez, G and Sapiro, G and Frangi,
AF},
Title = {Pre-clinical evaluation of Implicit Deformable Models for
three-dimensional segmentation of brain aneurysms in
CTA},
Journal = {Smart Structures and Materials 2005: Active Materials:
Behavior and Mechanics},
Volume = {5032 II},
Pages = {1264-1274},
Publisher = {SPIE},
Year = {2003},
Month = {September},
url = {http://dx.doi.org/10.1117/12.483596},
Abstract = {Knowledge of brain aneurysm dimensions is essential during
the planning stage of minimally invasive surgical
interventions using Guglielmi Detachable Coils (GDC). These
parameters are obtained in clinical routine using 2D Maximum
Intensity Projection images from Computed Tomographic
Angiography (CTA). Automated quantification of the three
dimensional structure of aneurysms directly from the 3D data
set may be used to provide accurate and objective
measurements of the clinically relevant parameters. The
properties of Implicit Deformable Models make them suitable
to accurately extract the three dimensional structure of the
aneurysm and its connected vessels. We have devised a
two-stage segmentation algorithm for this purpose. In the
first stage, a rough segmentation is obtained by means of
the Fast Marching Method combining a speed function based on
a vessel enhancement filtering and a freezing algorithm. In
the second stage, this rough segmentation provides the
initialization for Geodesic Active Contours driven by
region-based information. The latter problem is solved using
the Level Set algorithm. This work presents a comparative
study between a clinical and a computerized protocol to
derive three geometrical descriptors of aneurysm morphology
that are standard in assessing the viability of surgical
treatment with GDCs. The study was performed on a data base
of 40 brain aneurysms. The manual measurements were made by
two neuroradiologists in two independent sessions. Both
inter- and intra-observer variability and comparison with
the automated method are presented. According to these
results, Implicit Deformable Models are a suitable technique
for this application.},
Doi = {10.1117/12.483596},
Key = {fds264959}
}

@article{fds264968,
Author = {Fedkiw, RP and Sapiro, G and Shu, CW},
Title = {Shock capturing, level sets, and PDE based methods in
computer vision and image processing: A review of Osher's
contributions},
Journal = {Journal of Computational Physics},
Volume = {185},
Number = {2},
Pages = {309-341},
Publisher = {Elsevier BV},
Year = {2003},
Month = {March},
url = {http://dx.doi.org/10.1016/S0021-9991(02)00016-5},
Abstract = {In this paper we review the algorithm development and
applications in high resolution shock capturing methods,
evel set methods, and PDE based methods in computer vision
and image processing. The emphasis is on Stanley Osher's
contribution in these areas and the impact of his work. We
Engquist-Osher scheme, TVD schemes, entropy conditions, ENO
and WENO schemes, and numerical schemes for Hamilton-Jacobi
type equations. Among level set methods we will review level
set calculus, numerical techniques, fluids and materials,
variational approach, high codimension motion, geometric
optics, and the computation of discontinuous solutions to
Hamilton-Jacobi equations. Among computer vision and image
processing we will review the total variation model for
image denoising, images on implicit surfaces, and the level
set method in image processing and computer vision. © 2003
Doi = {10.1016/S0021-9991(02)00016-5},
Key = {fds264968}
}

@article{fds264969,
Author = {Pardo, A and Sapiro, G},
Title = {Visualization of high dynamic range images.},
Journal = {Ieee Transactions on Image Processing : a Publication of the
Ieee Signal Processing Society},
Volume = {12},
Number = {6},
Pages = {639-647},
Year = {2003},
Month = {January},
ISSN = {1057-7149},
url = {http://www.ncbi.nlm.nih.gov/pubmed/18237938},
Abstract = {A novel paradigm for information visualization in high
dynamic range images is presented in this paper. These
images, real or synthetic, have luminance with typical
ranges many orders of magnitude higher than that of standard
output/viewing devices, thereby requiring some processing
for their visualization. In contrast with existent
approaches, which compute a single image with reduced range,
close in a given sense to the original data, we propose to
look for a representative set of images. The goal is then to
produce a minimal set of images capturing the information
all over the high dynamic range data, while at the same time
preserving a natural appearance for each one of the images
in the set. A specific algorithm that achieves this goal is
presented and tested on natural and synthetic
data.},
Doi = {10.1109/tip.2003.812373},
Key = {fds264969}
}

@article{fds264970,
Author = {Rane, SD and Sapiro, G and Bertalmio, M},
Title = {Structure and texture filling-in of missing image blocks in
wireless transmission and compression applications.},
Journal = {Ieee Transactions on Image Processing : a Publication of the
Ieee Signal Processing Society},
Volume = {12},
Number = {3},
Pages = {296-303},
Year = {2003},
Month = {January},
ISSN = {1057-7149},
url = {http://www.ncbi.nlm.nih.gov/pubmed/18237909},
Abstract = {An approach for filling-in blocks of missing data in
wireless image transmission is presented. When compression
algorithms such as JPEG are used as part of the wireless
transmission process, images are first tiled into blocks of
8 x 8 pixels. When such images are transmitted over fading
channels, the effects of noise can destroy entire blocks of
the image. Instead of using common retransmission query
protocols, we aim to reconstruct the lost data using
correlation between the lost block and its neighbors. If the
lost block contained structure, it is reconstructed using an
image inpainting algorithm, while texture synthesis is used
for the textured blocks. The switch between the two schemes
is done in a fully automatic fashion based on the
surrounding available blocks. The performance of this method
is tested for various images and combinations of lost
blocks. The viability of this method for image compression,
in association with lossy JPEG, is also discussed.},
Doi = {10.1109/tip.2002.804264},
Key = {fds264970}
}

@article{fds264971,
Author = {Bertalmio, M and Vese, L and Sapiro, G and Osher,
S},
Title = {Simultaneous structure and texture image
inpainting.},
Journal = {Ieee Transactions on Image Processing : a Publication of the
Ieee Signal Processing Society},
Volume = {12},
Number = {8},
Pages = {882-889},
Year = {2003},
Month = {January},
ISSN = {1057-7149},
url = {http://www.ncbi.nlm.nih.gov/pubmed/18237962},
Abstract = {An algorithm for the simultaneous filling-in of texture and
structure in regions of missing image information is
presented in this paper. The basic idea is to first
decompose the image into the sum of two functions with
different basic characteristics, and then reconstruct each
one of these functions separately with structure and texture
filling-in algorithms. The first function used in the
decomposition is of bounded variation, representing the
underlying image structure, while the second function
captures the texture and possible noise. The region of
missing information in the bounded variation image is
reconstructed using image inpainting algorithms, while the
same region in the texture image is filled-in with texture
synthesis techniques. The original image is then
reconstructed adding back these two sub-images. The novel
contribution of this paper is then in the combination of
these three previously developed components, image
decomposition with inpainting and texture synthesis, which
permits the simultaneous use of filling-in algorithms that
are suited for different image characteristics. Examples on
real images show the advantages of this proposed
approach.},
Doi = {10.1109/tip.2003.815261},
Key = {fds264971}
}

@article{fds264779,
Author = {Pichon, E and Sapiro, G and Tannenbaum, A},
Title = {Segmentation of diffusion tensor imagery},
Journal = {Directions in Mathematical Systems Theory and
Optimization},
Volume = {286},
Pages = {239-247},
Year = {2003},
ISSN = {0170-8643},
Key = {fds264779}
}

@article{fds264958,
Author = {Sapiro, GR},
Title = {Guest editorial: Introduction to the special issue on
imaging science},
Journal = {Journal of Mathematical Imaging and Vision},
Volume = {18},
Number = {1},
Pages = {5-},
Year = {2003},
url = {http://dx.doi.org/10.1023/A:1021887609535},
Doi = {10.1023/A:1021887609535},
Key = {fds264958}
}

@article{fds264960,
Author = {Bertalmio, M and Vese, L and Sapiro, G and Osher,
S},
Title = {Simultaneous structure and texture image
inpainting},
Journal = {Proceedings of the Ieee Computer Society Conference on
Computer Vision and Pattern Recognition},
Volume = {2},
Pages = {II/707-II/712},
Year = {2003},
Abstract = {An algorithm for the simultaneous filling-in of texture and
structure in regions of missing image information is
presented in this paper. The basic idea is to first
decompose the image into the sum of two functions with
different basic characteristics, and then reconstruct each
one of these functions separately with structure and texture
filling-in algorithms. The first function used in the
decomposition is of bounded variation, representing the
underlying image structure, while the second function
captures the texture and possible noise. The region of
missing information in the bounded variation image is
reconstructed using image inpainting algorithms, while the
same region in the texture image is filled-in with texture
synthesis techniques. The original image is then
reconstructed adding back these two sub-images. The novel
contribution of this paper is then in the combination of
these three previously developed components, image
decomposition with inpainting and texture synthesis, which
permits the simultaneous use of filling-in algorithms that
are suited for different image characteristics. Examples on
real images show the advantages of this proposed
approach.},
Key = {fds264960}
}

@article{fds264953,
Author = {Interrante, V and Gorla, G and Kim, S and Hagh-Shenas, H and Sapiro,
G},
Title = {Texture synthesis for 3D shape representation},
Journal = {Journal of Vision},
Volume = {2},
Number = {7},
Pages = {305-305},
Publisher = {Association for Research in Vision and Ophthalmology
(ARVO)},
Year = {2002},
Month = {December},
url = {http://dx.doi.org/10.1167/2.7.305},
Abstract = {If we could design the perfect texture pattern to apply to
any smooth surface in order to enable observers to more
accurately perceive the surface's shape in a static
monocular image taken from an arbitrary generic viewpoint
under standard lightingconditions, what would the
characteristics of that texture pattern be? In order to gain
insight into this question, our group has developed an
efficient algorithm for synthesizing a high resolution
texture pattern (derived from a provided 2D image, e.g. from
the Brodatz album) over an arbitrary doubly curved surface
in such a way that both seams and projective distortion are
practically eliminated, and, most importantly, the
orientation of the texture pattern is constrained to follow
an underlying vector field over the surface at a perpixel
level. We are using this algorithm to generate stimuli for a
series of experiments investigating the effects of various
texture characteristics, including orientation, on surface
shape judgments. The results of earlier studies that we
conducted using a more restricted class of uni-directional
texture patterns seemed to support the hypothesis that shape
perception is most severely impeded when the texture pattern
consists of lines that turn in the surface, and that shape
perception is not significantly different in the case of a
texture pattern consisting of lines that are locally aligned
with the first principal direction than in the case of an
isotropic texture pattern of similar spatial frequency. Our
new texture synthesis method enables us to extend these
studies to a much broader class of textures, including
patterns that contain 90-degree rotational symmetry, which
is useful in enabling us to maintain continuity in a
principal-direction oriented pattern as it passes through
umbilic points where the first and second principal
directions switch places. Images are available at
www.cs.umn.edu/~interran/texture. Upon publication, our
software will be made available via the web.},
Doi = {10.1167/2.7.305},
Key = {fds264953}
}

@article{fds264762,
Author = {Sapiro, G},
Title = {The structure of the French literary field during the German
Occupation (1940–1944)},
Journal = {Poetics},
Volume = {30},
Number = {5-6},
Pages = {387-402},
Publisher = {Elsevier BV},
Year = {2002},
Month = {October},
ISSN = {0304-422X},
Doi = {10.1016/s0304-422x(02)00032-3},
Key = {fds264762}
}

@article{fds264785,
Author = {Sapiro, G and Lebovics, H},
Title = {Mona Lisa's Escort. Andre Malraux and the Reinvention of
French Culture},
Journal = {Le Mouvement Social},
Number = {201},
Pages = {104-104},
Publisher = {JSTOR},
Year = {2002},
Month = {October},
ISSN = {0027-2671},
Doi = {10.2307/3779882},
Key = {fds264785}
}

@article{fds264952,
Author = {Rane, SD and Remus, J and Sapiro, G},
Title = {Wavelet-domain reconstruction of lost blocks in wireless
image transmission and packet-switched networks},
Journal = {Ieee International Conference on Image Processing},
Volume = {1},
Pages = {I/309-I/312},
Year = {2002},
Month = {January},
Abstract = {A fast scheme for wavelet-domain interpolation of lost image
blocks in wireless image transmission is presented in this
paper. In the transmission of block-coded images, fading in
wireless channels and congestion in packet-switched networks
can cause entire blocks to be lost. Instead of using
retransmission query protocols, we reconstruct the lost
block in the wavelet-domain using the correlation between
the lost block and its neighbors. The algorithm first uses
simple thresholding to determine the presence or absence of
edges in the lost block. This is followed by an
interpolation scheme, designed to minimize the blockiness
effect, while preserving the edges or texture in the
interior of the blocks. The interpolation scheme minimizes
the square of the error between the border coefficients of
the lost block and those of its neighbors, at each transform
scale. The performance of the algorithm on standard test
images, its low computational overhead at the decoder, and
its performance vis-a-vis other reconstruction schemes, is
discussed.},
Key = {fds264952}
}

@article{fds264954,
Author = {Rane, SD and Sapiro, G and Bertalmio, M},
Title = {Structure and texture filling-in of missing image blocks in
wireless transmission and compression},
Journal = {Ieee International Conference on Image Processing},
Volume = {1},
Pages = {I/317-I/320},
Year = {2002},
Month = {January},
Abstract = {An approach for filling-in blocks of missing data in
wireless image transmission is presented in this paper. When
compression algorithms such as JPEG are used as part of the
wireless transmission process, images are first tiled into
blocks of 8 × 8 pixels. When such images are transmitted
over fading channels, the effects of noise can kill entire
blocks of the image. Instead of using common retransmission
query protocols, we aim to reconstruct the lost data using
correlation between the lost block and its neighbors. If the
lost block contained structure, it is reconstructed using an
image inpainting algorithm, while texture synthesis is used
for the textured blocks. The switch between the two schemes
is done in a fully automatic fashion based on the
surrounding available blocks. The performance of this method
is tested for various images and combinations of lost
blocks. The viability of this method for image compression,
in association with lossy JPEG, is also discussed.},
Key = {fds264954}
}

@article{fds264955,
Author = {Pardo, A and Sapiro, G},
Title = {Visualization of high dynamic range images},
Journal = {Ieee International Conference on Image Processing},
Volume = {1},
Pages = {I/633-I/636},
Year = {2002},
Month = {January},
Abstract = {A novel paradigm for information visualization in high
dynamic range images is presented in this paper. These
images, real or synthetic, have luminance with typical
ranges many orders of magnitude higher than that of standard
output devices, thereby requiring some processing for their
visualization. In contrast with existent approaches, which
compute a single image with reduced range, close in a given
sense to the original data, we propose to look for a
representative set of images. The goal is then to produce a
minimal set of images capturing the information all over the
high dynamic range data, while at the same time preserving a
natural appearance for each one of the images in the set. A
specific algorithm that achieves this goal is presented and
tested on natural and synthetic data.},
Key = {fds264955}
}

@article{fds264956,
Author = {Faugeras, O and Perona, P and Sapiro, G},
Title = {Special issue on partial differential equations in image
processing, computer vision, and computer
graphics},
Journal = {Journal of Visual Communication and Image
Representation},
Volume = {13},
Number = {1-2},
Pages = {1-2},
Publisher = {Elsevier BV},
Year = {2002},
Month = {January},
ISSN = {1047-3203},
url = {http://dx.doi.org/10.1006/jvci.2002.0505},
Doi = {10.1006/jvci.2002.0505},
Key = {fds264956}
}

@article{fds264951,
Author = {Bertalmío, M and Cheng, LT and Osher, S and Sapiro,
G},
Title = {Variational problems and partial differential equations on
implicit surfaces},
Journal = {Journal of Computational Physics},
Volume = {174},
Number = {2},
Pages = {759-780},
Publisher = {Elsevier BV},
Year = {2001},
Month = {December},
ISSN = {0021-9991},
url = {http://dx.doi.org/10.1006/jcph.2001.6937},
Abstract = {A novel framework for solving variational problems and
partial differential equations for scalar and vector-valued
data defined on surfaces is introduced in this paper. The
key idea is to implicitly represent the surface as the level
set of a higher dimensional function and to solve the
surface equations in a fixed Cartesian coordinate system
using this new embedding function. The equations are then
both intrinsic to the surface and defined in the embedding
space. This approach thereby eliminates the need for
performing complicated and inaccurate computations on
triangulated surfaces, as is commonly done in the
literature. We describe the framework and present examples
in computer graphics and image processing applications,
including texture synthesis, flow field visualization, and
image and vector field intrinsic regularization for data
defined on 3D surfaces. © 2001 Elsevier
Science.},
Doi = {10.1006/jcph.2001.6937},
Key = {fds264951}
}

@article{fds264943,
Author = {Bertalmío, M and Bertozzi, AL and Sapiro, G},
Title = {Navier-Stokes, fluid dynamics, and image and video
inpainting},
Journal = {Proceedings of the Ieee Computer Society Conference on
Computer Vision and Pattern Recognition},
Volume = {1},
Pages = {I355-I362},
Publisher = {IEEE Comput. Soc},
Year = {2001},
Month = {December},
url = {http://dx.doi.org/10.1109/cvpr.2001.990497},
Abstract = {Image inpainting involves filling in part of an image or
video using information from the surrounding area.
Applications include the restoration of damaged photographs
and movies and the removal of selected objects. In this
paper, we introduce a class of automated methods for digital
inpainting. The approach uses ideas from classical fluid
dynamics to propagate isophote lines continuously from the
exterior into the region to be inpainted. The main idea is
to think of the image intensity as a 'stream function' for a
two-dimensional incompressible flow. The Laplacian of the
image intensity plays the role of the vorticity of the
fluid; it is transported into the region to be inpainted by
a vector field defined by the stream function. The resulting
algorithm is designed to continue isophotes while matching
gradient vectors at the boundary of the inpainting region.
The method is directly based on the Navier-Stokes equations
for fluid dynamics, which has the immediate advantage of
well-developed theoretical and numerical results. This is a
new approach for introducing ideas from computational fluid
dynamics into problems in computer vision and image
analysis.},
Doi = {10.1109/cvpr.2001.990497},
Key = {fds264943}
}

@article{fds264950,
Author = {Mémoli, F and Sapiro, G},
Title = {Fast computation of weighted distance functions and
geodesics on implicit hyper-surfaces},
Journal = {Journal of Computational Physics},
Volume = {173},
Number = {2},
Pages = {730-764},
Publisher = {Elsevier BV},
Year = {2001},
Month = {November},
ISSN = {0021-9991},
url = {http://dx.doi.org/10.1006/jcph.2001.6910},
Abstract = {An algorithm for the computationally optimal construction of
intrinsic weighted distance functions on implicit
hyper-surfaces is introduced in this paper. The basic idea
is to approximate the intrinsic weighted distance by the
Euclidean weighted distance computed in a band surrounding
the implicit hyper-surface in the embedding space, thereby
performing all the computations in a Cartesian grid with
classical and efficient numerics. Based on work on geodesics
on Riemannian manifolds with boundaries, we bound the error
between the two distance functions. We show that this error
is of the same order as the theoretical numerical error in
computationally optimal, Hamilton-Jacobi-based, algorithms
for computing distance functions in Cartesian grids.
Therefore, we can use these algorithms, modified to deal
with spaces with boundaries, and obtain also for the case of
intrinsic distance functions on implicit hyper-surfaces a
computationally efficient technique. The approach can be
extended to solve a more general class of Hamilton-Jacobi
equations defined on the implicit surface, following the
same idea of approximating their solutions by the solutions
in the embedding Euclidean space. The framework here
introduced thereby allows for the computations to be
performed on a Cartesian grid with computationally optimal
algorithms, in spite of the fact that the distance and
Hamilton-Jacobi equations are intrinsic to the implicit
Doi = {10.1006/jcph.2001.6910},
Key = {fds264950}
}

@article{fds264949,
Author = {Rane, SD and Sapiro, G},
Title = {Evaluation of JPEG-LS, the new lossless and controlled-lossy
still image compression standard, for compression of
high-resolution elevation data},
Journal = {Ieee Transactions on Geoscience and Remote
Sensing},
Volume = {39},
Number = {10},
Pages = {2298-2306},
Publisher = {Institute of Electrical and Electronics Engineers
(IEEE)},
Year = {2001},
Month = {October},
ISSN = {0196-2892},
url = {http://dx.doi.org/10.1109/36.957293},
Abstract = {The compression of elevation data is studied in this paper.
The performance of JPEG-LS, the new international ISO/ITU
standard for lossless and near-lossless (controlled-lossy)
still-image compression, is investigated both for data from
the USGS digital elevation model (DEM) database and the
navy-provided digital terrain model (DTM) data. Using
JPEG-LS has the advantage of working with a standard
algorithm. Moreover, in contrast with algorithms like the
popular JPEG-lossy standard, this algorithm permits the
completely lossless compression of the data as well as a
controlled lossy mode where a sharp upper bound on the
elevation error is selected by the user. All these are
achieved at a very low computational complexity. In addition
to these algorithmic advantages, we show that JPEG-LS
achieves significantly better compression results than those
obtained with other (nonstandard) algorithms previously
investigated for the compression of elevation data. The
results here reported suggest that JPEG-LS can immediately
be adopted for the compression of elevation data for a
number of applications.},
Doi = {10.1109/36.957293},
Key = {fds264949}
}

@article{fds264946,
Author = {Bartesaghi, A and Sapiro, G},
Title = {A system for the generation of curves on 3D brain
images.},
Journal = {Human Brain Mapping},
Volume = {14},
Number = {1},
Pages = {1-15},
Year = {2001},
Month = {September},
ISSN = {1065-9471},
url = {http://www.ncbi.nlm.nih.gov/pubmed/11500986},
Abstract = {In this study, a computational optimal system for the
generation of curves on triangulated surfaces representing
3D brains is described. The algorithm is based on optimally
computing geodesics on the triangulated surfaces following
Kimmel and Sethian ([1998]: Proc Natl Acad Sci 95:15). The
system can be used to compute geodesic curves for accurate
distance measurements as well as to detect sulci and gyri.
These curves are defined based on local surface curvatures
that are computed following a novel approach presented in
this study. The corresponding software is available to the
research community.},
Doi = {10.1002/hbm.1037},
Key = {fds264946}
}

@article{fds264947,
Author = {Betelu, S and Sapiro, G and Tannenbaum, A and Giblin,
PJ},
Title = {On the computation of the affine skeletons of planar curves
and the detection of skew symmetry},
Journal = {Pattern Recognition},
Volume = {34},
Number = {5},
Pages = {943-952},
Publisher = {Elsevier BV},
Year = {2001},
Month = {May},
ISSN = {0031-3203},
url = {http://dx.doi.org/10.1016/S0031-3203(00)00045-5},
Abstract = {In this paper we discuss a new approach to compute discrete
skeletons of planar shapes which is based on affine
distances, being therefore affine invariant. The method
works with generic curves that may contain concave sections,
A dynamical interpretation of the affine skeleton
construction, based on curve evolution, is discussed as
well. We propose an efficient implementation of the method
and give examples. We also demonstrate how to use this
method to detect affine skew symmetry in real images. ©
Doi = {10.1016/S0031-3203(00)00045-5},
Key = {fds264947}
}

@article{fds264815,
Author = {Pardo, A and Sapiro, G},
Title = {Vector probability diffusion},
Journal = {Ieee Signal Processing Letters},
Volume = {8},
Number = {4},
Pages = {106-109},
Year = {2001},
Month = {April},
ISSN = {1070-9908},
Doi = {10.1109/97.911471},
Key = {fds264815}
}

@article{fds264944,
Author = {Pardo, A and Sapiro, G},
Title = {Vector probability diffusion},
Journal = {Ieee Signal Processing Letters},
Volume = {8},
Number = {4},
Pages = {106-109},
Publisher = {Institute of Electrical and Electronics Engineers
(IEEE)},
Year = {2001},
Month = {April},
ISSN = {1070-9908},
url = {http://dx.doi.org/10.1109/97.911471},
Abstract = {A method for isotropic and anisotropic diffusion of vector
probabilities in general, and posterior probabilities in
particular, is introduced. The technique is based on
diffusing via coupled partial differential equations
restricted to the semi-hyperplane corresponding to
probability functions. Both the partial differential
equations and their corresponding numerical implementation
guarantee that the vector remains a probability vector,
having all its components positive and adding to one.
Applying the method to posterior probabilities in
classification problems, spatial and contextual coherence is
introduced before the maximum a posteriori (MAP) decision,
thereby improving the classification results.},
Doi = {10.1109/97.911471},
Key = {fds264944}
}

@article{fds264942,
Author = {Solé, AF and Ngan, SC and Sapiro, G and Hu, X and López,
A},
Title = {Anisotropic 2-D and 3-D averaging of fMRI
signals.},
Journal = {Ieee Transactions on Medical Imaging},
Volume = {20},
Number = {2},
Pages = {86-93},
Year = {2001},
Month = {February},
ISSN = {0278-0062},
url = {http://www.ncbi.nlm.nih.gov/pubmed/11321593},
Abstract = {A novel method for denoising functional magnetic resonance
imaging temporal signals is presented in this note. The
method is based on progressively enhancing the temporal
signal by means of adaptive anisotropic spatial averaging.
This average is based on a new metric for comparing temporal
signals corresponding to active fMRI regions. Examples are
presented both for simulated and real two and
three-dimensional data. The software implementing the
proposed technique is publicly available for the research
community.},
Doi = {10.1109/42.913175},
Key = {fds264942}
}

@article{fds264773,
Author = {Bertalmió, M and Sapiro, G and Cheng, LT and Osher,
S},
Title = {Variational problems and PDEs on implicit
surfaces},
Journal = {Proceedings Ieee Workshop on Variational and Level Set
Methods in Computer Vision, Vlsm 2001},
Pages = {186-193},
Publisher = {IEEE COMPUTER SOC},
Year = {2001},
Month = {January},
ISBN = {076951278X},
Abstract = {© 2001 IEEE. A novel framework for solving variational
problems and partial differential equations for scalar and
vector-valued data defined on surfaces is introduced. The
key idea is to implicitly represent the surface as the level
set of a higher dimensional function, and solve the surface
equations in a fixed Cartesian coordinate system using this
new embedding function. The equations are then both
intrinsic to the surface and defined in the embedding space.
This approach thereby eliminates the need for performing
complicated and inaccurate computations on triangulated
surfaces, as is commonly done in the literature. We describe
the framework and present examples in computer graphics and
image processing applications, including texture synthesis,
flow field visualization, as well as image and vector field
intrinsic regularization for data defined on 3D
surfaces.},
Doi = {10.1109/VLSM.2001.938899},
Key = {fds264773}
}

@article{fds264939,
Author = {Betelu, S and Sapiro, G and Tannenbaum, A},
Title = {Affine invariant erosion of 3D shapes},
Journal = {Proceedings of the Ieee International Conference on Computer
Vision},
Volume = {2},
Pages = {174-180},
Publisher = {IEEE Comput. Soc},
Year = {2001},
Month = {January},
url = {http://dx.doi.org/10.1109/iccv.2001.937621},
Abstract = {A new definition of affine invariant erosion of 3D surfaces
is introduced. Instead of being based in terms of Euclidean
distances, the volumes enclosed between the surface and its
chords are used. The resulting erosion is insensitive to
noise, and by construction, it is affine invariant. We prove
propose a simple method to compute the erosion of implicit
surfaces. We also discuss how the affine erosion can be used
to define 3D affine invariant robust skeletons.},
Doi = {10.1109/iccv.2001.937621},
Key = {fds264939}
}

@article{fds264940,
Author = {Ballester, C and Caselles, V and Verdera, J and Bertalmio, M and Sapiro,
G},
Title = {A variational model for filling-in gray level and color
images},
Journal = {Proceedings of the Ieee International Conference on Computer
Vision},
Volume = {1},
Pages = {10-16},
Year = {2001},
Month = {January},
Abstract = {A variational approach for filling-in regions of missing
data in gray-level and color images is introduced in this
paper. The approach is based on joint interpolation of the
smoothly extending in an automatic fashion the isophote
lines into the holes of missing data. This interpolation is
computed solving the variational problem via its gradient
descent flow, which leads to a set of coupled second order
partial differential equations, one for the gray-levels and
one for the gradient orientations. The process underlying
this approach can be considered as an interpretation of the
Gestaltist's principle of good continuation. No limitations
are imposed on the topology of the holes, and all regions of
missing data can be simultaneously processed, even if they
are surrounded by completely different structures.
Applications of this technique include the restoration of
old photographs and removal of superimposed text like dates,
subtitles, or publicity. Examples of these applications are
given.},
Key = {fds264940}
}

@article{fds264941,
Author = {Haker, S and Sapiro, G and Tannenbaum, A and Washburn,
D},
Title = {Missile tracking using knowledge-based adaptive
thresholding},
Journal = {Ieee International Conference on Image Processing},
Volume = {1},
Pages = {786-789},
Year = {2001},
Month = {January},
Abstract = {In this paper, we apply a knowledge-based segmentation
method developed for still and video images to the problem
of tracking missiles and high speed projectiles. Since we
are only interested in segmenting a portion of the missile
(namely, the nose cone), we use our segmentation procedure
as a method of adapting thresholding. The key idea is to
utilize a priori knowledge about the objects present in the
image, e.g. missile and background, introduced via Bayes'
rule. Posterior probabilities obtained in this way are
anisotropically smoothed, and the image segmentation is
obtained via MAP classifications of the smoothed data. When
segmenting sequences of images, the smoothed posterior
probabilities of past frames are used as prior distributions
in succeeding frames.},
Key = {fds264941}
}

@article{fds264945,
Author = {Tang, B and Sapiro, G and Caselles, V},
Title = {Color image enhancement via chromaticity
diffusion.},
Journal = {Ieee Transactions on Image Processing : a Publication of the
Ieee Signal Processing Society},
Volume = {10},
Number = {5},
Pages = {701-707},
Year = {2001},
Month = {January},
ISSN = {1057-7149},
url = {http://www.ncbi.nlm.nih.gov/pubmed/18249660},
Abstract = {A novel approach for color image denoising is proposed in
this paper. The algorithm is based on separating the color
data into chromaticity and brightness, and then processing
each one of these components with partial differential
equations or diffusion flows. In the proposed algorithm,
each color pixel is considered as an n-dimensional vector.
The vectors' direction, a unit vector, gives the
chromaticity, while the magnitude represents the pixel
brightness. The chromaticity is processed with a system of
coupled diffusion equations adapted from the theory of
harmonic maps in liquid crystals. This theory deals with the
regularization of vectorial data, while satisfying the
intrinsic unit norm constraint of directional data such as
chromaticity. Both isotropic and anisotropic diffusion flows
are presented for this n-dimensional chromaticity diffusion
flow. The brightness is processed by a scalar median filter
or any of the popular and well established anisotropic
diffusion flows for scalar image enhancement. We present the
underlying theory, a number of examples, and briefly compare
with the current literature.},
Doi = {10.1109/83.918563},
Key = {fds264945}
}

@article{fds264948,
Author = {Ballester, C and Bertalmio, M and Caselles, V and Sapiro, G and Verdera,
J},
Title = {Filling-in by joint interpolation of vector fields and gray
levels.},
Journal = {Ieee Transactions on Image Processing : a Publication of the
Ieee Signal Processing Society},
Volume = {10},
Number = {8},
Pages = {1200-1211},
Year = {2001},
Month = {January},
ISSN = {1057-7149},
url = {http://www.ncbi.nlm.nih.gov/pubmed/18255537},
Abstract = {A variational approach for filling-in regions of missing
data in digital images is introduced. The approach is based
on joint interpolation of the image gray levels and
gradient/isophotes directions, smoothly extending in an
automatic fashion the isophote lines into the holes of
missing data. This interpolation is computed by solving the
variational problem via its gradient descent flow, which
leads to a set of coupled second order partial differential
equations, one for the gray-levels and one for the gradient
orientations. The process underlying this approach can be
considered as an interpretation of the Gestaltist's
principle of good continuation. No limitations are imposed
on the topology of the holes, and all regions of missing
data can be simultaneously processed, even if they are
surrounded by completely different structures. Applications
of this technique include the restoration of old photographs
and removal of superimposed text like dates, subtitles, or
publicity. Examples of these applications are given. We
conclude the paper with a number of theoretical results on
the proposed variational approach and its corresponding
Doi = {10.1109/83.935036},
Key = {fds264948}
}

@article{fds264957,
Author = {Solé, AF and López, A and Sapiro, G},
Title = {Crease enhancement diffusion},
Journal = {Computer Vision and Image Understanding},
Volume = {84},
Number = {2},
Pages = {241-248},
Publisher = {Elsevier BV},
Year = {2001},
Month = {January},
ISSN = {1077-3142},
url = {http://dx.doi.org/10.1006/cviu.2001.0945},
Abstract = {Ridge and valley structures are important image features,
especially in oriented textures. Usually, the extraction of
these structures requires a prefiltering step to regularize
the source image. In this paper, we show that classical
diffusion-based filters are not always appropriate for this
task and propose a new filtering process. This new filter
can be interpreted as an example of introducing the
intrinsic image structure in a diffusion process. © 2001
Elsevier Science (USA).},
Doi = {10.1006/cviu.2001.0945},
Key = {fds264957}
}

@article{fds264801,
Author = {Sapiro, G},
Title = {Harmonic map flows and image processing},
Journal = {Foundations of Computational Mathematics},
Volume = {284},
Pages = {299-322},
Year = {2001},
ISBN = {0-521-00349-0},
ISSN = {0076-0552},
Key = {fds264801}
}

@article{fds264928,
Author = {Chung, DH and Sapiro, G},
Title = {Segmentation-free skeletonization of gray-scale images via
PDE's},
Journal = {Ieee International Conference on Image Processing},
Volume = {2},
Pages = {927-930},
Year = {2000},
Month = {December},
Abstract = {A simple approach to compute the skeletons of gray-scale
images using partial differential equations is presented in
this paper. The proposed scheme works directly on the
gray-scale images, without the necessity of pre-segmentation
(binarization), or the addition of shock capturing schemes.
This is accomplished by deforming the given image according
to a family of modified continuous-scale erosion/dilation
equations. With the scheme here proposed, the skeleton of
multiple objects can be simultaneously computed. Examples on
synthetic and real images are provided.},
Key = {fds264928}
}

@article{fds264930,
Author = {Neoh, HS and Sapiro, G},
Title = {Using anisotropic diffusion of probability maps for activity
detection in block-design functional MRI},
Journal = {Ieee International Conference on Image Processing},
Volume = {1},
Pages = {621-624},
Year = {2000},
Month = {December},
Abstract = {A new approach for improving the detection of pixels
associated with neural activity in functional magnetic
resonance imaging (fMRI) is presented. We propose to use
anisotropic diffusion to exploit the spatial correlation
between the active pixels in functional MRI. Specifically,
in this paper the anisotropic diffusion flow is applied to a
probability image, obtained either from t-map statistics or
via Bayes rule. In general, this information diffusion
technique can be incorporated into other activity detection
algorithms before the active/non-active hard decision is
made. Examples with simulated and real data show
improvements over classical techniques.},
Key = {fds264930}
}

@article{fds264932,
Author = {Tang, B and Sapiro, G and Caselles, V},
Title = {Chromaticity diffusion},
Journal = {Ieee International Conference on Image Processing},
Volume = {2},
Pages = {784-787},
Publisher = {IEEE},
Year = {2000},
Month = {December},
url = {http://dx.doi.org/10.1109/icip.2000.899826},
Abstract = {A novel approach for color image denoising is proposed in
this paper. The algorithm is based on separating the color
data into chromaticity and brightness, and then processing
each one of these components with partial differential
equations or diffusion flows. In the proposed algorithm,
each color pixel is considered as an n-dimensional vector.
The vectors' direction, a unit vector, gives the
chromaticity, while the magnitude represents the pixel
brightness. The chromaticity is processed with a system of
coupled diffusion equations adapted from the theory of
harmonic maps in liquid crystals. This theory deals with the
regularization of vectorial data, while satisfying the
intrinsic unit norm constraint of directional data such as
chromaticity. Both isotropic and anisotropic diffusion flows
are presented for this n-dimensional chromaticity diffusion
flow. The brightness is processed by a scalar median filter
or any of the popular and well established anisotropic
diffusion flows for scalar image enhancement. We present the
underlying theory, a number of examples, and briefly compare
with the current literature.},
Doi = {10.1109/icip.2000.899826},
Key = {fds264932}
}

@article{fds264933,
Author = {Bertalmio, M and Sapiro, G and Caselles, V and Ballester,
C},
Title = {Image inpainting},
Journal = {Proceedings of the Acm Siggraph Conference on Computer
Graphics},
Pages = {417-424},
Year = {2000},
Month = {December},
Abstract = {Inpainting, the technique of modifying an image in an
undetectable form, is as ancient as art itself. The goals
and applications of inpainting are numerous, from the
restoration of damaged paintings and photographs to the
removal/replacement of selected objects. In this paper, we
introduce a novel algorithm for digital inpainting of still
images that attempts to replicate the basic techniques used
by professional restorators. After the user selects the
regions to be restored, the algorithm automatically fills-in
these regions with information surrounding them. The fill-in
is done in such a way that isophote lines arriving at the
regions' boundaries are completed inside. In contrast with
previous approaches, the technique here introduced does not
require the user to specify where the novel information
comes from. This is automatically done (and in a fast way),
thereby allowing to simultaneously fill-in numerous regions
containing completely different structures and surrounding
backgrounds. In addition, no limitations are imposed on the
topology of the region to be inpainted. Applications of this
technique include the restoration of old photographs and
damaged film; removal of superimposed text like dates,
subtitles, or publicity; and the removal of entire objects
from the image like microphones or wires in special
effects.},
Key = {fds264933}
}

@article{fds264934,
Author = {Chung, DH and Sapiro, G},
Title = {Segmenting skin lesions with partial differential equations
based image processing algorithms},
Journal = {Ieee International Conference on Image Processing},
Volume = {3},
Pages = {[d]404-[d]407},
Year = {2000},
Month = {December},
Abstract = {In this paper, a PDE-based system for detecting the boundary
of skin lesions in digital clinical skin images is
presented. The image is first-processed via
contrast-enhancement and anisotropic diffusion. If the
lesion is covered by hairs, a PDE-based continuous
morphological filter that removes them is used as an
additional pre-processing step. Following these steps, the
skin lesion is segmented either by the geodesic active
contours model or the geodesic edge tracing approach. These
techniques are based on computing, again via PDE's, a
geodesic curve in a space defined by the image content.
Examples showing the performance of the algorithm are
given.},
Key = {fds264934}
}

@article{fds264938,
Author = {Chung, DH and Sapiro, G},
Title = {On the level lines and geometry of vector-valued
images},
Journal = {Ieee Signal Processing Letters},
Volume = {7},
Number = {9},
Pages = {241-243},
Publisher = {Institute of Electrical and Electronics Engineers
(IEEE)},
Year = {2000},
Month = {September},
ISSN = {1070-9908},
url = {http://dx.doi.org/10.1109/97.863143},
Abstract = {In this letter, we extend the concept of level lines of
scalar images to vector-valued data. Consistent with the
scalar case, we define the level-lines of vector-valued
images as the integral curves of the directions of minimal
vectorial change. This direction, and the magnitude of the
change, are computed using classical Riemannian geometry. As
an example of the use of this new concept, we show how to
visualize the basic geometry of vector-valued images with a
scalar image.},
Doi = {10.1109/97.863143},
Key = {fds264938}
}

@article{fds264936,
Author = {Bertalmio, M and Sapiro, G and Randall, G},
Title = {Morphing active contours},
Journal = {Ieee Transactions on Pattern Analysis and Machine
Intelligence},
Volume = {22},
Number = {7},
Pages = {733-737},
Publisher = {Institute of Electrical and Electronics Engineers
(IEEE)},
Year = {2000},
Month = {July},
ISSN = {0162-8828},
url = {http://dx.doi.org/10.1109/34.865191},
Abstract = {A method for deforming curves in a given image to a desired
position in a second image is introduced in this paper. The
algorithm is based on deforming the first image toward the
second one via a Partial Differential Equation (PDE), while
tracking the deformation of the curves of interest in the
first image with an additional, coupled, PDE. The tracking
is performed by projecting the velocities of the first
equation into the second one. In contrast with previous
PDE-based approaches, both the images and the curves on the
frames/slices of interest are used for tracking. The
technique can be applied to object tracking and sequential
segmentation. The topology of the deforming curve can change
without any special topology handling procedures added to
the scheme. This permits, for example, the automatic
tracking of scenes where, due to occlusions, the topology of
the objects of interest changes from frame to frame. In
addition, this work introduces the concept of projecting
velocities to obtain systems of coupled PDEs for image
analysis applications. We show examples for object tracking
and segmentation of electronic microscopy.},
Doi = {10.1109/34.865191},
Key = {fds264936}
}

@article{fds264937,
Author = {Chung, DH and Sapiro, G},
Title = {Segmenting skin lesions with partial-differential-equations-based
image processing algorithms.},
Journal = {Ieee Transactions on Medical Imaging},
Volume = {19},
Number = {7},
Pages = {763-767},
Year = {2000},
Month = {July},
ISSN = {0278-0062},
url = {http://www.ncbi.nlm.nih.gov/pubmed/11055791},
Abstract = {In this paper, a partial-differential equations (PDE)-based
system for detecting the boundary of skin lesions in digital
clinical skin images is presented. The image is first
preprocessed via contrast-enhancement and anisotropic
diffusion. If the lesion is covered by hairs, a PDE-based
continuous morphological filter that removes them is used as
an additional preprocessing step. Following these steps, the
skin lesion is segmented either by the geodesic active
contours model or the geodesic edge tracing approach. These
techniques are based on computing, again via PDEs, a
geodesic curve in a space defined by the image content.
Examples showing the performance of the algorithm are
given.},
Doi = {10.1109/42.875204},
Key = {fds264937}
}

@article{fds264816,
Author = {Faugeras, O and Nielsen, M and Perona, P and Romeny, BTH and Sapiro,
G},
Title = {Special Issue on the Second International Conference on
Scale Space Theory in Computer Vision},
Journal = {Journal of Visual Communication and Image
Representation},
Volume = {11},
Number = {2},
Pages = {95-95},
Publisher = {Elsevier BV},
Year = {2000},
Month = {June},
ISSN = {1047-3203},
Doi = {10.1006/jvci.1999.0436},
Key = {fds264816}
}

@article{fds264935,
Author = {Haker, S and Angenent, S and Tannenbaum, A and Kikinis, R and Sapiro, G and Halle, M},
Title = {Conformal surface parameterization for texture
mapping},
Journal = {Ieee Transactions on Visualization and Computer
Graphics},
Volume = {6},
Number = {2},
Pages = {181-189},
Publisher = {Institute of Electrical and Electronics Engineers
(IEEE)},
Year = {2000},
Month = {April},
ISSN = {1077-2626},
url = {http://dx.doi.org/10.1109/2945.856998},
Abstract = {In this paper, we give an explicit method for mapping any
simply connected surface onto the sphere in a manner which
preserves angles. This technique relies on certain conformal
mappings from differential geometry. Our method provides a
new way to automatically assign texture coordinates to
complex undulating surfaces. We demonstrate a finite element
method that can be used to apply our mapping technique to a
triangulated geometric description of a surface.},
Doi = {10.1109/2945.856998},
Key = {fds264935}
}

@article{fds264831,
Author = {Betelu, S and Sapiro, G and Tannenbaum, A and Giblin,
PJ},
Title = {Noise-resistant affine skeletons of planar
curves},
Journal = {Lecture Notes in Computer Science (Including Subseries
Lecture Notes in Artificial Intelligence and Lecture Notes
in Bioinformatics)},
Volume = {1842},
Pages = {742-754},
Publisher = {SPRINGER},
Editor = {Vernon, D},
Year = {2000},
Month = {January},
ISBN = {3540676856},
url = {http://www.informatik.uni-trier.de/~ley/db/conf/eccv/eccv2000-1.html},
Abstract = {© Springer-Verlag Berlin Heidelberg 2000. A new definition
of affine invariant skeletons for shape re- presentation is
introduced. A point belongs to the affine skeleton if and
only if it is equidistant from at least two points of the
curve, with the distance being a minima and given by the
areas between the curve and its corresponding chords. The
skeleton is robust, eliminating the need for curve
denoising. Previous approaches have used either the
Euclidean or affine distances, thereby resulting in a much
less robust computation. We propose a simple method to
compute the skeleton and give examples with real images, and
show that the proposed definition works also for noisy data.
We also demonstrate how to use this method to detect affine
skew symmetry.},
Doi = {10.1007/3-540-45054-8_48},
Key = {fds264831}
}

@article{fds264929,
Author = {Haker, S and Sapiro, G and Tannenbaum, A},
Title = {Knowledge-based segmentation of SAR data with learned
priors.},
Journal = {Ieee Transactions on Image Processing : a Publication of the
Ieee Signal Processing Society},
Volume = {9},
Number = {2},
Pages = {299-301},
Year = {2000},
Month = {January},
ISSN = {1057-7149},
url = {http://www.ncbi.nlm.nih.gov/pubmed/18255401},
Abstract = {An approach for the segmentation of still and video
synthetic aperture radar (SAR) images is described. A priori
knowledge about the objects present in the image, e.g.,
target, shadow and background terrain, is introduced via
Bayes' rule. Posterior probabilities obtained in this way
are then anisotropically smoothed, and the image
segmentation is obtained via MAP classifications of the
smoothed data. When segmenting sequences of images, the
smoothed posterior probabilities of past frames are used to
learn the prior distributions in the succeeding frame. We
show with examples from public data sets that this method
provides an efficient and fast technique for addressing the
segmentation of SAR data.},
Doi = {10.1109/83.821747},
Key = {fds264929}
}

@article{Weinberger2000,
Author = {Weinberger, MJ and Seroussi, G and Sapiro, G},
Title = {The LOCO-I lossless image compression algorithm: principles
and standardization into JPEG-LS.},
Journal = {Ieee Transactions on Image Processing : a Publication of the
Ieee Signal Processing Society},
Volume = {9},
Number = {8},
Pages = {1309-1324},
Year = {2000},
Month = {January},
ISSN = {1057-7149},
url = {http://www.ncbi.nlm.nih.gov/pubmed/18262969},
Abstract = {LOCO-I (LOw COmplexity LOssless COmpression for Images) is
the algorithm at the core of the new ISO/ITU standard for
lossless and near-lossless compression of continuous-tone
images, JPEG-LS. It is conceived as a "low complexity
projection" of the universal context modeling paradigm,
matching its modeling unit to a simple coding unit. By
combining simplicity with the compression potential of
context models, the algorithm "enjoys the best of both
worlds." It is based on a simple fixed context model, which
approaches the capability of the more complex universal
techniques for capturing high-order dependencies. The model
is tuned for efficient performance in conjunction with an
extended family of Golomb-type codes, which are adaptively
chosen, and an embedded alphabet extension for coding of
low-entropy image regions. LOCO-I attains compression ratios
similar or superior to those obtained with state-of-the-art
schemes based on arithmetic coding. Moreover, it is within a
few percentage points of the best available compression
ratios, at a much lower complexity level. We discuss the
principles underlying the design of LOCO-I, and its
standardization into JPEC-LS.},
Doi = {10.1109/83.855427},
Key = {Weinberger2000}
}

@article{fds264786,
Author = {Giblin, PJ and Sapiro, G},
Title = {Affine versions of the symmetry set},
Journal = {Real and Complex Singularities},
Volume = {412},
Pages = {173-187},
Year = {2000},
ISBN = {1-58488-142-9},
Key = {fds264786}
}

@article{fds264925,
Author = {Caselles, V and Sapiro, G and Chung, DH},
Title = {Vector median filters, inf-sup operations, and coupled
PDE's: Theoretical connections},
Journal = {Journal of Mathematical Imaging and Vision},
Volume = {12},
Number = {2},
Pages = {109-119},
Year = {2000},
url = {http://dx.doi.org/10.1023/A:1008310305351},
Abstract = {In this paper, we formally connect between vector median
filters, inf-sup morphological operations, and geometric
partial differential equations. Considering a lexicographic
order, which permits to define an order between vectors in
IRN, we first show that the vector median filter of a
vector-valued image is equivalent to a collection of
infimum-supremum morphological operations. We then proceed
and study the asymptotic behavior of this filter. We also
provide an interpretation of the infinitesimal iteration of
this vectorial median filter in terms of systems of coupled
geometric partial differential equations. The main component
of the vector evolves according to curvature motion, while,
intuitively, the others regularly deform their level-sets
toward those of this main component. These results extend to
the vector case classical connections between scalar median
filters, mathematical morphology, and mean curvature
motion.},
Doi = {10.1023/A:1008310305351},
Key = {fds264925}
}

@article{fds264926,
Author = {Tang, B and Sapiro, G and Caselles, V},
Title = {Diffusion of general data on non-flat manifolds via harmonic
maps theory: The direction diffusion case},
Journal = {International Journal of Computer Vision},
Volume = {36},
Number = {2},
Pages = {149-161},
Year = {2000},
url = {http://dx.doi.org/10.1023/A:1008152115986},
Abstract = {In a number of disciplines, directional data provides a
fundamental source of information. A novel framework for
isotropic and anisotropic diffusion of directions is
presented in this paper. The framework can be applied both
to denoise directional data and to obtain multiscale
representations of it. The basic idea is to apply and extend
results from the theory of harmonic maps, and in particular,
harmonic maps in liquid crystals. This theory deals with the
regularization of vectorial data, while satisfying the
intrinsic unit norm constraint of directional data. We show
the corresponding variational and partial differential
equations formulations for isotropic diffusion, obtained
from an L2 norm, and edge preserving diffusion, obtained
from an Lp norm in general and an L1 norm in particular. In
contrast with previous approaches, the framework is valid
for directions in any dimensions, supports non-smooth data,
and gives both isotropic and anisotropic formulations. In
addition, the framework of harmonic maps here described can
be used to diffuse and analyze general image data defined on
general non-flat manifolds, that is, functions between two
general manifolds. We present a number of theoretical
results, open questions, and examples for gradient vectors,
optical flow, and color images.},
Doi = {10.1023/A:1008152115986},
Key = {fds264926}
}

@article{fds264931,
Author = {Pardo, A and Sapiro, G},
Title = {Vector probability diffusion},
Journal = {Ieee International Conference on Image Processing},
Volume = {1},
Pages = {884-887},
Year = {2000},
Abstract = {A method for isotropic and anisotropic diffusion of vector
probabilities in general, and posterior probabilities in
particular, is introduced. The technique is based on
diffusing via coupled partial differential equations
restricted to the semi-hyperplane corresponding to
probability functions. Both the partial differential
equations and their corresponding numerical implementation
guarantee that the vector remains a probability vector,
having all its components positive and adding to one.
Applying the method to posterior probabilities in
classification problems, spatial and contextual coherences
is introduced before the MAP decision, thereby improving the
classification results.},
Key = {fds264931}
}

@article{fds264921,
Author = {Sapiro, G},
Title = {Color and illuminant voting},
Journal = {Ieee Transactions on Pattern Analysis and Machine
Intelligence},
Volume = {21},
Number = {11},
Pages = {1210-1215},
Publisher = {Institute of Electrical and Electronics Engineers
(IEEE)},
Year = {1999},
Month = {December},
ISSN = {0162-8828},
url = {http://dx.doi.org/10.1109/34.809114},
Abstract = {A gsometric-vision approach to color constancy and
illuminant estimation is presented in this paper. We show a
general framework, based on ideas from the generalized
probabilistic Hough translorm, to estimate the illuminant
and reflectance ot natural images. Each image pixel votes
lor possible illuminants and the estimation is based on
cumulative votes. The framework is natural for the
introduction of physical constraints in the color constancy
problem. We show the relationship of this work 1o previous
algorithms for color constancy and present examples. © 1999
IEEE.},
Doi = {10.1109/34.809114},
Key = {fds264921}
}

@article{fds264922,
Author = {Tang, B and Sapiro, G and Caselles, V},
Title = {Direction diffusion},
Journal = {Proceedings of the Ieee International Conference on Computer
Vision},
Volume = {2},
Pages = {1245-1252},
Publisher = {IEEE},
Year = {1999},
Month = {December},
url = {http://dx.doi.org/10.1109/iccv.1999.790423},
Abstract = {In a number of disciplines, directional data provides a
fundamental source of information. A novel framework for
isotropic and anisotropic diffusion of directions is
presented in this paper. The framework can be applied both
to regularize directional data and to obtain multiscale
representations of it. The basic idea is to apply and extend
results from the theory of harmonic maps in liquid crystals.
This theory deals with the regularization of vectorial data,
while satisfying the unit norm constraint of directional
data. We show the corresponding variational and partial
differential equations formulations for isotropic diffusion,
obtained from an L2norm, and edge preserving diffusion,
obtained from an L1norm. In contrast with previous
approaches, the framework is valid for directions in any
dimensions, supports non-smooth data, and gives both
isotropic and anisotropic formulations. We present a number
of theoretical results, open questions, and examples for
gradient vectors, optical flow, and color
images.},
Doi = {10.1109/iccv.1999.790423},
Key = {fds264922}
}

@article{fds264923,
Author = {Caselles, V and Sapiro, G and Chung, DH},
Title = {Vector median filters, morphology, and PDE's: Theoretical
connections},
Journal = {Ieee International Conference on Image Processing},
Volume = {4},
Pages = {177-181},
Year = {1999},
Month = {December},
Abstract = {In this paper, we formally connect between vector median
filters, morphological operators, and geometric partial
differential equations. Considering a lexicographic order,
which permits to define an order between vectors in IR N ,
we first show that the vector median filter of a
vector-valued image is equivalent to a collection of
infimum-supremum morphological operations. We then proceed
and study the asymptotic behavior of this filter. We also
provide an interpretation of the infinitesimal iteration of
this vectorial median filter in terms of systems of coupled
geometric partial differential equations. The main component
of the vector evolves according to curvature motion, while,
intuitively, the others regularly deform their level-sets
toward those of this main component. These results extend to
the vector case classical connections between scalar median
filters, mathematical morphology, and mean curvature
motions.},
Key = {fds264923}
}

@article{fds264924,
Author = {Weinberger, MJ and Seroussi, G and Sapiro, G},
Title = {From LOCO-I to the JPEG-LS standard},
Journal = {Ieee International Conference on Image Processing},
Volume = {4},
Pages = {68-72},
Year = {1999},
Month = {December},
Abstract = {LOCO-I (LOw COmplexity LOssless COmpression for Images) is
the algorithm at the core of the new ISO/ITU standard for
lossless and near-lossless compression of continuous-tone
images, JPEG-LS. The algorithm was conceived as a low
complexity projection' of the universal context modeling
paradigm, matching its modeling unit to a simple coding unit
based on Golomb codes. The JPEG-LS standard evolved after
successive refinements of the core algorithm, and a
description of its design principles and main algorithmic
components is presented in this paper. LOCO-I/JPEG-LS
attains compression ratios similar or superior to those
obtained with state-of-the-art schemes based on arithmetic
coding. Moreover, it is within a few percentage points of
the best available compression ratios, at a much lower
complexity level.},
Key = {fds264924}
}

@article{fds264918,
Author = {Bertalmio, M and Sapiro, G and Randall, G},
Title = {Region tracking on level-sets methods.},
Journal = {Ieee Transactions on Medical Imaging},
Volume = {18},
Number = {5},
Pages = {448-451},
Year = {1999},
Month = {May},
ISSN = {0278-0062},
url = {http://www.ncbi.nlm.nih.gov/pubmed/10416806},
Abstract = {Since the work by Osher and Sethian on level-sets algorithms
for numerical shape evolutions, this technique has been used
for a large number of applications in numerous fields. In
medical imaging, this numerical technique has been
successfully used, for example, in segmentation and cortex
unfolding algorithms. The migration from a Lagrangian
implementation to a Eulerian one via implicit
representations or level-sets brought some of the main
advantages of the technique, i.e., topology independence and
stability. This migration means also that the evolution is
parametrization free. Therefore, we do not know exactly how
each part of the shape is deforming and the point-wise
correspondence is lost. In this note we present a technique
to numerically track regions on surfaces that are being
deformed using the level-sets method. The basic idea is to
represent the region of interest as the intersection of two
implicit surfaces and then track its deformation from the
deformation of these surfaces. This technique then solves
one of the main shortcomings of the very useful level-sets
approach. Applications include lesion localization in
medical images, region tracking in functional MRI (fMRI)
visualization, and geometric surface mapping.},
Doi = {10.1109/42.774172},
Key = {fds264918}
}

@article{fds264833,
Author = {Black, MJ and Sapiro, G},
Title = {Edges as outliers: Anisotropic smoothing using local image
statistics},
Journal = {Lecture Notes in Computer Science (Including Subseries
Lecture Notes in Artificial Intelligence and Lecture Notes
in Bioinformatics)},
Volume = {1682},
Pages = {259-270},
Publisher = {SPRINGER},
Editor = {Nielsen, M and Johansen, P and Olsen, OF and Weickert,
J},
Year = {1999},
Month = {January},
ISBN = {354066498X},
url = {http://www.informatik.uni-trier.de/~ley/db/conf/scalespace/scalespace1999.html},
Abstract = {© Springer-Verlag Berlin Heidelberg 1999. Edges are viewed
as statistical outliers with respect to local image gradient
magnitudes. Within local image regions we compute a robust
statistical measure of the gradient variation and use this
in an anisotropic diffusion framework to determine a
spatially varying edge- stopping" parameter σ. We show how
to determine this parameter for two edge-stopping functions
described in the literature (Perona-Malik and the Tukey
biweight). Smoothing of the image is related the local
texture and in regions of low texture, small gradient values
may be treated as edges whereas in regions of high texture,
large gradient magni- tudes are necessary before an edge is
preserved. Intuitively these results have similarities with
human perceptual phenomena such as masking and popout.
Results are shown on a variety of standard
images.},
Doi = {10.1007/3-540-48236-9_23},
Key = {fds264833}
}

@article{fds264838,
Author = {Bertalmio, M and Sapiro, G and Randall, G},
Title = {Region tracking on surfaces deforming via level-sets
methods},
Journal = {Lecture Notes in Computer Science (Including Subseries
Lecture Notes in Artificial Intelligence and Lecture Notes
in Bioinformatics)},
Volume = {1682},
Pages = {330-338},
Publisher = {SPRINGER},
Editor = {Nielsen, M and Johansen, P and Olsen, OF and Weickert,
J},
Year = {1999},
Month = {January},
ISBN = {354066498X},
url = {http://www.informatik.uni-trier.de/~ley/db/conf/scalespace/scalespace1999.html},
Abstract = {© Springer-Verlag Berlin Heidelberg 1999. Since the work by
Osher and Sethian on level-sets algorithms for numerical
shape evolutions, this technique has been used for a large
number of applications in numerous fields. In medical
imaging, this numerical technique has been successfully used
for example in segmentation and cortex unfolding algorithms.
The migration from a Lagrangian im- plementation to an
Eulerian one via implicit representations or level-sets
brought some of the main advantages of the technique,
mainly, topology independence and stability. This migration
means also that the evolution is parametrization free, and
therefore we do not know exactly how each part of the shape
is deforming, and the point-wise correspondence is lost. In
this note we present a technique to numerically track
regions on sur- faces that are being deformed using the
level-sets method. The basic idea is to represent the region
of interest as the intersection of two implicit surfaces,
and then track its deformation from the deformation of these
surfaces. This technique then solves one of the main
shortcomings of the very useful level-sets approach.
Applications include lesion localization in medical images,
region tracking in functional MRI visualization, and
geometric surface mapping.},
Doi = {10.1007/3-540-48236-9_29},
Key = {fds264838}
}

@article{fds264839,
Author = {Bertalmio, M and Sapiro, G and Randall, G},
Title = {Morphing active contours},
Journal = {Lecture Notes in Computer Science (Including Subseries
Lecture Notes in Artificial Intelligence and Lecture Notes
in Bioinformatics)},
Volume = {1682},
Pages = {46-53},
Publisher = {SPRINGER},
Editor = {Nielsen, M and Johansen, P and Olsen, OF and Weickert,
J},
Year = {1999},
Month = {January},
ISBN = {354066498X},
url = {http://www.informatik.uni-trier.de/~ley/db/conf/scalespace/scalespace1999.html},
Abstract = {© Springer-Verlag Berlin Heidelberg 1999. A method for
deforming curves in a given image to a desired position in a
second image is introduced in this paper. The algorithm is
based on deforming the first image toward the second one via
a partial differential equation, while tracking the
deformation of the curves of interest in the first image
with an additional, coupled, partial differential equation.
The tracking is performed by projecting the velocities of
the first equation into the second one. In contrast with
previous PDE based approaches, both the images and the
curves on the frames/slices of interest are used for
tracking. The technique can be applied to object tracking
and sequential segmentation. The topology of the deforming
curve can change, without any special topology handling
procedures added to the scheme. This permits for example the
automatic tracking of scenes where, due to occlusions, the
topology of the objects of interest changes from frame to
frame. In addition, this work introduces the concept of
projecting velocities to obtain systems of coupled partial
differential equations for image analysis applications. We
show examples for object tracking and segmentation of
electronic microscopy. We also briefly discuss possible uses
of this framework îîfor three dimensional
morphing.},
Doi = {10.1007/3-540-48236-9_5},
Key = {fds264839}
}

@article{fds264843,
Author = {Chung, DH and Sapiro, G},
Title = {A windows-based user friendly system for image analysis with
partial differential equations},
Journal = {Lecture Notes in Computer Science (Including Subseries
Lecture Notes in Artificial Intelligence and Lecture Notes
in Bioinformatics)},
Volume = {1682},
Pages = {453-458},
Publisher = {SPRINGER},
Editor = {Nielsen, M and Johansen, P and Olsen, OF and Weickert,
J},
Year = {1999},
Month = {January},
ISBN = {354066498X},
url = {http://www.informatik.uni-trier.de/~ley/db/conf/scalespace/scalespace1999.html},
Abstract = {© Springer-Verlag Berlin Heidelberg 1999. In this paper we
present and briefly describe a Windows user-friendly system
designed to assist with the analysis of images in general,
and biomedical images in particular. The system, which is
being made publicly available to the research community,
implements basic 2D image analysis operations based on
partial differential equations (PDE’s). The system is
under continuous development, and already includes a large
number of image enhancement and segmentation routines that
have been tested for several applications.},
Doi = {10.1007/3-540-48236-9_42},
Key = {fds264843}
}

@article{fds264920,
Author = {Caselles, V and Lisani, JL and Morel, JM and Sapiro,
G},
Title = {Shape preserving local histogram modification.},
Journal = {Ieee Transactions on Image Processing : a Publication of the
Ieee Signal Processing Society},
Volume = {8},
Number = {2},
Pages = {220-230},
Year = {1999},
Month = {January},
ISSN = {1057-7149},
url = {http://www.ncbi.nlm.nih.gov/pubmed/18267469},
Abstract = {A novel approach for shape preserving contrast enhancement
is presented in this paper. Contrast enhancement is achieved
by means of a local histogram equalization algorithm which
preserves the level-sets of the image. This basic property
is violated by common local schemes, thereby introducing
spurious objects and modifying the image information. The
scheme is based on equalizing the histogram in all the
connected components of the image, which are defined based
both on the grey-values and spatial relations between pixels
in the image, and following mathematical morphology,
constitute the basic objects in the scene. We give examples
for both grey-value and color images.},
Doi = {10.1109/83.743856},
Key = {fds264920}
}

@article{fds264818,
Author = {Teo, PC and Sapiro, G and Wandell, BA},
Title = {Anisotropic smoothing of posterior probabilities},
Journal = {Dynamical Systems, Control, Coding, Computer
Vision},
Volume = {25},
Pages = {419-432},
Year = {1999},
ISBN = {3-7643-6060-7},
Key = {fds264818}
}

@article{fds264927,
Author = {Olver, PJ and Sapiro, G and Tannenbaum, A},
Title = {Affine invariant detection: Edge maps, anisotropic
diffusion, and active contours},
Journal = {Acta Applicandae Mathematicae},
Volume = {59},
Number = {1},
Pages = {45-77},
Year = {1999},
Abstract = {In this paper we undertake a systematic investigation of
affine invariant object detection and image denoising. Edge
detection is first presented from the point of view of the
affine invariant scale-space obtained by curvature based
motion of the image level-sets. In this case, affine
invariant maps are derived as a weighted difference of
images at different scales. We then introduce the affine
gradient as an affine invariant differential function of
lowest possible order with qualitative behavior similar to
the Euclidean gradient magnitude. These edge detectors are
the basis for the extension of the affine invariant
scale-space to a complete affine flow for image denoising
and simplification, and to define affine invariant active
contours for object detection and edge integration. The
active contours are obtained as a gradient flow in a
conformally Euclidean space defined by the image on which
the object is to be detected. That is, we show that objects
can be segmented in an affine invariant manner by computing
a path of minimal weighted affine distance, the weight being
given by functions of the affine edge detectors. The
gradient path is computed via an algorithm which allows to
simultaneously detect any number of objects independently of
the initial curve topology. Based on the same theory of
affine invariant gradient flows we show that the affine
geometric heat flow is minimizing, in an affine invariant
form, the area enclosed by the curve.},
Key = {fds264927}
}

@article{fds264911,
Author = {Vazquez, L and Sapiro, G and Randall, G},
Title = {Segmenting neurons in electronic microscopy via geometric
tracing},
Journal = {Ieee International Conference on Image Processing},
Volume = {3},
Pages = {814-818},
Year = {1998},
Month = {December},
Abstract = {In this paper we describe a system that is being used for
the segmentation of neurons in images obtained from
electronic microscopy. These images are extremely noisy, and
ordinary active contours techniques detect spurious objects
and fail to detect the neuron boundaries. The algorithm here
described is based on combining robust anisotropic diffusion
with minimal weighted-path computations. After the image is
regularized via anisotropic diffusion, the user clicks
points on the boundary of the desired object, and the
algorithm completes the boundary between those points. This
tracing is based on computing paths of minimal weighted
distance, where the weight is given by the image edge
content. Thanks to advanced numerical algorithms, the
algorithm is very fast and accurate. We compare our results
with those obtained with PictureIt, a commercially available
general purpose image processing package developed by
Microsoft.},
Key = {fds264911}
}

@article{fds264912,
Author = {Haker, S and Sapiro, G and Tannenbaum, A},
Title = {Knowledge-based segmentation of SAR images},
Journal = {Ieee International Conference on Image Processing},
Volume = {1},
Pages = {597-601},
Publisher = {IEEE Comput. Soc},
Year = {1998},
Month = {December},
url = {http://dx.doi.org/10.1109/icip.1998.723572},
Abstract = {A new approach for the segmentation of still and video SAR
images is described in this paper. A priori knowledge about
the objects present in the image, e.g., target, shadow, and
background terrain, is introduced via Bayes' rule. Posterior
probabilities obtained in this way are then anisotropically
smoothed, and the image segmentation is obtained via MAP
classifications of the smoothed data. When segmenting
sequences of images, the smoothed posterior probabilities of
past frames are used to learn the prior distributions in the
succeeding frame. We show, via a large number of examples
from public data sets, that this method provides an
efficient and fast technique for addressing the segmentation
of SAR data.},
Doi = {10.1109/icip.1998.723572},
Key = {fds264912}
}

@article{fds264913,
Author = {Teo, PC and Sapiro, G and Wandell, B},
Title = {Segmenting cortical gray matter for functional MRI
visualization},
Journal = {Proceedings of the Ieee International Conference on Computer
Vision},
Pages = {292-297},
Publisher = {Narosa Publishing House},
Year = {1998},
Month = {December},
url = {http://dx.doi.org/10.1109/iccv.1998.710733},
Abstract = {We describe a system that is being used to segment gray
matter and create connected cortical representations from
MRI. The method exploits knowledge of the anatomy of the
cortex and incorporates structural constraints into the
segmentation. First, the white matter and CSF regions in the
MR volume are segmented using some novel techniques of
posterior anisotropic diffusion. Then, the user selects the
cortical white matter component of interest, and its
structure is verified by checking for cavities and handles.
After this, a connected representation of the gray matter is
created by a constrained growing-out from the white matter
boundary. Because the connectivity is computed, the
segmentation can be used as input to several methods of
visualizing the spatial pattern of cortical activity within
gray matter. In our case, the connected representation of
gray matter is used to create a representation of the
flattened cortex. Then, fMRI measurements are overlaid on
the flattened representation, yielding a representation of
the volumetric data within a single image.},
Doi = {10.1109/iccv.1998.710733},
Key = {fds264913}
}

@article{fds264914,
Author = {Sapiro, G},
Title = {Bilinear voting},
Journal = {Proceedings of the Ieee International Conference on Computer
Vision},
Pages = {178-183},
Publisher = {Narosa Publishing House},
Year = {1998},
Month = {December},
url = {http://dx.doi.org/10.1109/iccv.1998.710716},
Abstract = {A geometric-vision approach to solve bilinear problems in
general, and the color constancy and illuminant estimation
problem in particular, is presented in this paper. We show a
general framework, based on ideas from the generalized
(probabilistic) Hough transform, to estimate the unknown
variables in the bilinear form. In the case of illuminant
and reflectance estimation in natural images, each image
pixel votes' for possible illuminants (or reflectance), and
the estimation is based on cumulative votes. In the general
case, the voting is for the parameters of the bilinear
model. The framework is natural for the introduction of
physical constraints. For the case of illuminant estimation,
we briefly show the relation of this work with previous
algorithms for color constancy, and present
examples.},
Doi = {10.1109/iccv.1998.710716},
Key = {fds264914}
}

@article{fds264915,
Author = {Giblin, PJ and Sapiro, G},
Title = {Affine invariant medial axis and skew symmetry},
Journal = {Proceedings of the Ieee International Conference on Computer
Vision},
Pages = {833-838},
Publisher = {Narosa Publishing House},
Year = {1998},
Month = {December},
url = {http://dx.doi.org/10.1109/iccv.1998.710814},
Abstract = {Affine invariant medial axes and symmetry sets of planar
shapes are introduced and studied in this paper. Two
different approaches are presented. The first one is based
on affine invariant distances, and defines the symmetry set,
a set containing the medial axis as the closure of the locus
of points on (at least) two affine normals and
affine-equidistant from the corresponding points on the
curve. The second approach is based on affine bitangent
conics. In this case the symmetry set is defined as the
closure of the locus of centers of conics with (at least)
three-point contact with two or more distinct points on the
curve. This is equivalent to conic and curve having, at
those points, the same affine tangent, or the same Euclidean
tangent and curvature. Although the two analogous
definitions for the classical Euclidean symmetry set (medial
axis) are equivalent, this is not the case for the affine
group. We then show how to use the symmetry set to detect
affine skew symmetry, proving that the contact based
symmetry set is a straight line if and only if the given
shape is the affine transformation of a symmetric
object.},
Doi = {10.1109/iccv.1998.710814},
Key = {fds264915}
}

@article{fds264917,
Author = {Bertalmio, M and Sapiro, G and Randall, G},
Title = {Morphing active contours: a geometric approach to
topology-independent image segmentation and
tracking},
Journal = {Ieee International Conference on Image Processing},
Volume = {3},
Pages = {318-322},
Year = {1998},
Month = {December},
Abstract = {A method for deforming curves in a given image to a desired
position in a second image is introduced in this paper. The
algorithm is based on deforming the first image toward the
second one via a partial differential equation, while
tracking the deformation of the curves of interest in the
first image with an additional, coupled, partial
differential equation. The technique can be applied to
abject tracking and slice-by-slice segmentation of 3D data.
The topology of the deforming curve can change, without any
special topology handling procedures added to the scheme.
This permits for example the automatic tracking of scenes
where, due to occlusions, the topology of the objects of
interest changes from frame to frame.},
Key = {fds264917}
}

@article{fds264919,
Author = {Weinberger, M and Seroussi, G and Sapiro, G},
Title = {LOCO-I lossless image compression algorithm: Principles and
standardization into JPEG-LS},
Number = {HPL-98-193},
Pages = {1-31},
Year = {1998},
Month = {November},
Abstract = {LOCO-I (LOw COmplexity LOssless COmpression for Images) is
the algorithm at the core of the new ISO/ITU standard for
lossless and near-lossless compression of continuous-tone
images, JPEG-LS. It is conceived as a low complexity
projection' of the universal context modeling paradigm,
matching its modeling unit to a simple coding unit. By
combining simplicity with the compression potential of
context models, the algorithm enjoys the best of both
worlds'. It is based on a simple fixed context model, which
approaches the capability of the more complex universal
techniques for capturing high-order dependencies. The model
is tuned for efficient performance in conjunction with an
extended family of Golomb-type codes, which are adaptively
chosen, and an embedded alphabet extension for coding of
low-entropy image regions. LOCO-I attains compression ratios
similar or superior to those obtained with state-of-the-art
schemes based on arithmetic coding. Moreover, it is within a
few percentage points of the best available compression
ratios, at a complexity level estimated at an order of
magnitude lower. We discuss the principles underlying the
design of LOCO-I, and its standardization into
JPEG-LS.},
Key = {fds264919}
}

@article{fds264809,
Author = {Caselles, V and Morel, J},
Title = {Introduction to the special issue on partial differential
equations and geometry-driven diffusion in image processing
and analysis.},
Journal = {Ieee Transactions on Image Processing : a Publication of the
Ieee Signal Processing Society},
Volume = {7},
Number = {3},
Pages = {269-273},
Year = {1998},
Month = {January},
ISSN = {1057-7149},
Doi = {10.1109/tip.1998.661176},
Key = {fds264809}
}

@article{fds264909,
Author = {Black, MJ and Sapiro, G and Marimont, DH and Heeger,
D},
Title = {Robust anisotropic diffusion.},
Journal = {Ieee Transactions on Image Processing : a Publication of the
Ieee Signal Processing Society},
Volume = {7},
Number = {3},
Pages = {421-432},
Year = {1998},
Month = {January},
ISSN = {1057-7149},
url = {http://www.ncbi.nlm.nih.gov/pubmed/18276262},
Abstract = {Relations between anisotropic diffusion and robust
statistics are described in this paper. Specifically, we
show that anisotropic diffusion can be seen as a robust
estimation procedure that estimates a piecewise smooth image
from a noisy input image. The "edge-stopping" function in
the anisotropic diffusion equation is closely related to the
error norm and influence function in the robust estimation
framework. This connection leads to a new "edge-stopping"
function based on Tukey's biweight robust estimator that
preserves sharper boundaries than previous formulations and
improves the automatic stopping of the diffusion. The robust
statistical interpretation also provides a means for
detecting the boundaries (edges) between the piecewise
smooth regions in an image that has been smoothed with
anisotropic diffusion. Additionally, we derive a
relationship between anisotropic diffusion and
regularization with line processes. Adding constraints on
the spatial organization of the line processes allows us to
develop new anisotropic diffusion equations that result in a
qualitative improvement in the continuity of
edges.},
Doi = {10.1109/83.661192},
Key = {fds264909}
}

@article{fds264910,
Author = {Giblin, PJ and Sapiro, G},
Title = {Affine-Invariant Distances, Envelopes and Symmetry
Sets},
Journal = {Geometriae Dedicata},
Volume = {71},
Number = {3},
Pages = {237-261},
Year = {1998},
Abstract = {Affine invariant symmetry sets of planar curves are
introduced and studied in this paper. Two different
approaches are investigated. The first one is based on
affine invariant distances, and defines the symmetry set as
the closure of the locus of points on (at least) two affine
normals and affine-equidistant from the corresponding points
on the curve. The second approach is based on affine
bitangent conics. In this case the symmetry set is defined
as the closure of the locus of centers of conics with (at
least) 3-point contact with the curve at two or more
distinct points on the curve. This is equivalent to conic
and curve having, at those points, the same affine tangent,
or the same Euclidean tangent and curvature. Although the
two analogous definitions for the classical Euclidean
symmetry set are equivalent, this is not the case for the
affine group. We present a number of properties of both
affine symmetry sets, showing their similarities with and
differences from the Euclidean case. We conclude the paper
with a discussion of possible extensions to higher
dimensions and other transformation groups, as well as to
invariant Voronoi diagrams.},
Key = {fds264910}
}

@article{fds264916,
Author = {Angenent, S and Sapiro, G and Tannenbaum, A},
Title = {On the affine heat equation for non-convex
curves},
Journal = {Journal of the American Mathematical Society},
Volume = {11},
Number = {3},
Pages = {601-634},
Year = {1998},
Key = {fds264916}
}

@article{fds264896,
Author = {Caselles, V and Kimmel, R and Sapiro, G and Sbert,
C},
Title = {Minimal surfaces based object segmentation},
Journal = {Ieee Transactions on Pattern Analysis and Machine
Intelligence},
Volume = {19},
Number = {4},
Pages = {394-398},
Publisher = {Institute of Electrical and Electronics Engineers
(IEEE)},
Year = {1997},
Month = {December},
ISSN = {0162-8828},
url = {http://dx.doi.org/10.1109/34.588023},
Abstract = {A geometric approach for 3D object segmentation and
representation is presented. The segmentation is obtained by
deformable surfaces moving towards the objects to be
detected in the 3D image. The model is based on curvature
motion and the computation of surfaces with minimal areas
better known as minimal surfaces. The space where the
surfaces are computed is induced from the 3D image
(volumetric data) in which the objects are to be detected.
The model links between classical deformable surfaces
obtained via energy minimization and intrinsic ones derived
from curvature based flows. The new approach is stable
robust and automatically handles changes in the surface
topology during the deformation. © 1997
IEEE.},
Doi = {10.1109/34.588023},
Key = {fds264896}
}

@article{fds264897,
Author = {Teo, PC and Sapiro, G and Wandell, BA},
Title = {Creating connected representations of cortical gray matter
for functional MRI visualization.},
Journal = {Ieee Transactions on Medical Imaging},
Volume = {16},
Number = {6},
Pages = {852-863},
Year = {1997},
Month = {December},
ISSN = {0278-0062},
url = {http://www.ncbi.nlm.nih.gov/pubmed/9533585},
Abstract = {We describe a system that is being used to segment gray
matter from magnetic resonance imaging (MRI) and to create
connected cortical representations for functional MRI
visualization (fMRI). The method exploits knowledge of the
anatomy of the cortex and incorporates structural
constraints into the segmentation. First, the white matter
and cerebral spinal fluid (CSF) regions in the MR volume are
segmented using a novel techniques of posterior anisotropic
diffusion. Then, the user selects the cortical white matter
component of interest, and its structure is verified by
checking for cavities and handles. After this, a connected
representation of the gray matter is created by a
constrained growing-out from the white matter boundary.
Because the connectivity is computed, the segmentation can
be used as input to several methods of visualizing the
spatial pattern of cortical activity within gray matter. In
our case, the connected representation of gray matter is
used to create a flattened representation of the cortex.
Then, fMRI measurements are overlaid on the flattened
representation, yielding a representation of the volumetric
data within a single image. The software is freely available
to the research community.},
Doi = {10.1109/42.650881},
Key = {fds264897}
}

@article{fds264898,
Author = {Black, M and Sapiro, G and Marimont, D and Heeger,
D},
Title = {Robust anisotropic diffusion and sharpening of scalar and
vector images},
Journal = {Ieee International Conference on Image Processing},
Volume = {1},
Pages = {263-266},
Publisher = {IEEE Comput. Soc},
Year = {1997},
Month = {December},
url = {http://dx.doi.org/10.1109/icip.1997.647755},
Abstract = {Relations between anisotropic diffusion and robust
statistics are described in this paper. We show that
anisotropic diffusion can be seen as a robust estimation
procedure that estimates a piecewise smooth image from a
noisy input image. The edge-stopping' function in the
anisotropic diffusion equation is closely related to the
error norm and influence function in the robust estimation
framework. This connection leads to a new edge-stopping'
function based on Tukey's biweight robust estimator, that
preserves sharper boundaries than previous formulations and
improves the automatic stopping of the diffusion. The robust
statistical interpretation also provides a means for
detecting the boundaries (edges) between the piecewise
smooth regions in the image. We extend the framework to
vector-valued images and show applications to robust image
sharpening.},
Doi = {10.1109/icip.1997.647755},
Key = {fds264898}
}

@article{fds264899,
Author = {Caselles, V and Lisani, JL and Morel, JM and Sapiro,
G},
Title = {Shape preserving local contrast enhancement},
Journal = {Ieee International Conference on Image Processing},
Volume = {1},
Pages = {314-317},
Publisher = {IEEE Comput. Soc},
Year = {1997},
Month = {December},
url = {http://dx.doi.org/10.1109/icip.1997.647769},
Abstract = {A novel approach for shape preserving contrast enhancement
is presented in this paper. Contrast enhancement is achieved
by means of a local histogram equalization algorithm which
preserves the level-sets of the image. This basic property
is violated by common local schemes, thereby introducing
spurious objects and modifying the image information. The
scheme is based on equalizing the histogram in all the
connected components of the image, which are defined based
on the image grey-values and spatial relations between its
pixels. Following mathematical morphology, these constitute
the basic objects in the scene. We give examples for both
grey-valued and color images.},
Doi = {10.1109/icip.1997.647769},
Key = {fds264899}
}

@article{fds264901,
Author = {Teo, PC and Sapiro, G and Wandell, BA},
Title = {Anisotropic smoothing of posterior probabilities},
Journal = {Ieee International Conference on Image Processing},
Volume = {1},
Pages = {675-678},
Publisher = {IEEE Comput. Soc},
Year = {1997},
Month = {December},
url = {http://dx.doi.org/10.1109/icip.1997.648003},
Abstract = {Recently, we proposed an efficient image segmentation
technique that anisotropically smoothes the homogeneous
posterior probabilities before independent pixelwise MAP
classification is carried out. In this paper, we develop the
mathematical theory underlying the technique. We demonstrate
that prior anisotropic smoothing of the posterior
probabilities yields the MAP solution of a discrete MRF with
a non-interacting, analog discontinuity field. In contrast,
isotropic smoothing of the posterior probabilities is
equivalent to computing the MAP solution of a single,
discrete MRF using continuous relaxation labeling. Combining
a discontinuity field with a discrete MRT is important as it
allows the disabling of clique potentials across
discontinuities. Furthermore, explicit representation of the
discontinuity field suggests new algorithms that incorporate
properties like hysteresis and non-maximal
suppression.},
Doi = {10.1109/icip.1997.648003},
Key = {fds264901}
}

@article{fds264775,
Author = {Sapiro, G},
Title = {The Vichy government - French - Baruch,MO},
Journal = {Actes De La Recherche En Sciences Sociales},
Number = {119},
Pages = {71-71},
Year = {1997},
Month = {September},
ISSN = {0335-5322},
Key = {fds264775}
}

@article{fds264906,
Author = {Ringach, DL and Sapiro, G and Shapley, R},
Title = {A subspace reverse-correlation technique for the study of
visual neurons.},
Journal = {Vision Research},
Volume = {37},
Number = {17},
Pages = {2455-2464},
Year = {1997},
Month = {September},
ISSN = {0042-6989},
url = {http://www.ncbi.nlm.nih.gov/pubmed/9381680},
Abstract = {A new discrete-time reverse-correlation scheme for the study
of visual neurons is proposed. The visual stimulus is
generated by drawing with uniform probability, at each
refresh time, an image from a finite set S of orthonormal
images. We show that if the neuron can be modeled as a
spatiotemporal linear filter followed by a static
nonlinearity, the cross-correlation between the input image
sequence and the cell's spike train output gives the
projection of the receptive field onto the subspace spanned
by S. The technique has been applied to the analysis of
simple cells in the primary visual cortex of cats and
macaque monkeys. Experimental results are presented where S
spans a subspace of spatially low-pass signals. Advantages
of the proposed scheme over standard white-noise techniques
include improved signal to noise ratios, increased spatial
resolution, and the possibility to restrict the study to
particular subspaces of interest.},
Doi = {10.1016/s0042-6989(96)00247-7},
Key = {fds264906}
}

@article{fds264772,
Author = {Sapiro, G and Simonin, A},
Title = {Les Editions de Minuit 1942-1955. Le devoir
d'insoumission},
Journal = {Le Mouvement Social},
Number = {180},
Pages = {244-244},
Publisher = {JSTOR},
Year = {1997},
Month = {July},
ISSN = {0027-2671},
Doi = {10.2307/3779380},
Key = {fds264772}
}

@article{fds264798,
Author = {Sapiro, G},
Title = {Les conditions professionnelles d'une mobilisation reussie:
le Comite national des ecrivains},
Journal = {Le Mouvement Social},
Number = {180},
Pages = {179-179},
Publisher = {JSTOR},
Year = {1997},
Month = {July},
ISSN = {0027-2671},
Doi = {10.2307/3779354},
Key = {fds264798}
}

@article{fds264823,
Author = {Sapiro, G},
Title = {'Alea' - French - Kjaerstad,J},
Journal = {Actes De La Recherche En Sciences Sociales},
Number = {118},
Pages = {5-5},
Year = {1997},
Month = {June},
ISSN = {0335-5322},
Key = {fds264823}
}

@article{fds264904,
Author = {Sapiro, G and Caselles, V},
Title = {Histogram Modification via Differential Equations},
Journal = {Journal of Differential Equations},
Volume = {135},
Number = {2},
Pages = {238-268},
Publisher = {Elsevier BV},
Year = {1997},
Month = {April},
url = {http://dx.doi.org/10.1006/jdeq.1996.3237},
Abstract = {The explicit use of partial differential equations (PDEs) in
image processing became a major research topic in the past
years. In this work we present a framework for histogram
(pixel-value distribution) modification via ordinary and
partial differential equations. In this way, the image
contrast is improved. We show that the histogram can be
modified to achieve any given distribution as the steady
state solution of an image flow. The contrast modification
can be performed while simultaneously reducing noise in a
unique PDE, avoiding noise sharpening effects of classical
algorithms. The approach is extended to local contrast
enhancement as well. A variational interpretation of the
flow is presented and theoretical results on the existence
Press.},
Doi = {10.1006/jdeq.1996.3237},
Key = {fds264904}
}

@article{fds264903,
Author = {Caselles, V and Lisani, JL and Morel, JM and Sapiro,
G},
Title = {Shape preserving local histogram modification},
Number = {97-58},
Pages = {1-20},
Year = {1997},
Month = {April},
Abstract = {A novel approach for shape preserving contrast enhancement
is presented in this paper. Contrast enhancement is achieved
by means of a local histogram equalization algorithm which
preserves the level-sets of the image. This basic property
is violated by common local schemes, thereby introducing
spurious objects and modifying the image information. The
scheme is based on equalizing the histogram in all the
connected components of the image, which are defined based
both on the grey-values and spatial relations between pixels
in the image, and following mathematical morphology,
constitute the basic objects in the scene. We give examples
for both grey-value and color images.},
Key = {fds264903}
}

@article{fds264900,
Author = {Pollick, FE and Sapiro, G},
Title = {Constant affine velocity predicts the 1/3 power law of
planar motion perception and generation.},
Journal = {Vision Research},
Volume = {37},
Number = {3},
Pages = {347-353},
Year = {1997},
Month = {February},
ISSN = {0042-6989},
url = {http://www.ncbi.nlm.nih.gov/pubmed/9135867},
Abstract = {Numerous studies have shown that the power of 1/3 is
important in relating Euclidean velocity to radius of
curvature (R) in the generation and perception of planar
movement. Although the relation between velocity and
curvature is clear and very intuitive, no valid explanation
for the specific 1/3 value has yet been found. We show that
if instead of computing the Euclidean velocity we compute
the affine one, a velocity which is invariant to affine
transformations, then we obtain that the unique function of
R which will give (constant) affine invariant velocity is
precisely R1/3. This means that the 1/3 power law,
experimentally found in the studies of hand-drawing and
planar motion perception, implies motion at constant affine
velocity. Since drawing/perceiving at constant affine
velocity implies that curves of equal affine length will be
drawn in equal time, we performed an experiment to further
support this result. Results showed agreement between the
1/3 power law and drawing at constant affine velocity.
Possible reasons for the appearance of affine
transformations in the generation and perception of planar
movement are discussed.},
Doi = {10.1016/s0042-6989(96)00116-2},
Key = {fds264900}
}

@article{fds264902,
Author = {Olver, PJ and Sapiro, G and Tannenbaum, A},
Title = {Invariant geometric evolutions of surfaces and volumetric
smoothing},
Journal = {Siam Journal on Applied Mathematics},
Volume = {57},
Number = {1},
Pages = {176-194},
Publisher = {Society for Industrial & Applied Mathematics
(SIAM)},
Year = {1997},
Month = {February},
url = {http://dx.doi.org/10.1137/s0036139994266311},
Abstract = {The study of geometric flows for smoothing, multiscale
representation, and analysis of two- and three-dimensional
objects has received much attention in the past few years.
In this paper, we first survey the geometric smoothing of
curves and surfaces via geometric heat-type flows, which are
invariant under the groups of Euclidean and affine motions.
Second, using the general theory of differential invariants,
we determine the general formula for a geometric
hypersurface evolution which is invariant under a prescribed
symmetry group. As an application, we present the simplest
affine invariant flow for (convex) surfaces in
three-dimensional space, which, like the affine-invariant
curve shortening flow, will be of fundamental importance in
the processing of three-dimensional images.},
Doi = {10.1137/s0036139994266311},
Key = {fds264902}
}

@article{fds264895,
Author = {Teo, PC and Sapiro, G and Wandell, BA},
Title = {Anatomically consistent segmentation of the human cortex for
functional MRI visualization},
Journal = {Hp Laboratories Technical Report},
Number = {97-3},
Pages = {1-21},
Year = {1997},
Month = {January},
Abstract = {We describe a system that is being used to segment gray
matter from volumetric representations of the human cortex
obtained using magnetic resonance imaging. The segmentation
algorithm identifies gray matter voxels and computes their
connectivity. The method differs from existing schemes in
that it exploits knowledge of the anatomy of human cortex
and produces anatomically consistent segmentations. The
method is based on a novel and computationally efficient
technique of incorporating structural constraints into the
segmentation algorithm. Because the gray matter segmentation
is anatomically consistent, it can be used together with
functional magnetic resonance imaging measurements to
visualize the spatial pattern of cortical activity within
the gray matter.},
Key = {fds264895}
}

@article{fds264905,
Author = {Caselles, V and Kimmel, R and Sapiro, G and Sbert,
C},
Title = {Minimal surfaces: A geometric three dimensional segmentation
approach},
Journal = {Numerische Mathematik},
Volume = {77},
Number = {4},
Pages = {423-451},
Publisher = {Springer Nature},
Year = {1997},
Month = {January},
url = {http://dx.doi.org/10.1007/s002110050294},
Abstract = {A novel geometric approach for three dimensional object
segmentation is presented. The scheme is based on geometric
deformable surfaces moving towards the objects to be
detected. We show that this model is related to the
computation of surfaces of minimal area (local minimal
surfaces). The space where these surfaces are computed is
induced from the three dimensional image in which the
objects are to be detected. The general approach also shows
the relation between classical deformable surfaces obtained
via energy minimization and geometric ones derived from
curvature flows in the surface evolution framework. The
scheme is stable, robust, and automatically handles changes
in the surface topology during the deformation. Results
related to existence, uniqueness, stability, and correctness
of the solution to this geometric deformable model are
presented as well. Based on an efficient numerical algorithm
for surface evolution, we present a number of examples of
object detection in real and synthetic images.},
Doi = {10.1007/s002110050294},
Key = {fds264905}
}

@article{fds264907,
Author = {Sapiro, G},
Title = {Color Snakes},
Journal = {Computer Vision and Image Understanding},
Volume = {68},
Number = {2},
Pages = {247-253},
Publisher = {Elsevier BV},
Year = {1997},
Month = {January},
url = {http://dx.doi.org/10.1006/cviu.1997.0562},
Abstract = {A framework for object segmentation in vector-valued images
is presented in this paper. The first scheme proposed is
based on geometric active contours moving toward the objects
to be detected in the vector-valued image. Object boundaries
are obtained as geodesies or minimal weighted-distance
curves, where the metric is given by a definition of edges
in vector-valued data. The curve flow corresponding to the
proposed active contours holds formal existence, uniqueness,
stability, and correctness results. The scheme automatically
handles changes in the deforming curve topology. The
technique is applicable, for example, to color and texture
images as well as multiscale representations. We then
present an extension of these vector active contours,
proposing a possible image flow for vector-valued image
segmentation. The algorithm is based on moving each one of
the image level sets according to the proposed vector active
contours. This extension also shows the relation between
active contours and a number of partial-differential-equations-based
image processing algorithms as anisotropic diffusion and
Doi = {10.1006/cviu.1997.0562},
Key = {fds264907}
}

@article{fds264908,
Author = {Sapiro, G and Caselles, V},
Title = {Contrast Enhancement via Image Evolution
Flows},
Journal = {Graphical Models and Image Processing},
Volume = {59},
Number = {6},
Pages = {407-416},
Publisher = {Elsevier BV},
Year = {1997},
Month = {January},
url = {http://dx.doi.org/10.1006/gmip.1997.0446},
Abstract = {A framework for contrast enhancement via image evolution
flows and variational formulations is introduced in this
paper. First, an algorithm for histogram modification via
image evolution equations is presented. We show that the
image histogram can be modified to achieve any given
distribution as the steady state solution of this
differential equation. We then prove that the proposed
evolution equation solves an energy minimization problem.
This gives a new interpretation to histogram modification
and contrast enhancement in general. This interpretation is
completely formulated in the image domain, in contrast with
classical techniques for histogram modification which are
formulated in a probabilistic domain. From this, new
algorithms for contrast enhancement, including, for example,
image and perception models, can be derived. Based on the
energy formulation and its corresponding differential form,
we show that the proposed histogram modification algorithm
can be combined with image regularization schemes. This
allows us to perform simulations contrast enhancement and
denoising, avoiding common noise sharpening effects in
classical schemes. Theoretical results regarding the
existence of solutions to the proposed equations are
Doi = {10.1006/gmip.1997.0446},
Key = {fds264908}
}

@article{Caselles1997,
Author = {Caselles, V and Kimmel, R and Sapiro, G},
Title = {Geodesic Active Contours},
Journal = {International Journal of Computer Vision},
Volume = {22},
Number = {1},
Pages = {61-79},
Year = {1997},
Month = {January},
ISSN = {0920-5691},
url = {http://dx.doi.org/10.1023/A:1007979827043},
Abstract = {A novel scheme for the detection of object boundaries is
presented. The technique is based on active contours
evolving in time according to intrinsic geometric measures
of the image. The evolving contours naturally split and
merge, allowing the simultaneous detection of several
objects and both interior and exterior boundaries. The
proposed approach is based on the relation between active
contours and the computation of geodesics or minimal
distance curves. The minimal distance curve lays in a
Riemannian space whose metric is defined by the image
content. This geodesic approach for object segmentation
allows to connect classical "snakes" based on energy
minimization and geometric active contours based on the
theory of curve evolution. Previous models of geometric
active contours are improved, allowing stable boundary
detection when their gradients suffer from large variations,
including gaps. Formal results concerning existence,
uniqueness, stability, and correctness of the evolution are
presented as well. The scheme was implemented using an
efficient algorithm for curve evolution. Experimental
results of applying the scheme to real images including
objects with holes and medical data imagery demonstrate its
power. The results may be extended to 3D object segmentation
as well.},
Doi = {10.1023/A:1007979827043},
Key = {Caselles1997}
}

@article{fds264834,
Author = {Black, MJ and Sapiro, G and Marimont, DH and Heeger,
D},
Title = {Robust Anisotropic Diffusion: Connections Between Robust
Statistics, Line Processing, and Anisotropic
Diffusion.},
Journal = {Scale Space},
Volume = {1252},
Pages = {323-326},
Publisher = {SPRINGER},
Editor = {Romeny, BMTH and Florack, L and Koenderink, JJ and Viergever,
MA},
Year = {1997},
ISBN = {3-540-63167-4},
url = {http://www.informatik.uni-trier.de/~ley/db/conf/scalespace/scalespace1997.html},
Abstract = {© Springer-Verlag Berlin Heidelberg 1997. Relations between
anisotropic diffusion and robust statistics are described in
this paper. We show that anisotropic diffusion can be seen
as a robust estimation procedure that estimates a piecewise
smooth image from a noisy input image. The "edge-stopping"
function in the anisotropic diffusion equation is closely
related to the error norm and influence function in the
robust estimation framework. This connection leads to a new
"edge-stopping" function based on Tukey's biweightrobust
estimator, that preserves sharper boundaries than previous
formulations and improves the automatic stopping of the
diffusion. The robust statistical interpretation also
provides a means for detecting the boundaries (edges)
between the piecewise smooth regions in the image. Finally,
connections between robust estimation and line processing
provide a framework to introduce spatial coherence in
anisotropic diffusion flows.},
Doi = {10.1007/3-540-63167-4_27},
Key = {fds264834}
}

@article{fds264894,
Author = {Sapiro, G and Cohen, A and Bruckstein, AM},
Title = {A Subdivision Scheme for Continuous-Scale B-Splines and
Affine-Invariant Progressive Smoothing},
Journal = {Journal of Mathematical Imaging and Vision},
Volume = {7},
Number = {1},
Pages = {23-40},
Year = {1997},
ISSN = {0924-9907},
Abstract = {Multiscale representations and progressive smoothing
constitute an important topic in different fields as
computer vision, CAGD, and image processing. In this work, a
multiscale representation of planar shapes is first
described. The approach is based on computing classical
B-splines of increasing orders, and therefore is
automatically affine invariant. The resulting representation
satisfies basic scale-space properties at least in a
qualitative form, and is simple to implement. The
representation obtained in this way is discrete in scale,
since classical B-splines are functions in Ck-2, where k is
an integer bigger or equal than two. We present a
subdivision scheme for the computation of B-splines of
finite support at continuous scales. With this scheme,
B-splines representations in Cr are obtained for any real r
in [0, ∞), and the multiscale representation is extended
to continuous scale. The proposed progressive smoothing
receives a discrete set of points as initial shape, while
the smoothed curves are represented by continuous
(analytical) functions, allowing a straightforward
computation of geometric characteristics of the
shape.},
Key = {fds264894}
}

@article{fds264764,
Author = {Sapiro, G},
Title = {Intellectuals in 19th-century Europe - Comparative history -
French - Charle,C},
Journal = {Actes De La Recherche En Sciences Sociales},
Number = {115},
Pages = {107-107},
Year = {1996},
Month = {December},
ISSN = {0335-5322},
Key = {fds264764}
}

@article{fds264804,
Author = {Sapiro, G},
Title = {Economics and humanism - From public utopia to struggle for
the Third World 1941-1996 - French - Pelletier,D},
Journal = {Actes De La Recherche En Sciences Sociales},
Number = {115},
Pages = {105-106},
Year = {1996},
Month = {December},
ISSN = {0335-5322},
Key = {fds264804}
}

@article{fds264890,
Author = {Sapiro, G},
Title = {From active contours to anisotropic diffusion: Connections
between basic PDE's in image processing},
Journal = {Ieee International Conference on Image Processing},
Volume = {1},
Pages = {477-480},
Year = {1996},
Month = {December},
Abstract = {In this paper, we present mathematical and qualitative
relations between a number of partial differential equations
frequently used in image processing and computer vision. We
show for example that classical active contours introduced
for object detection by Terzopoulos and colleagues are
connected to anisotropic diffusion flows as those defined by
Perona and Malik. We also deal with the relation of these
flows with shock filters and variational approaches for
image restoration.},
Key = {fds264890}
}

@article{fds264892,
Author = {Sapiro, G},
Title = {Vector (self) snakes: A geometric framework for color,
texture, and multiscale image segmentation},
Journal = {Ieee International Conference on Image Processing},
Volume = {1},
Pages = {817-820},
Year = {1996},
Month = {December},
Abstract = {A partial-differential-equations (PDE') based geometric
framework for segmentation of vector-valued images is
described in this paper. The first component of this
approach is based on two dimensional geometric active
contours deforming from their initial position towards
objects in the image. The boundaries of these objects are
then obtained as geodesics or minimal weighted distance
curves in a Riemannian space. The metric in this space is
given by a definition of edges in vector-valued images,
incorporating information from all the image components. The
curve flow corresponding to these active contours holds
formal existence, uniqueness, stability, and correctness
results. Then, embedding the deforming curve as the
level-set of the image, that is, deforming each one of the
image components level-sets according to these active
contours, a system of coupled PDE's is obtained. This system
deforms the image towards uniform regions, obtaining a
simplified (or segmented) image. The flow is related to a
number of PDE's based image processing algorithms as
anisotropic diffusion and shock filters. The technique is
applicable to color and texture images, as well as to vector
data obtained from general image decompositions.},
Key = {fds264892}
}

@article{fds264757,
Author = {Sapiro, G},
Title = {Mircea Eliade and the amnesia of history},
Journal = {Actes De La Recherche En Sciences Sociales},
Number = {114},
Pages = {5-5},
Year = {1996},
Month = {September},
ISSN = {0335-5322},
Key = {fds264757}
}

@article{fds264791,
Author = {Sapiro, G},
Title = {Dans les soulevements - French - Vargaftig,B},
Journal = {Actes De La Recherche En Sciences Sociales},
Number = {114},
Pages = {13-13},
Year = {1996},
Month = {September},
ISSN = {0335-5322},
Key = {fds264791}
}

@article{fds264800,
Author = {Sapiro, G},
Title = {Boarded-up windows - French - Vona,A},
Journal = {Actes De La Recherche En Sciences Sociales},
Number = {114},
Pages = {3-3},
Year = {1996},
Month = {September},
ISSN = {0335-5322},
Key = {fds264800}
}

@article{fds264770,
Author = {Sapiro, G},
Title = {Vichy and femininity - Political sociology of the order of
bodies - French - MuelDreyfus,F},
Journal = {Actes De La Recherche En Sciences Sociales},
Number = {113},
Pages = {103-104},
Year = {1996},
Month = {June},
ISSN = {0335-5322},
Key = {fds264770}
}

@article{fds264893,
Author = {Giblin, PJ and Sapiro, G},
Title = {Affine invariant distances, envelopes and symmetry
sets},
Journal = {Hp Laboratories Technical Report},
Number = {96-93},
Pages = {2-30},
Year = {1996},
Month = {June},
Abstract = {This work aims to present and study symmetry sets which are
affine invariant. Two alternatives to affine invariant
symmetry sets are presented. The first one is based on a
definition of affine invariant distances. The second
approach is based on affine bitangent conics. Following the
formal definitions of the affine symmetry sets, a number of
their properties are highlighted.},
Key = {fds264893}
}

@article{fds264759,
Author = {Sapiro, G},
Title = {For reasons of literature - French literature during the
occupation 1940-1944},
Journal = {Actes De La Recherche En Sciences Sociales},
Number = {111-12},
Pages = {3-&},
Year = {1996},
Month = {March},
ISSN = {0335-5322},
Key = {fds264759}
}

@article{fds264821,
Author = {Sapiro, G},
Title = {Salvation through literature and literature of salvation -
Two different paths of the Catholic novelists: Francois
Mauriac and Henry Bordeaux},
Journal = {Actes De La Recherche En Sciences Sociales},
Number = {111-12},
Pages = {36-&},
Year = {1996},
Month = {March},
ISSN = {0335-5322},
Key = {fds264821}
}

@article{fds264766,
Author = {Ringach, DL and Sapiro, G and Shapley, R},
Title = {A simple reverse correlation scheme for the identification
of visual neurons},
Journal = {Investigative Ophthalmology & Visual Science},
Volume = {37},
Number = {3},
Pages = {4178-4178},
Year = {1996},
Month = {February},
ISSN = {0146-0404},
Key = {fds264766}
}

@article{fds264891,
Author = {Ringach, DL and Sapiro, G and Shapley, R},
Title = {A simple reverse correlation scheme for the identification
of visual neurons},
Journal = {Investigative Ophthalmology & Visual Science},
Volume = {37},
Number = {3},
Pages = {S904},
Year = {1996},
Month = {February},
ISSN = {0146-0404},
Abstract = {Purpose. The standard approach to generalize the white-noise
technique to neural systems with multiple inputs consists of
using a spatio-temporal white noise stimulus. A drawback of
this methodology is that the input space to be explored is
huge, and only a sparse coverage can be achieved in limited
time. We propose a new discrete-time reverse correlation
technique that effectively reduces the dimension of the
input space, yielding higher signal to noise ratios. This is
achieved by exploiting a priori knowledge about the spatial
tuning properties of the neuron. Results. We first select a
set S of M orthonormal images of size N2 pixels. The idea is
to have M ≪ N2 and use previous knowledge about the
neuron's spatial tuning to select an appropriate input
space. An input image sequence is generated by selecting, at
each time, a random element from S. We prove that the
projection of the receptive field onto the subspace spanned
by the set S can be estimated based on measurements of the
crosscorrelation between the input image sequence and the
cell's output. The technique can also be applied to systems
that can be modeled as a linear receptive field followed by
a static nonlinearity. Examples are shown where S is a
subset of the complete two-dimensional discrete Hartley
basis functions. Conclusions. A simple reverse correlation
scheme that only requires the generation of a fixed number
of images can be used to identify quasi-linear visual
neurons. Prior knowledge of the spatial tuning of the cell
can be incorporated in the selection of an effective set of
stimulus images. We are currently applying this technique to
the analysis of V1 simple cells.},
Key = {fds264891}
}

@article{fds264837,
Author = {Caselles, V and Kimmel, R and Sapiro, G and Sbert,
C},
Title = {Three dimensional object modeling via minimal
surfaces},
Journal = {Lecture Notes in Computer Science (Including Subseries
Lecture Notes in Artificial Intelligence and Lecture Notes
in Bioinformatics)},
Volume = {1064},
Pages = {97-106},
Publisher = {SPRINGER},
Editor = {Buxton, BF and Cipolla, R},
Year = {1996},
Month = {January},
ISBN = {3540611223},
url = {http://www.informatik.uni-trier.de/~ley/db/conf/eccv/eccv1996-1.html},
Abstract = {© Springer-Verlag Berlin Heidelberg 1996. A novel geometric
approach for 3D object segmentation and representation is
presented. The scheme is based on geometric deformable
surfaces moving towards the objects to be detected. We show
that this model is equivalent to the computation of surfaces
of minimal area, better known as ’minimal surfaces,’ in
a Riemannian space. This space is defined by a metric
induced from the 3D image (volumetric data) in which the
objects are to be detected. The model shows the relation
between classical deformable surfaces obtained via energy
minimization, and geometric ones derived from curvature
based flows. The new approach is stable, robust, and
automatically handles changes in the surface topology during
the deformation. Based on an efficient numerical algorithm
for surface evolution, we present examples of object
detection in real and synthetic images.},
Doi = {10.1007/BFb0015526},
Key = {fds264837}
}

@article{fds264884,
Author = {Weinberger, MJ and Seroussi, G and Sapiro, G},
Title = {LOCO-I: a low complexity, context-based, lossless image
compression algorithm},
Journal = {Data Compression Conference Proceedings},
Pages = {140-149},
Publisher = {IEEE Comput. Soc. Press},
Year = {1996},
Month = {January},
url = {http://dx.doi.org/10.1109/dcc.1996.488319},
Abstract = {LOCO-I (LOw COmplexity LOssless COmpression for Images) is a
novel lossless compression algorithm for continuous-tone
images which combines the simplicity of Huffman coding with
the compression potential of context models, thus 'enjoying
the best of both worlds.' The algorithm is based on a simple
fixed context model, which approaches the capability of the
more complex universal context modeling techniques for
capturing high-order dependencies. The model is tuned for
efficient performance in conjunction with a collection of
(context-conditioned) Huffman codes, which is realized with
an adaptive, symbol-wise, Golomb-Rice code. LOCO-I attains,
in one pass, and without recourse to the higher complexity
arithmetic coders, compression ratios similar or superior to
those obtained with state-of-the-art schemes based on
arithmetic coding. In fact, LOCO-I is being considered by
the ISO committee as a replacement for the current lossless
standard in low-complexity applications.},
Doi = {10.1109/dcc.1996.488319},
Key = {fds264884}
}

@article{fds264885,
Author = {Sapiro, G},
Title = {Vector-valued active contours},
Journal = {Proceedings of the Ieee Computer Society Conference on
Computer Vision and Pattern Recognition},
Pages = {680-685},
Publisher = {IEEE},
Year = {1996},
Month = {January},
url = {http://dx.doi.org/10.1109/cvpr.1996.517146},
Abstract = {A framework for object segmentation in vector-valued images
is presented in this paper. The first scheme proposed is
based on geometric active contours moving towards the
objects to be detected in the vector-valved image. Objects
boundaries are obtained as geodesics or minimal weighted
distance curves in a Riemannian space. The metric in this
space is given by a definition of edges in vector-valued
images. The curve flow corresponding to the proposed active
contours holds formal existence, uniqueness, stability, and
correctness results. The techniques is applicable for
example to color and texture images. The scheme
automatically handles changes in the deforming curve
topology. We conclude the paper presenting an extension of
the color active contours which leads to a possible image
flow for vector-valued image segmentation. The algorithm is
based on moving each one of the image level-sets according
to the proposed color active contours. This extension also
shows the relation of the color geodesic active contours
with a number of partial-differential-equations based image
processing algorithms as anisotropic diffusion and shock
filters.},
Doi = {10.1109/cvpr.1996.517146},
Key = {fds264885}
}

@article{fds264886,
Author = {Sapiro, G and Ringach, DL},
Title = {Anisotropic diffusion of color images},
Journal = {Smart Structures and Materials 2005: Active Materials:
Behavior and Mechanics},
Volume = {2657},
Pages = {471-482},
Publisher = {SPIE},
Year = {1996},
Month = {January},
ISSN = {0277-786X},
url = {http://dx.doi.org/10.1117/12.238745},
Abstract = {A new approach for anisotropic diffusion processing of color
images is proposed. The main idea of the algorithm is to
facilitate diffusion of the image in the direction parallel
to color edges. The direction of maximal and minimal color
change at each point is computed using the first fundamental
form of the image in (L*a*b*) color space. The image Φ
evolves according to an anisotropic diffusion flow given by
δΦ/δt equals g(λ +, λ -)δ 2Φ/δξ 2, where ξ is the
direction of minimal color change. The diffusion
coefficient, g(λ +, λ -), is a function of the eigenvalues
of the first fundamental form, which represent the maximal
and minimal rates of color change. Examples for real color
images are presented.},
Doi = {10.1117/12.238745},
Key = {fds264886}
}

@article{fds264887,
Author = {Olver, PJ and Sapiro, G and Tannenbaum, A},
Title = {Affine invariant detection: edges, active contours, and
segments},
Journal = {Proceedings of the Ieee Computer Society Conference on
Computer Vision and Pattern Recognition},
Pages = {520-525},
Publisher = {IEEE},
Year = {1996},
Month = {January},
url = {http://dx.doi.org/10.1109/cvpr.1996.517121},
Abstract = {In this paper we undertake a systematic investigation of
affine invariant object detection. Edge detection is first
presented from the point of view of the affine invariant
scale-space obtained by curvature based motion of the image
level-sets. In this case, affine invariant edges are
obtained as a weighted difference of images at different
scales. We then introduce the affine gradient as the
simplest possible affine invariant differential function
which has the same qualitative behavior as the Euclidean
gradient magnitude. These edge detectors are the basis both
to extend the affine invariant scale-space to a complete
affine flow for image denoising and simplification, and to
define affine invariant active contours for object detection
and edge integration. The active contours are obtained as a
gradient flow in a conformally Euclidean space defined by
the image on which the object is to be detected. That is, we
show that objects can be segmented in an affine invariant
manner by computing a path of minimal weighted affine
distance, the weight being given by functions of the affine
edge detectors. The geodesic path is computed via an
algorithm which allows to simultaneously detect any number
of objects independently of the initial curve
topology.},
Doi = {10.1109/cvpr.1996.517121},
Key = {fds264887}
}

@article{fds264888,
Author = {Malladi, R and Kimmel, R and Adalsteinsson, D and Sapiro, G and Caselles, V and Sethian, JA},
Title = {Geometric approach to segmentation and analysis of 3D
medical images},
Journal = {Proceedings of the Workship on Mathematical Methods in
Biomedical Image Analysis},
Pages = {244-252},
Publisher = {IEEE},
Year = {1996},
Month = {January},
url = {http://dx.doi.org/10.1109/mmbia.1996.534076},
Abstract = {A geometric scheme for detecting, representing, and
measuring 3D medical data is presented. The technique is
based on deforming 3D surfaces, represented via level-sets,
towards the medical objects, according to intrinsic
geometric measures of the data. The 3D medical object is
represented as a (weighted) minimal surface in a Riemannian
space whose metric is induced from the image. This minimal
surface is computed using the level-set methodology for
propagating interfaces, combined with a narrow band
technique which allows fast implementation. This computation
technique automatically handles topological changes.
Measurements like volume and area are performed on the
surface, exploiting the representation and the high accuracy
intrinsic to the algorithm.},
Doi = {10.1109/mmbia.1996.534076},
Key = {fds264888}
}

@article{fds264889,
Author = {Sapiro, G and Ringach, DL},
Title = {Anisotropic diffusion of multivalued images with
applications to color filtering.},
Journal = {Ieee Transactions on Image Processing : a Publication of the
Ieee Signal Processing Society},
Volume = {5},
Number = {11},
Pages = {1582-1586},
Year = {1996},
Month = {January},
ISSN = {1057-7149},
url = {http://www.ncbi.nlm.nih.gov/pubmed/18290076},
Abstract = {A general framework for anisotropic diffusion of multivalued
images is presented. We propose an evolution equation where,
at each point in time, the directions and magnitudes of the
maximal and minimal rate of change in the vector-image are
first evaluated. These are given by eigenvectors and
eigenvalues of the first fundamental form in the given image
metric. Then, the image diffuses via a system of coupled
differential equations in the direction of minimal change.
The diffusion "strength" is controlled by a function that
measures the degree of dissimilarity between the
eigenvalues. We apply the proposed framework to the
filtering of color images represented in CIE-L*a*b*
space.},
Doi = {10.1109/83.541429},
Key = {fds264889}
}

@article{fds264751,
Author = {Ringach, DL and Carandini, M and Sapiro, G and Shapley,
R},
Title = {Cortical circuitry revealed by reverse correlation in the
orientation domain},
Journal = {Perception},
Volume = {25},
Pages = {31-31},
Year = {1996},
ISSN = {0301-0066},
Key = {fds264751}
}

@article{fds264880,
Author = {Sapiro, G and Caselles, V},
Title = {Histogram modification via partial differential
equations},
Journal = {Ieee International Conference on Image Processing},
Volume = {3},
Pages = {632-635},
Year = {1995},
Month = {December},
Abstract = {An algorithm for histogram modification via image evolution
equations is first presented in this paper. We show that the
image histogram can be modified to achieve any given
distribution as the steady state solution of this partial
differential equation. We then prove that this equation
corresponds to a gradient descent flow of a variational
problem. That is, the proposed PDE is solving an energy
minimization problem. This gives a new interpretation to
histogram modification and contrast enhancement in general.
This interpretation is completely formulated in the image
domain, in contrast with classical techniques for histogram
modification which are formulated in a probabilistic domain.
From this, new algorithms for contrast enhancement, which
include for example image modeling, can be derived. Based on
the energy formulation and its corresponding PDE, we show
that the proposed histogram modification algorithm can be
combined with denoising schemes. This allows to perform
simultaneous contrast enhancement and denoising, avoiding
common noise sharpening effects in classical algorithms. The
approach is extended to local contrast enhancement as well.
Theoretical results regarding the existence of solutions to
the proposed equations are presented.},
Key = {fds264880}
}

@article{fds264883,
Author = {Sapiro, G},
Title = {Geometric partial differential equations in image analysis:
past, present, and future},
Journal = {Ieee International Conference on Image Processing},
Volume = {3},
Pages = {1-4},
Year = {1995},
Month = {December},
Abstract = {In this paper I briefly discuss the main characteristics of
the use of partial differential equations and curve/surface
evolution theory in computer vision and image processing. I
will describe the approach and its main advantages, together
with a number of examples.},
Key = {fds264883}
}

@article{fds264789,
Author = {Sapiro, G and Casalles, V},
Title = {Image evolution approach for contrast enhancement},
Journal = {Investigative and Trial Image Processing},
Volume = {2567},
Pages = {19-30},
Publisher = {SPIE},
Year = {1995},
Month = {September},
ISBN = {0-8194-1926-5},
Doi = {10.1117/12.218484},
Key = {fds264789}
}

@article{fds264793,
Author = {Sapiro, G and Kimmel, R and Caselles, V},
Title = {Object detection and measurements in medical images
via geodesic deformable contours},
Journal = {Vision Geometry Iv},
Volume = {2573},
Pages = {366-378},
Publisher = {SPIE},
Year = {1995},
Month = {August},
ISBN = {0-8194-1932-X},
Doi = {10.1117/12.216429},
Key = {fds264793}
}

@article{fds264810,
Author = {Sapiro, G and Caselles, V},
Title = {Simultaneous contrast improvement and denoising via
diffusion-related equations},
Journal = {Vision Geometry Iv},
Volume = {2573},
Pages = {342-353},
Publisher = {SPIE},
Year = {1995},
Month = {August},
ISBN = {0-8194-1932-X},
Doi = {10.1117/12.216427},
Key = {fds264810}
}

@article{fds264787,
Author = {Sapiro, G and Steel, J},
Title = {Litteratures de l'ombre},
Journal = {Le Mouvement Social},
Number = {171},
Pages = {109-109},
Publisher = {JSTOR},
Year = {1995},
Month = {April},
ISSN = {0027-2671},
Doi = {10.2307/3779547},
Key = {fds264787}
}

@article{fds264765,
Author = {POLLICK, FE and SAPIRO, G},
Title = {CONSTANT AFFINE VELOCITY AND THE GENERATION AND PERCEPTION
OF UNIFORM PLANAR MOTION},
Journal = {Investigative Ophthalmology & Visual Science},
Volume = {36},
Number = {4},
Pages = {S360-S360},
Year = {1995},
Month = {March},
ISSN = {0146-0404},
Key = {fds264765}
}

@article{fds264881,
Author = {Sapiro, G and Bruckstein, AM},
Title = {The ubiquitous ellipse},
Journal = {Acta Applicandae Mathematicae},
Volume = {38},
Number = {2},
Pages = {149-161},
Publisher = {Springer Nature},
Year = {1995},
Month = {February},
ISSN = {0167-8019},
url = {http://dx.doi.org/10.1007/BF00992844},
Abstract = {We discuss three different affine invariant evolution
processes for smoothing planar curves. The first one is
derived from a geometric heat-type flow, both the initial
and the smoothed curves being differentiable. The second
smoothing process is obtained from a discretization of this
affine heat equation. In this case, the curves are
represented by planar polygons. The third process is based
on B-spline approximations. For this process, the initial
curve is a planar polygon, and the smoothed curves are
differentiable and even analytic. We show that, in the
limit, all three affine invariant smoothing processes
collapse any initial curve into an elliptic point. © 1995
Doi = {10.1007/BF00992844},
Key = {fds264881}
}

@article{fds264877,
Author = {Caselles, V and Kimmel, R and Sapiro, G},
Title = {Geodesic active contours},
Journal = {Ieee International Conference on Computer
Vision},
Pages = {694-699},
Publisher = {IEEE Comput. Soc. Press},
Year = {1995},
Month = {January},
url = {http://dx.doi.org/10.1109/iccv.1995.466871},
Abstract = {A novel scheme for the detection of object boundaries is
presented. The technique is based on active contours
deforming according to intrinsic geometric measures of the
image. The evolving contours naturally split and merge,
allowing the simultaneous detection of several objects and
both interior and exterior boundaries. The proposed approach
is based on the relation between active contours and the
computation of geodesics or minimal distance curves. The
minimal distance curve lays in a Riemannian space whose
metric is defined by the image content. This geodesic
approach for object segmentation allows to connect classical
'snakes' based on energy minimization and geometric active
contours based on the theory of curve evolution. Previous
models of geometric active contours are improved as showed
by a number of examples. Formal results concerning
existence, uniqueness, stability, and correctness of the
evolution are presented as well.},
Doi = {10.1109/iccv.1995.466871},
Key = {fds264877}
}

@article{fds264878,
Author = {Tannenbaum, A and Sapiro, G},
Title = {Area and Length Preserving Geometric Invariant
Scale-Spaces},
Journal = {Ieee Transactions on Pattern Analysis and Machine
Intelligence},
Volume = {17},
Number = {1},
Pages = {67-72},
Publisher = {Institute of Electrical and Electronics Engineers
(IEEE)},
Year = {1995},
Month = {January},
url = {http://dx.doi.org/10.1109/34.368150},
Abstract = {In this paper, area preserving multi-scale representations
of planar curves are described. This allows smoothing
without shrinkage at the same time preserving all the
scale-space properties. The representations are obtained
deforming the curve via geometric heat flows while
simultaneously magnifying the plane by a homethety which
keeps the enclosed area constant When the Euclidean
geometric heat flow is used, the resulting representation is
Euclidean invariant, and similarly it is affine invariant
when the affine one is used. The flows are geometrically
intrinsic to the curve, and exactly satisfy all the basic
requirements of scale-space representations. In the case of
the Euclidean heat flow, it is completely local as well. The
same approach is used to define length preserving geometric
flows. A similarity (scale) invariant geometric heat flow is
studied as well in this work. © 1995 IEEE},
Doi = {10.1109/34.368150},
Key = {fds264878}
}

@article{fds264882,
Author = {Kimmel, R and Sapiro, G},
Title = {Shortening three-dimensional curves via two-dimensional
flows},
Journal = {Computers & Mathematics With Applications},
Volume = {29},
Number = {3},
Pages = {49-62},
Publisher = {Elsevier BV},
Year = {1995},
Month = {January},
ISSN = {0898-1221},
url = {http://dx.doi.org/10.1016/0898-1221(94)00228-D},
Abstract = {In this paper, a curve evolution approach for the
computation of geodesic curves on 3D surfaces is presented.
The algorithm is based on deforming, via the curve
shortening flow, an arbitrary initial curve ending at two
given surface points. The 3D curve shortening flow is first
transformed into an equivalent 2D one. This 2D flow is
implemented, using an efficient numerical algorithm for
curve evolution with fixed end points. ©
1995.},
Doi = {10.1016/0898-1221(94)00228-D},
Key = {fds264882}
}

@article{fds264879,
Author = {Bruckstein, AM and Sapiro, G and Shaked, D},
Title = {Evolutions of planar polygons},
Journal = {International Journal of Pattern Recognition and Artificial
Intelligence},
Volume = {9},
Number = {6},
Pages = {991-1014},
Publisher = {World Scientific Pub Co Pte Lt},
Year = {1995},
url = {http://dx.doi.org/10.1142/S0218001495000407},
Abstract = {Evolutions of closed planar polygons are studied in this
work. In the first part of the paper, the general theory of
linear polygon evolutions is presented, and two specific
problems are analyzed. The first one is a polygonal analog
of a novel affine-invariant differential curve evolution,
for which the convergence of planar curves to ellipses was
proved. In the polygon case, convergence to polygonal
approximation of ellipses, polygonal ellipses, is proven.
The second one is related to cyclic pursuit problems, and
convergence, either to polygonal ellipses or to polygonal
circles, is proven. In the second part, two possible
polygonal analogues of the well-known Euclidean curve
shortening flow are presented. The models follow from
geometric considerations. Experimental results show that an
arbitrary initial polygon converges to either regular or
irregular polygonal approximations of circles when evolving
according to the proposed Euclidean flows.},
Doi = {10.1142/S0218001495000407},
Key = {fds264879}
}

@article{fds264820,
Author = {OLVER, PJ and SAPIRO, G and TANNENBAUM, A},
Title = {CLASSIFICATION AND UNIQUENESS OF INVARIANT GEOMETRIC
FLOWS},
Journal = {Comptes Rendus De L Academie Des Sciences Serie I
Mathematique},
Volume = {319},
Number = {4},
Pages = {339-344},
Year = {1994},
Month = {August},
ISSN = {0764-4442},
Key = {fds264820}
}

@article{fds264845,
Author = {Sapiro, G and Tannenbaum, A},
Title = {Area and length preserving geometric invariant
scale-spaces},
Journal = {Lecture Notes in Computer Science (Including Subseries
Lecture Notes in Artificial Intelligence and Lecture Notes
in Bioinformatics)},
Volume = {801 LNCS},
Pages = {449-458},
Publisher = {SPRINGER},
Editor = {Eklundh, J-O},
Year = {1994},
Month = {January},
ISBN = {9783540579571},
url = {http://www.informatik.uni-trier.de/~ley/db/conf/eccv/eccv1994-2.html},
Abstract = {© Springer-Verlag Berlin Heidelberg 1994. In this paper,
area preserving geometric multi-scale representations of
planar curves are described. This allows geometric smoothing
without shrinkage at the same time preserving all the
scale-space properties. The representations are obtained
deforming the curve via invariant geometric heat flows while
simultaneously magnifying the plane by a homethety which
keeps the enclosed area constant. The flows are
geometrically intrinsic to the curve, and exactly satisfy
all the basic requirements of scale-space representations.
In the case of the Euclidean heat flow for example, it is
completely local as well. The same approach is used to
define length preserving geometric flows. The geometric
scalespaces are implemented using an efficient numerical
algorithm.},
Doi = {10.1007/BFb0028376},
Key = {fds264845}
}

@article{fds264850,
Author = {Sapiro, G and Tannenbaum, A and You, YL and Kaveh,
M},
Title = {Experiments on geometric image enhancement},
Journal = {Proceedings International Conference on Image Processing,
Icip},
Volume = {2},
Pages = {472-476},
Publisher = {IEEE Comput. Soc. Press},
Year = {1994},
Month = {January},
ISBN = {0818669527},
url = {http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=3232},
Abstract = {© 1994 IEEE. In this paper we experiments with geometric
algorithms for image smoothing. Examples are given for MRI
and ATR data. We emphasize experiments with the affine
invariant geometric smoother or affine heat equation,
originally developed for binary shape smoothing, and found
to be efficient for gray-level images as well. Efficient
numerical implementations of these flows give anisotropic
diffusion processes which preserve edges.},
Doi = {10.1109/ICIP.1994.413615},
Key = {fds264850}
}

@article{fds264876,
Author = {Sapiro, G and Malah, D},
Title = {Morphological image coding based on a geometric sampling
theorem and a modified skeleton representation},
Journal = {Journal of Visual Communication and Image
Representation},
Volume = {5},
Number = {1},
Pages = {29-40},
Publisher = {Elsevier BV},
Year = {1994},
Month = {January},
ISSN = {1047-3203},
url = {http://dx.doi.org/10.1006/jvci.1994.1003},
Abstract = {A new approach for gray-level image coding using binary
morphological operations on the image bit-planes is
presented. This approach is based on a Geometric Sampling
Theorem (GST), and on a modified morphological skeleton. The
theorem, which is proved in this paper, states conditions
for the reconstruction of the boundary of a continuous two
level image from a unique subset of points of its skeleton
representation. This set of points, referred to as essential
points, is found to play an important role in the skeleton
representation of discrete binary images as well. The
modified morphological skeleton (MMS) uses an exponentially
increasing in size structuring element. The computational
advantage of this representation was previously reported. A
new approach to its development is presented here, and its
advantage in image coding is demonstrated. The coding scheme
consists of the following steps: First, the image is
preprocessed by an error-diffusion technique in order to
reduce the number of bit-planes from 8 to 4 without
significant quality degradation. The pixel values are
subsequently converted to Gray-code. The bit-planes are
represented by the MMS. Redundancy in this representation is
reduced using an algorithm motivated by the GST. These
reduced modified morphological skeletons are coded with an
entropy coding scheme particularly devised for efficient
skeleton coding. The possibility of the introduction of
geometric errors to reduce the bit-rate is also discussed.
Compression ratios of up to 11:1 were obtained for satellite
reserved.},
Doi = {10.1006/jvci.1994.1003},
Key = {fds264876}
}

@article{fds264749,
Author = {SAPIRO, G and BRUCKSTEIN, AM},
Title = {The ubiquitous ellipse},
Journal = {Curves and Surfaces in Geometric Design},
Pages = {409-418},
Year = {1994},
ISBN = {1-56881-039-3},
Key = {fds264749}
}

@article{fds264797,
Author = {Sapiro, G},
Title = {Geometric invariant signatures and flows:
classification and applications in image
analysis},
Journal = {Automatic Systems for the Identification and Inspection of
Humans},
Volume = {2277},
Pages = {275-287},
Publisher = {SPIE},
Year = {1994},
ISBN = {0-8194-1601-0},
Doi = {10.1117/12.191890},
Key = {fds264797}
}

@article{SAPIRO1994,
Author = {SAPIRO, G},
Title = {On affine plane curve evolution},
Journal = {J. of Functional Analysis},
Volume = {119},
Number = {1},
Pages = {79-120},
Publisher = {Elsevier BV},
Year = {1994},
ISSN = {0022-1236},
url = {http://dx.doi.org/10.1006/jfan.1994.1004},
Abstract = {An affine invariant curve evolution process is presented in
this work. The evolution studied is the affine analogue of
the Euclidean Curve Shortening flow. Evolution equations,
for both affine and Euclidean invariants, are developed. An
affine version of the classical (Euclidean) isoperimetric
inequality is proved. This inequality is used to show that
in the case of affine evolution of convex plane curves, the
affine isoperimetric ratio is a non-decreasing function of
time. Convergence of this affine isoperimetric ratio to the
ellipse′s value (8π2), as well as convergence, in the
Hausdorff metric, of the evolving curve to an ellipse, is
Doi = {10.1006/jfan.1994.1004},
Key = {SAPIRO1994}
}

@article{fds264875,
Author = {Sapiro, G and Tannenbaum, A},
Title = {Affine invariant scale-space},
Journal = {International Journal of Computer Vision},
Volume = {11},
Number = {1},
Pages = {25-44},
Publisher = {Springer Nature},
Year = {1993},
Month = {August},
ISSN = {0920-5691},
url = {http://dx.doi.org/10.1007/BF01420591},
Abstract = {A new affine invariant scale-space for planar curves is
presented in this work. The scale-space is obtained from the
solution of a novel nonlinear curve evolution equation which
admits affine invariant solutions. This flow was proved to
be the affine analogue of the well known Euclidean
shortening flow. The evolution also satisfies properties
such as causality, which makes it useful in defining a
scale-space. Using an efficient numerical algorithm for
curve evolution, this continuous affine flow is implemented,
and examples are presented. The affine-invariant progressive
smoothing property of the evolution equation is demonstrated
Doi = {10.1007/BF01420591},
Key = {fds264875}
}

@article{fds264873,
Author = {Sapiro, G and Kimmel, R and Shaked, D and Kimia, BB and Bruckstein,
AM},
Title = {Implementing continuous-scale morphology via curve
evolution},
Journal = {Pattern Recognition},
Volume = {26},
Number = {9},
Pages = {1363-1372},
Publisher = {Elsevier BV},
Year = {1993},
Month = {January},
ISSN = {0031-3203},
url = {http://dx.doi.org/10.1016/0031-3203(93)90142-J},
Abstract = {A new approach to digital implementation of continuous-scale
mathematical morphology is presented. The approach is based
on discretization of evolution equations associated with
continuous multiscale morphological operations. Those
equations, and their corresponding numerical implementation,
can be derived either directly from mathematical morphology
definitions or from curve evolution theory. The advantages
of the proposed approach over the classical discrete
Doi = {10.1016/0031-3203(93)90142-J},
Key = {fds264873}
}

@article{fds264874,
Author = {Sapiro, G and Tannenbaum, AR},
Title = {Formulating invariant heat-type curve flows},
Journal = {Smart Structures and Materials 2005: Active Materials:
Behavior and Mechanics},
Volume = {2031},
Pages = {234-245},
Publisher = {SPIE},
Year = {1993},
Month = {January},
url = {http://dx.doi.org/10.1117/12.146629},
Abstract = {We describe a geometric method for formulating planar curve
evolution equations which are invariant under a certain
transformation group. The approach is based on concepts from
the classical theory of differential invariants. The flows
we obtain are geometric analogues of the classical heat
equation, and can be used to define invariant scale-spaces.
We give a high-level' general procedure for the
construction of these flows. Examples are presented for
viewing transformations.},
Doi = {10.1117/12.146629},
Key = {fds264874}
}

@article{fds264799,
Author = {SAPIRO, G and TANNENBAUM, A and UNIV, JH},
Title = {IMAGE SMOOTHING BASED ON AN AFFINE INVARIANT CURVE
FLOW},
Journal = {Proceedings of the Twenty Seventh Annual Conference on
Information Sciences and Systems},
Pages = {196-201},
Year = {1993},
Key = {fds264799}
}

@article{fds264814,
Author = {SAPIRO, G},
1940S - FUNCTION AND OPERATION OF LITERARY INSTITUTIONS IN A
PERIOD OF NATIONAL CRISIS},
Journal = {Texte Revue De Critique Et De Theorie Litteraire},
Number = {12},
Pages = {151-196},
Year = {1992},
ISSN = {0715-8920},
Key = {fds264814}
}

@article{fds264754,
Author = {SAPIRO, G and MALAH, D},
Title = {A GEOMETRIC SAMPLING THEOREM AND ITS APPLICATION IN
MORPHOLOGICAL IMAGE-CODING},
Journal = {Digital Signal Processing 91},
Pages = {410-415},
Year = {1991},
ISBN = {0-444-88890-X},
Key = {fds264754}
}

@article{fds264867,
Author = {Whitsel, BL and Petrucelli, LM and Sapiro, G and Ha,
H},
Title = {Fiber sorting in the fasciculus gracilis of squirrel
monkeys.},
Journal = {Experimental Neurology},
Volume = {29},
Number = {2},
Pages = {227-242},
Year = {1970},
Month = {November},
ISSN = {0014-4886},
url = {http://www.ncbi.nlm.nih.gov/pubmed/4994110},
Doi = {10.1016/0014-4886(70)90054-3},
Key = {fds264867}
}

@article{fds264866,
Author = {Whitsel, BL and Petrucelli, LM and Sapiro, G},
Title = {Modality representation in the lumbar and cervical
fasciculus gracilis of squirrel monkeys.},
Journal = {Brain Research},
Volume = {15},
Number = {1},
Pages = {67-78},
Year = {1969},
Month = {September},
ISSN = {0006-8993},
url = {http://www.ncbi.nlm.nih.gov/pubmed/4241233},
Doi = {10.1016/0006-8993(69)90310-2},
Key = {fds264866}
}

`

dept@math.duke.edu
ph: 919.660.2800
fax: 919.660.2821

Mathematics Department
Duke University, Box 90320
Durham, NC 27708-0320