Department of Mathematics
 Search | Help | Login | pdf version | printable version

Math @ Duke



Publications [#335972] of Guillermo Sapiro

Papers Published

  1. Tepper, M; Sapiro, G, Nonnegative matrix underapproximation for robust multiple model fitting, Proceedings 30th Ieee Conference on Computer Vision and Pattern Recognition, Cvpr 2017, vol. 2017-January (November, 2017), pp. 655-663, IEEE, ISBN 9781538604571 [doi]
    (last updated on 2019/06/26)

    © 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 traditional NMF, they present additional sparsity and 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.
ph: 919.660.2800
fax: 919.660.2821

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