Math @ Duke
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Publications [#304063] of Guillermo Sapiro
Papers Published
- Hashemi, J; Spina, TV; Tepper, M; Esler, A; Morellas, V; Papanikolopoulos, N; Sapiro, G, Computer vision tools for the non-invasive assessment of autism-related
behavioral markers, vol. abs/1210.7014
(October, 2012) [1210.7014v2]
(last updated on 2025/02/02)
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 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 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.
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