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

Math @ Duke



Publications [#264715] of Guillermo Sapiro

Papers Published

  1. Hashemi, J; Tepper, M; Vallin Spina, T; Esler, A; Morellas, V; Papanikolopoulos, N; Egger, H; Dawson, G; Sapiro, G, Computer vision tools for low-cost and noninvasive measurement of autism-related behaviors in infants., Autism Research and Treatment, vol. 2014 (2014), pp. 935686, ISSN 2090-1925 [repository], [doi]
    (last updated on 2019/06/24)

    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.
ph: 919.660.2800
fax: 919.660.2821

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