NDLI: When Occlusions are Outliers

Content Provider	IEEE Xplore Digital Library
Author	Goldenstein, S. Vogler, C.
Copyright Year	2006
Description	Author affiliation: Universidade Estadual de Campinas, Brazil (Goldenstein, S.)
Abstract	In many tracking applications, the deformable object of interest suffers from frequent occlusions. Traditional augmenting methods use templates and measures of similarity to recover from occlusions. In this paper, we break with these methods. Instead, we model bad image correspondences, which are induced by occlusions, as statistical outliers in the context of tracking high-dimensional deformable models. This interpretation allows us to use robust statistical estimators in the deformable models parameter space to detect and eliminate such outliers. Because fast-moving occlusions can generate an excessively large outlier to inlier ratio in the occluded areas, we combine the robust statistical estimation with an initial rejection of correspondences based on the magnitude of the optical flow, a simple 2D criterion. To improve robustness even further, we have the final outlier rejection test take into account both the statistical distribution of the deformable models parameters, and that different parameters are affected by different subsets of correspondences. We validate and demonstrate our technique on real sequences of American Sign Language, which exhibit frequent and extensive occlusions caused by fast movement of the subjects hands.
Starting Page	98
Ending Page	98
File Size	1181431
Page Count	1
File Format	PDF
ISBN	0769526462
DOI	10.1109/CVPRW.2006.215
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-06-17
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Deformable models Computer vision Image motion analysis Handicapped aids Statistical distributions Robustness Cognition Application software Context modeling Testing
Content Type	Text
Resource Type	Article

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3D Pose Estimation via Elliptical Fourier Descriptors for Deformable Hand Representations

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When Occlusions are Outliers

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