Local occlusion detection under deformations using topological invariants

Authors:
Edgar Lobaton;Ram Vasudevan;Ruzena Bajcsy;Ron Alterovitz
Affiliations:
Department of Computer Science, University of North Carolina at Chapel Hill, NC;Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA;Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA;Department of Computer Science, University of North Carolina at Chapel Hill, NC
Venue:
ECCV'10 Proceedings of the 11th European conference on computer vision conference on Computer vision: Part III
Year:
2010

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Abstract

Occlusions provide critical cues about the 3D structure of man-made and natural scenes. We present a mathematical framework and algorithm to detect and localize occlusions in image sequences of scenes that include deforming objects. Our occlusion detector works under far weaker assumptions than other detectors. We prove that occlusions in deforming scenes occur when certain well-defined local topological invariants are not preserved. Our framework employs these invariants to detect occlusions with a zero false positive rate under assumptions of bounded deformations and color variation. The novelty and strength of this methodology is that it does not rely on spatio-temporal derivatives or matching, which can be problematic in scenes including deforming objects, but is instead based on a mathematical representation of the underlying cause of occlusions in a deforming 3D scene. We demonstrate the effectiveness of the occlusion detector using image sequences of natural scenes, including deforming cloth and hand motions.