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Learning Spatiotemporal T-Junctions for Occlusion Detection

9 years 5 months ago
Learning Spatiotemporal T-Junctions for Occlusion Detection
The goal of motion segmentation and layer extraction can be viewed as the detection and localization of occluding surfaces. A feature that has been shown to be a particularly strong indicator of occlusion, in both computer vision and neuroscience, is the T-junction; however, little progress has been made in T-junction detection. One reason for this is the difficulty in distinguishing false T-junctions (i.e. those not on an occluding edge) and real T-junctions in cluttered images. In addition to this, their photometric profile alone is not enough for reliable detection. This paper overcomes the first problem by searching for T-junctions not in space, but in space-time. This removes many false T-junctions and creates a simpler image structure to explore. The second problem is mitigated by learning the appearance of T-junctions in these spatiotemporal images. An RVM T-junction classifier is learnt from handlabelled data using SIFT to capture its redundancy. This detector is then demonstr...
Nicholas Apostoloff, Andrew W. Fitzgibbon
Added 12 Oct 2009
Updated 29 Oct 2009
Type Conference
Year 2005
Where CVPR
Authors Nicholas Apostoloff, Andrew W. Fitzgibbon
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