Title :
Learning spatiotemporal T-junctions for occlusion detection
Author :
Apostoloff, Nicholas ; Fitzgibbon, Andrew
Author_Institution :
Robotics Res. Group, Oxford Univ., UK
Abstract :
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 hand-labelled data using SIFT to capture its redundancy. This detector is then demonstrated in a novel occlusion detector that fuses Canny edges and T-junctions in the spatiotemporal domain to detect occluding edges in the spatial domain.
Keywords :
edge detection; feature extraction; hidden feature removal; image classification; image motion analysis; image segmentation; learning (artificial intelligence); RVM T-junction classifier; computer vision; feature extraction; image detection; motion segmentation; neuroscience; occlusion detection; spatiotemporal T-junction; Computer vision; Detectors; Fuses; Image edge detection; Motion detection; Motion segmentation; Neuroscience; Photometry; Redundancy; Spatiotemporal phenomena;
Conference_Titel :
Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on
Print_ISBN :
0-7695-2372-2
DOI :
10.1109/CVPR.2005.206