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Learning spatiotemporal T-junctions for occlusion detection Nicholas Apostoloff and Andrew Fitzgibbon
 

Summary: Learning spatiotemporal T-junctions for occlusion detection
Nicholas Apostoloff and Andrew Fitzgibbon
Robotics Research Group
University of Oxford
Oxford, OX1 4AJ, UK
{nema, awf}@robots.ox.ac.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 struc-

  

Source: Apostoloff, Nicholas - Department of Engineering Science, University of Oxford
Zisserman, Andrew - Department of Engineering Science, University of Oxford

 

Collections: Computer Technologies and Information Sciences; Engineering