Home/Combining Local Appearance and Motion Cues for Occlusion Boundary Detection

Combining Local Appearance and Motion Cues for Occlusion Boundary Detection

Andrew Stein and Martial Hebert
Carnegie Mellon University, British Machine Vision Conference (BMVC), September, 2007

Download Publication (PDF)

Copyright notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author’s copyright. These works may not be reposted without the explicit permission of the copyright holder.

Abstract

Building on recent advances in the detection of appearance edges from multiple local cues, we present an approach for detecting occlusion boundaries which also incorporates local motion information. We argue that these boundaries have physical significance which makes them important for many high-level vision tasks and that motion offers a unique, often critical source of additional information for detecting them. We provide a new dataset of natural image sequences with labeled occlusion boundaries, on which we learn a classifier that leverages appearance cues along with motion estimates from either side of an edge. We demonstrate improved performance for pixelwise differentiation of occlusion boundaries from non-occluding edges by combining these weak local cues, as compared to using them separately. The results are suitable as improved input to subsequent mid- or high-level reasoning methods.

BibTeX Reference
@conference{Stein-2007-9812,
title = {Combining Local Appearance and Motion Cues for Occlusion Boundary Detection},
author = {Andrew Stein and Martial Hebert},
booktitle = {British Machine Vision Conference (BMVC)},
notes = {Dataset available from http://www.cs.cmu.edu/~stein/occlusion_data},
school = {Robotics Institute , Carnegie Mellon University},
month = {September},
year = {2007},
address = {Pittsburgh, PA},
}
2017-09-13T10:42:02+00:00