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Carnegie Mellon Robotics Institute

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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.

Notes

Associated Center(s) / Consortia: Vision and Autonomous Systems Center

Number of pages: 10

Note: Dataset available from http://www.cs.cmu.edu/~stein/occlusion_data

Text Reference

Andrew Stein and Martial Hebert, "Combining Local Appearance and Motion Cues for Occlusion Boundary Detection," British Machine Vision Conference (BMVC), September, 2007.

BibTeX Reference

@inproceedings{Stein_2007_5835,
   author = "Andrew Stein and Martial Hebert",
   title = "Combining Local Appearance and Motion Cues for Occlusion Boundary Detection",
   booktitle = "British Machine Vision Conference (BMVC)",
   month = "September",
   year = "2007",
   Notes = "Dataset available from http://www.cs.cmu.edu/~stein/occlusion_data"
}

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