Detachable Object Detection with Efficient Model Selection
We describe a computationally efficient scheme to perform model selection while simultaneously segmenting a short video stream into an unknown number of detachable objects. Detachable objects are regions of space bounded by surfaces that are surrounded by the medium other than for their region of su...
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Published in | Energy Minimization Methods in Computer Vision and Pattern Recognition pp. 191 - 204 |
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Main Authors | , |
Format | Book Chapter |
Language | English |
Published |
Berlin, Heidelberg
Springer Berlin Heidelberg
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Series | Lecture Notes in Computer Science |
Subjects | |
Online Access | Get full text |
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Summary: | We describe a computationally efficient scheme to perform model selection while simultaneously segmenting a short video stream into an unknown number of detachable objects. Detachable objects are regions of space bounded by surfaces that are surrounded by the medium other than for their region of support, and the region of support changes over time. These include humans walking, vehicles moving, etc. We exploit recent work on occlusion detection to bootstrap an energy minimization approach that is solved with linear programming. The energy integrates both appearance and motion statistics, and can be used to seed layer segmentation approaches that integrate temporal information on long timescales. |
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Bibliography: | Research supported by ARO 56765, ONR N000140810414, AFOSR FA95500910427. |
ISBN: | 3642230938 9783642230936 |
ISSN: | 0302-9743 1611-3349 |
DOI: | 10.1007/978-3-642-23094-3_14 |