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Visual Tracking by Local Superpixel Matching with Markov Random Field Read chapter Read first chapter

2016 | OriginalPaper | Chapter

Stereo Matching Based on CF-EM Joint Algorithm

Authors : Baoping Li, Long Ye, Yun Tie, Qin Zhang

Published in: Advances in Multimedia Information Processing - PCM 2016

Publisher: Springer International Publishing

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Abstract

Cost Filtering (CF) and Energy Minimization (EM) are two main cost aggregation methods in stereo matching. Due to global smoothness assumption, EM methods can get higher matching accuracy. However, they tend to fail in occluded areas, while locally adaptive support-weight CF method can solve it well. This paper proposed a CF-EM joint stereo matching framework on the basis of the proof that CF and EM methods can realize interconversion to each other. In this joint framework, we firstly use CF method with fully connected Markov Random Field (F-MRF) model to yield a more robust unary potential. And then, the output unary potential is used as the input to a standard EM method to compute the final disparity in Local connected MRF (L-MRF) model. Experiments results demonstrate that our method can improve the stereo matching accuracy as the achievement of energy transferring from F-MRF to L-MRF.

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Appendix
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Metadata
Title
Stereo Matching Based on CF-EM Joint Algorithm
Authors
Baoping Li
Long Ye
Yun Tie
Qin Zhang
Copyright Year
2016
Book
Advances in Multimedia Information Processing - PCM 2016

Print ISBN: 978-3-319-48889-9

Electronic ISBN: 978-3-319-48890-5

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-48890-5_27