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Stereo Matching Using Belief Propagation

  • Jian Sun
  • Heung-Yeung Shum
  • Nan-Ning Zheng
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2351)

Abstract

In this paper, we formulate the stereo matching problem as a Markov network consisting of three coupled Markov random fields (MRF’s). These three MRF’s model a smooth field for depth/disparity, a line process for depth discontinuity and a binary process for occlusion, respectively. After eliminating the line process and the binary process by introducing two robust functions, we obtain the maximum a posteriori (MAP) estimation in the Markov network by applying a Bayesian belief propagation (BP) algorithm. Furthermore, we extend our basic stereo model to incorporate other visual cues (e.g., image segmentation) that are not modeled in the three MRF’s, and again obtain the MAP solution. Experimental results demonstrate that our method outperforms the state-of-art stereo algorithms for most test cases.

Keywords

Belief Propagation Stereo Vision Stereo Match Line Process Markov Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Jian Sun
    • 1
    • 2
  • Heung-Yeung Shum
    • 2
  • Nan-Ning Zheng
    • 1
  1. 1.Artificial Intelligence and Robotics LabXi’an Jiaotong UniversityChina
  2. 2.Visual Computing GroupMicrosoft Research AsiaBeijing

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