Stereo Matching Using Population-Based MCMC

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Stereo Matching Using Population-Based MCMC

Park, Joonyoung / Kim, Wonsik / Lee, Kyoung Mu

In this paper, we propose a new stereo matching method using the population-based Markov Chain Monte Carlo (Pop-MCMC). Pop-MCMC belongs to the sampling-based methods. Since previous MCMC methods produce only one sample at a time, only local moves are available. However, since Pop-MCMC uses multiple chains and produces multiple samples at a time, it enables global moves by exchanging information between samples, and in turn leads to faster mixing rate. In the view of optimization, it means that we can reach a state with the lower energy. The experimental results on real stereo images demonstrate that the performance of proposed algorithm is superior to those of previous algorithms.

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Document information

Title :

Stereo Matching Using Population-Based MCMC

Contributors:

Yagi, Yasushi (editor) / Kang, Sing Bing (editor) / Kweon, In So (editor) / Zha, Hongbin (editor) / Park, Joonyoung (author) / Kim, Wonsik (author) / Lee, Kyoung Mu (author)

Conference:

Asian Conference on Computer Vision ; 2007 ; Tokyo, Japan November 18, 2007 - November 22, 2007

Published in:

Computer Vision – ACCV 2007 ; Chapter : 55 ; 560-569

Lecture Notes in Computer Science ; 4844

Publication date :

2007-01-01

Size :

10 pages

ISBN :

978-3-540-76390-1 , 978-3-540-76389-5

ISSN :