A Pixel Dissimilarity Measure That Is Insensitive to Image Sampling
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multi-camera Scene Reconstruction via Graph Cuts
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
Stereo Matching Using Belief Propagation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Graph Partition by Swendsen-Wang Cuts
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Stereo Correspondence by Dynamic Programming on a Tree
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Segment-Based Stereo Matching Using Belief Propagation and a Self-Adapting Dissimilarity Measure
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
Multigrid and multi-level Swendsen-Wang cuts for hierarchic graph partition
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Stereo Matching Using Population-Based MCMC
International Journal of Computer Vision
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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.