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
A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms
International Journal of Computer Vision
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
What Energy Functions Can Be Minimizedvia Graph Cuts?
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Generalizing Swendsen-Wang to Sampling Arbitrary Posterior Probabilities
IEEE Transactions on Pattern Analysis and Machine Intelligence
Convergent Tree-Reweighted Message Passing for Energy Minimization
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
On population-based simulation for static inference
Statistics and Computing
Stereo matching using population-based MCMC
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part II
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
A comparative study of energy minimization methods for markov random fields
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Bayesian identification of a cracked plate using a population-based Markov Chain Monte Carlo method
Computers and Structures
Computer Vision and Image Understanding
Over-Parameterized optical flow using a stereoscopic constraint
SSVM'11 Proceedings of the Third international conference on Scale Space and Variational Methods in Computer Vision
Abnormal object detection by canonical scene-based contextual model
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part III
Window annealing for pixel-labeling problems
Computer Vision and Image Understanding
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In this paper, we propose a new stereo matching method using the population-based Markov Chain Monte Carlo (Pop-MCMC), which belongs to the sampling-based methods. Since the previous MCMC methods produce only one sample at a time, only local moves are available. In contrast, the proposed Pop-MCMC uses multiple chains in parallel and produces multiple samples at a time. It thereby enables global moves by exchanging information between samples, which in turn, leads to faster mixing rate. In the view of optimization, it means that we can reach a lower energy state rapidly. In order to apply Pop-MCMC to the stereo matching problem, we design two effective 2-D mutation and crossover moves among multiple chains to explore a high dimensional state space efficiently. The experimental results on real stereo images demonstrate that the proposed algorithm gives much faster convergence rate than conventional sampling-based methods including SA (Simulated Annealing) and SWC (Swendsen-Wang Cuts). And it also gives consistently lower energy solutions than BP (Belief Propagation) in our experiments. In addition, we also analyze the effect of each move in Pop-MCMC and examine the effect of parameters such as temperature and the number of the chains.