Stereo Matching Using Population-Based MCMC
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.
IJCV 2008 paper(pdf, 0.96MB)
Joonyoung Park, Wonsik Kim, and Kyoung Mu Lee, "Stereo Matching Using Population-Based MCMC,"
8th Asian Conference on Computer Vision(ACCV),
"Honorable Mention Award", 2007.