关键词: 置信传递立体匹配, 动态Bayes网, EM算法, Bayes学习, Schwarz信息准则

Abstract: According to image feature space of time-varying parameters，the stereo matching problem is introduced.Stereo vision matching algorithm is usually made for time-invariant.Therefore，for the time-varying probability distribution of the image feature space，these methods can not effectively realize the matching of dynamic three-dimensional vision.To solve this problem，this paper builds a Dynamic Bayesian Network（DBN） for the edge of Graph Cuts（GC）.The unsupervised Bayesian learner，classifier and DBN are syncretized.It will acquire the rules that the probability or stochastic process model evolves with time so as to the image feature space of time-varying parameters can be estimated dynamically and accurately.Model results are used as the corresponding inter-element.In the edge region，for Belief Propagation（BP），it realizes the matching of dynamic three-dimensional visual images.

Key words: belief propagation, dynamic Bayesian network, EM algorithm, Bayesian learning, Schwarz information criterion