A new stereo matching algorithm based on Bayesian model

We derive the general formula of Bayesian model for stereo matching algorithm and implement it with simplified probabilistic models. The probabilistic models use the independence property and similarity between the neighborhood disparities in the configuration. The formula is the generalization of the Bayesian model of stereo matching, and can be implemented in the different forms corresponding to the probabilistic models in the disparity neighborhood system or configuration. We propose a new probabilistic model in order to simplify the joint probability distribution of the disparities in the configuration. According to the experimental results, we can conclude that the derived formula generalizes the Bayesian model for the stereo matching algorithm, and the simplified probabilistic models are reasonable and approximate the pure joint probability distribution very well. Compared with the conventional method of the Bayesian model and the sum of squared difference (SSD) algorithm, furthermore, our proposed algorithm outperforms the others