A new stereo matching algorithm based on probabilistic diffusion

In this paper, the general formula of disparity estimation based on Bayesian Maximum A Posteriori (MAP) algorithm is derived and implemented with simplified probabilistic models. The probabilistic models are independence and similarity among the neighboring disparities in the configuration. The formula can be implemented into the some different forms corresponding to the probabilistic models in the disparity neighborhood or configuration. And, we propose new probabilistic models in order to simplify the joint probability distribution of disparities in the configuration. According to the experimental results, the proposed algorithm outperformed the other ones, such as sum of squared difference(SSD) based algorithm and Scharstein's method, and the proposed probabilistic models are reasonable and approximate the pure joint probability distribution very well with decreasing the computations to 0.01% of the generalized formula.