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Ensuring both of the algorithm accuracy and speed is a key technical problem of stereo matching algorithms. A fast dense stereo matching algorithm based on Bayesian is presented. Bayesian probability theory is applied to the stereo matching problem, the support points are extracted with the MSERDoG operator with the pixel gray value as the matching cost and the fixed window as the cost aggregation matched, the matched support points are triangulated, the disparity and gradient of the support points, the formation of the linear coefficient triangulation and segmentation are selected as priori probability conditions, thus ensuring efficient disparity search space and improving the matching efficiency. The dense disparity map is obtained by minimizing the energy function. In experiments with the international standard Middlebury platform, the results show that the proposed algorithm gets matching with high precision, high speed, low mismatch, and high matching efficiency.

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