在立体匹配中,同时保证算法的精度和速度是一个技术难题。提出了一种基于贝叶斯理论的快速稠密立体匹配算法。将贝叶斯概率分布理论应用到立体匹配问题上，利用MSERDoG 算子提取支撑点,像素灰度值作为匹配代价、固定窗口作为代价聚合对其进行匹配，对匹配好的支撑点进行三角剖分，将支撑点的视差、梯度、三角剖分的线性系数及分割作为计算视差的先验概率条件，从而保证了有效的视差搜索空间，提高了匹配效率。最终通过最小化能量函数获得稠密的视差图。在国际标准Middlebury 平台进行实验，结果表明提出的算法匹配精度高，速度快，误匹配率低，匹配效率高。

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.