Abstract: This paper presents a stereo matching algorithm based on inter-regional cooperative optimization. This algorithm uses regions as matching primitives and defines the corresponding region cost functions for matching by utilizing the color statistics of regions and the constraints on smoothness and occlusion between adjacent regions. In order to obtain a more reasonable disparity map, a cooperative optimization procedure is employed to minimize the matching costs of all regions by introducing the cooperative and competitive mechanism between regions. Firstly, a color based segmentation method is used to segment the reference image into regions with homogeneous color. Secondly, a local window-based matching method is used to determine the initial disparity estimates of each image point. And then, a plane fitting technique is applied to obtain the parameters of disparity plane corresponding to each image region. Finally, under a framework of inter-regional cooperative optimization, the disparity plane parameters of all regions are iteratively optimized by a local optimization method until a reasonable disparity map is obtained. The experimental results based on Middlebury test set indicate that the performance of our method is competitive with the best stereo matching algorithms and the disparity maps recovered are close to the ground truth data.