ing at the problem that the stereo matching algorithms have high mismatching rates in non-occluded regions of the images, especially the weak texture regions, a stereo matching algorithm based on adaptive color weights over cross window and tree dynamic programming is proposed. Firstly, we combine color, gradient information and census transform as similarity measure function to propose the cost calculation function. Then the adaptive cross window is constructed with distance and color information of the image, and the cost aggregation based on color weights is proposed. Instead of using winner-take-all strategy solely for global optimization of disparity, the dynamic programming algorithm based on tree structure is introduced to calculate disparity. Finally, the dense disparity maps are obtained by the process of disparity refinement. The experimental results demonstrate that on the Middlebury test platform, the average mismatching rate evaluated with proposed algorithm in non-occluded regions of four standard images is 2.45%. Meanwhile, the other ten images are compared and evaluated. The proposed algorithm effectively improves the accuracy of stereo matching in non-occluded regions.