Region-based local match methods are the simplest and most effective stereo matching algorithms. Considering the problem of the window chosen in the local methods

we propose a cross-based bidirectional adaptive window-matching algorithm. In this algorithm

we construct the support window adaptively by cross-based bidirectional search

which is based on the correlation of intensity and disparity in the image patches

and obtain a mask window. The integral images are adopted to calculate the matching costs in the mask window. Thus

disparity map is obtained. Two steps are implemented: Union Jack-shaped voting and bilateral filtering algorithm as a post-processing step. The proposal is adopted on different stereo images

and adaptive match windows are obtained for the image structures. The matching accuracy is increased by 30% for Teddy compared with the original cross-based method. The two-step disparity post-processing keeps the edges of the images well. Experimental results show that the proposed algorithm alleviates the depth edge expansion problem introduced by regular window as well as improves the robustness and depth accuracy of the algorithm.