Abstract: Aiming at the problem that the traditional binocular stereo matching algorithm uses fixed window,which leads to low matching accuracy in weak texture regions,an adaptive window stereo matching algorithm based on image segmentation is proposed.Firstly,the mean shift algorithm is used to segment the image,and then the gray standard deviation of local sub regions is calculated.Based on this,an adaptive window size setting operator is proposed according to the texture richness.Based on the adaptive window size setting,the matching cost is calculated by combining census transform and gradient value,and the initial disparity is calculated by adaptive weight cost aggregation and “winner takeall” strategy respectively.Finally,the dense disparity map is obtained by using the principle of left and right disparity consistency and weighted median filtering.The adaptive window matching algorithm and fixed window matching algorithm proposed in this paper are used to match standard images on Middlebury dataset.The experimental results show that the average matching error rate of the proposed algorithm is 2.04%,which is 4.5% and 7.9% lower than that of the contrast algorithm.

Key words: Adaptive weight, Adaptive window, Image segmentation, Stereo matching, Weak texture