A Binocular Vision Stereo Matching Algorithm for Depth Map Estimation

Abstract

A new binocular stereo matching algorithm is proposed to solve the problem that the existing stereo matching algorithm is susceptible to noise and pixel mismatch and wrong matching in weak texture areas. First, set the search window and the neighborhood window, the neighborhood window slides inside the search window, compare the similarity between the neighborhood window and the Census transformation window, and determine the degree of influence of the center pixel of the neighborhood window relative to the center pixel of the transformation window according to the similarity, and assign the weight to process the gray value of the pixel. Secondly, the Sobel operator is introduced to calculate the gradient value of the image, and the texture level of the image is divided according to the gradient value. Three thresholds are set according to the texture level, and the parallax is aggregated with the cross-domain algorithm. Finally, the disparity map is optimized by the least squares fitting plane method. Experiments show that the average mismatch rate of the disparity map processed by the algorithm is 5.39% in the test of randomly selected images. Compared with other algorithms, it is more robust to noise, smoother in low-texture areas, has higher matching accuracy of disparity maps, and better optimization of disparity maps.