Local Binary Description Combined with Superpixel Segmentation Refinement for Stereo Matching

In order to polish up the target edge burring and staircase effect in low-textured regions or discontinuous regions, a stereo matching method based on the local binary description and superpixel segmentation is proposed. Firstly, the initial disparity is obtained by space and color features binary cost computation and winner-takes-all method. Then the segmentation results by simple linear iterative clustering method arc labeled for each pixel's space and color features. In disparity refinement procedure, the appropriate fixed points are chosen to propagate disparity for both edge and inner pixels of each superpixel. Experiments with Middlebury datasets arc mainly carried out in the initial disparity considerations and disparity refinement. The result shows that the disparity maps arc much smoother especially in target boundary. The proposed method can achieve more accurate disparity value in non-overlapping and occluded regions between reference image and matching image, which effectively reduces the mismatching rate in non-occluded, all, and discontinuity regions.