Non-Local Stereo Matching Algorithm Based on Color and Edge Information

This study proposes an alternative non-local stereo matching algorithm based on color and edge information to reduce the large matching error in the texture-rich regions of the traditional non-local stereo matching algorithm. In the cost computation stage, the gray and gradient information arc combined to obtain the matching cost of pixels. In the cost aggregation stage, to reduce the mismatch rate for regions with a similar background, the minimum spanning tree is used for cost aggregation, and the weight function is redefined through combination of color and edge information. Then, the winner-take-all (WTA) strategy is implemented to obtain optimal disparity, and the disparity map is refined through post-processing operations such as left-right consistency checking and median filtering. Finally, the feasibility of the proposed algorithm is verified using the Middlebury data platform. Experimental results show that compared with the traditional algorithm, the proposed algorithm reduces the average mismatch rate of the image from 6.02% to 5.10%.