Minimum Generation Cube Matching Algorithm Based on Improved Matching Cost and Mean Segmentation

The disparity accuracy issue still exists in weak texture and depth discontinuity areas, although the conventional stereo matching model has demonstrated good performance in accuracy and robustness. To address the above issues, a minimum generating cube matching algorithm based on enhanced matching cost and mean segmentation is proposed. First, in the matching cost computation stage, the initial matching cost is computed by the Census to transform, and the input image's edge information is extracted by the Sobel operator. The extracted image edge information is merged with the matching cost value after Census transform, and the nonlinear fusion is conducted with the cost value based on image brightness information to enhance the matching cost's accuracy. Then, the minimum spanning tree cost aggregation model is employed for aggregation operation and the winner-take-all technique is employed to estimate the image's initial parallax. Finally, in the disparity optimization stage, the MeanShift algorithm is employed to segment the image, and the mismatching points are corrected along with the image's contour information to further enhance the disparity accuracy in weak texture and edge areas. Experimental findings demonstrate that compared with some conventional algorithms, the proposed approach has higher disparity accuracy, and the disparity map's edges and textures are smoother and more robust than other algorithms.