Binocular Stereo Matching with Multi-feature Representation and Super-pixel Optimization
Aiming at the accuracy problems in texture lacking region, occlusion region and depth discontinuous in binocular stereo matching, an algorithm based on multi-feature representation and super-pixel optimization is proposed. By adding edge information into initial cost calculating, and combining with image local information, it can improve the edge region recognition in disparity calculation. In cost aggregation step, the initial aggregation region is computed by simple linear iterative clustering method. In order to aggregate much more information in texture lacking region, an algorithm of adaptive searching based on the rice skeleton is proposed. In disparity optimization step, using the initial super-pixel region, to correct disparities which are mismatched. Experiments on the Middlebury stereoscopic dataset test platform prove that the proposed algorithm has higher accuracy.
国家科技期刊平台
NSTL
万方数据
维普
