A cost construction via MSW and linear regression for stereo matching

This paper proposes a new cost construction method with multiscale Weber (MSW) descriptor and weighted linear regression for robust stereo matching in a two-layer hierarchical structure. Firstly, the MSW descriptors extracted from stereo pairs are utilized to combined raw matching costs to reduce the disparity search range. Secondly, the indispensable matching costs on the subsets of disparity candidates are aggregated by spatial moving discrete sampling and weighted least square. The proposed cost technique can provide an effective and efficient way to reflect robust visual dissimilarity measure for local stereo approach. Experimental results on the Middlebury benchmark datasets demonstrate the validity of this cost construction which can eliminate effectively the ambiguity of stereo matching.