Stereo matching with adaptive support-weight correlation and Graph Cuts

Constructing a reliable data term and occlusion handling are two important issues for energy model based stereo method. In this paper, we at first use a 2-step adaptive support-weight correlation approach to get a reliable correlation volume. Then a pixel classification is proposed which classifies pixels into three classes: occluded, unstable and stable. For each pixel, according its class, a confidence weight is assigned. After that a new energy model is then constructed by integrating the correlation volume and the confidence weight. Finally, through minimizing this energy using Graph cuts, a better disparity map is obtained. Experimental results on the Middlebury data set show that our proposed method has the similar good performance with the top rank Graph Cuts based algorithms listed on the Middlebury homepage.