Enhancement of local methods by using spatial support weight

In window-based stereo matching algorithms, a local window is used to measure the similarity (or dissimilarity) between pixels of a stereo pair. An implicit assumption in local stereo matching is that all pixel within a support window have the same (or similar) disparity, and many local methods have been proposed to satisfy this assumption. However, objects in real-word images have arbitrary sizes and shapes, and hence this assumption can be violated frequently. In this paper, we propose spatial fixed window, spatial shiftable window, spatial multiple window, and spatial variable window methods which are improved methods of fixed window, shiftable window, multiple window, and variable window, respectively. We also experiment these improved algorithms in gray and color images, and the experimental results using the Middleburry images show that the proposed methods outperform their corresponding original methods.