Segmentation and stereo matching are difficult problems in computer vision. One of the possible solutions is to solve these problems in an integrated manner as described in this paper. After region-based segmentation, a candidate stereo matching is carried out, which assigns the corresponding regions from one image to another image by shape-based matching. During the next segmentation, stereo information is included, that is, in considering the merging of one region with its neighboring regions, the corresponding regions in the candidate matching pools are extracted and a new measurement is calculated, based on intensity and shape information from both images. The global matching finally combines other constraints like uniqueness, ordering, and topological relations to get unique matching. The developed algorithm has successfully reconstructed disparity maps on test images. It is concluded that our method is a good one to solve segmentation and stereo matching together.