In this paper, we present a new area-based method for visual correspondence search that focuses on the dissimilarity computation. Local and area-based matching methods generally measure the similarity (or dissimilarity) between the image pixels using local support windows. In this approach, an appropriate support window should be selected adaptively for each pixel to make the measure reliable and certain. Finding the optimal support window with an arbitrary shape and size is, however, very difﬁcult and generally known as an NP-hard problem. For this reason,unlike the existing methods that try to ﬁnd an optimal support window, we adjusted the support-weight of each pixel in a given support window. The adaptive support-weight of a pixel is computed based on the photometric and geometric relationship with the pixel under consideration. Dissimilarity is then computed using the raw matching costs and supportweights of both support windows, and the correspondence is ﬁnally selected by the WTA (Winner-Takes-All) method. The experimental results for the rectiﬁed real images show that the proposed method successfully produces piecewise smooth disparity maps while preserving sharp depth discontinuities accurately.