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.