In this paper, we present a novel method for improving the speed and accuracy of the initial disparity estimation of the stereo matching algorithms. These algorithms are widely investigated, but fast and precise estimation of a disparity map still remains a challenging problem. Recent top ranking stereo matching algorithms usually utilize a window-based approach and mean shift based clustering. We propose an algorithm inspired by a top-down approach exploiting these two steps.
By using the mean shift algorithm, we transform the input images into the attractor space and then perform the matching on the attractor sets. In contrast to the state-of-the-art algorithms, where matching is done on the basis of pixel intensities, grouped according to the results of mean shift algorithm, we perform the matching between the attractor sets of both input images. In this way we are able to acquire fast disparity estimates for whole segments.