A similarity-based adaptive neighborhood method for correlation-based stereo matching

In this paper, we present a new method for dense stereo matching. In area-based methods, the similarity between one pixel of the left image and one pixel of the right image is measured using a correlation index computed on neighborhoods of these pixels. In our method, the neighbor pixels not similar to the center one are excluded when computing the correlation index, which corresponds to adjusting the equivalent size and shape of the correlation neighborhood. Our algorithm yields a precise estimation of the disparity in nontextured areas, while avoiding undesired smoothing at discontinuities. This method is suitable for real-time implantation using specialized hardware. We demonstrate and discuss performances using synthetic stereo pairs.