Stereo Matching with Segmentation-Based Cooperation

Abstract

In this paper we present a new stereo matching algorithm that produces accurate dense disparity maps and explicitly detects occluded areas. This algorithm extends the original cooperative algorithms in two ways. First, we design a method of adjusting the initial matching score volume to guarantee that correct matches have high matching scores. This method propagates “good” disparity information within or among image segments based on certain disparity confidence measurement criterion, thus improving the robustness of the algorithm. Second, we develop a scheme of choosing local support areas by enforcing the image segmentation information. This scheme sees that the depth discontinuities coincide with the color or intensity boundaries. As a result, the foreground fattening errors are drastically reduced. Extensive experimental results demonstrate the effectiveness of our algorithm, both quantitatively and qualitatively. Comparison between our algorithm and some other representative algorithms is also reported.