MCGE: multi-candidate based group evolution in stereo matching

This paper addresses the subject of stereo matching between the corners of two perspectives. Similarity-based matching is prone to errors, and the existing algorithms reject outliers but never correct them. Frequently, in fact, the correct correspondence is not found at the single point with the largest similarity; but it lies among a few points with large value. So we propose a new algorithm, which first selects several candidates for each corner and then optimizes the whole match with global constraints. The algorithm increases remarkably not only the percentage of correctness, but also the number of correct matches. To expedite the optimization, we apply group evolution. A simple directional constraint is used as criteria for evaluation, which avoids the estimation of epipolar lines. The principles and applicable cases are presented. Results are provided for corners, retrieved both manually and automatically, in real images.