A Fast Rich Information-Based Stereo Matching Framework

With the recent development on image affine region descriptors, we can extract more salient and useful local information from images. That information can be used to help us to better solve a fundamental problem in computer vision stereo vision. In this paper we propose a framework for stereo matching problems in order to give a rich-information based, high-precision and fast solution. Affine regions based SIFT are chosen as features rather than point features to extract more information. In the matching period, a search algorithm with incremental dissimilarity approximations is used for efficient computing. For correctness, MLESAC (maximum likelihood estimation sample consensus) method is used to eliminate outliers. In the experiment part, we evaluate different combinations on the performance of speed, correctness and transformations.