Multi-resolution stereo matching using genetic algorithm

In this paper, a new genetic-based stereo matching algorithm is presented. Our motivation is to improve the accuracy of the disparity map generated by removing the mismatches caused by both occlusions and false targets. In our approach, the stereo matching problem is considered as an optimization problem. The algorithm first takes advantage of multi-view stereo images to detect occlusions, therefore, removes mismatches caused by visibility problems. A genetic algorithm is then used to optimize both the compatibility between corresponding points and the continuity of the disparity map, which removes mismatches caused false targets. In addition, the quadtree structure is used to implement a multiresolution framework. Since nodes at different level of the quadtree cover different number of pixels, selecting nodes at different levels gives similar effect as adjusting the window size at different locations of the image. The experimental results show that our approach can generate more accurate disparity maps than two existing approaches.