Genetic-Based Stereo Algorithm and Disparity Map Evaluation

In this paper, a new genetic-based stereo algorithm is presented. Our motivation is to improve the accuracy of the disparity map 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, and therefore, removes mismatches caused by visibility problems. By optimizing the compatibility between corresponding points and the continuity of the disparity map using a genetic algorithm, mismatches caused by false targets are removed. The quadtree structure is used to implement the multi-resolution framework. Since nodes at different level of the quadtree cover different number of pixels, selecting nodes at different levels gives a 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. In addition, we introduce a new disparity map evaluation technique, which is developed based on a similar technique employed in the image segmentation area. Comparing with two existing evaluation approaches, the new technique can evaluate the disparity maps generated without additional knowledge of the scene, such as the correct depth information or novel views.