Particle swarm based stereo algorithm and disparity map evaluation

In this paper, a new particle swarm 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 divided into two steps, including partial matching of segmented image and particle swarm optimization of the rest. The algorithm first takes advantage of SAD and Dynamic Programming to remove the mismatches mainly caused by visibility problems; after the first step, the algorithm selects all the rest image segmented regions, takes them as a particle and uses particle swarm to optimization it. In the second step, the cost function is defined on the pixel level, as well as on the segmented level, while the pixel level measures the data similarity based the current disparity map, the segmented level incorporates a smooth term. Results obtained for benchmark indicate that the proposed method is able to get rather accurate disparity maps.