Improved SVD matching based on Delaunay triangulation and belief propagation

Finding correspondences between pair of images of the same scene is a key problem in computer vision. When matching images undergo viewpoint change, partial occlusion, clutters and illumination change, there will be a lot of mismatches due to the limited repeatability and discriminative power of features. In this paper, we propose a robust matching algorithm that can remove false matches and propagate the correct ones to obtain more matches, thus improve the matching accuracy. Firstly, extract SURF (Speeded Up Robust Features) descriptors from the input two images, which can be used to build the proximity matrix. Then perform SVD(Singular Value Decomposition) on the proximity matrix to obtain the initial matches. Thirdly, refine the initial matches by the unique property of Delaunay triangulation that can produce the maximum clique of the two Delaunay graph. Finally, recover the lost matches with the constraint of dual graph of Voronoi. Experimental results on Oxford datasets indicate that our algorithm can improve the match performance compared to the RANSAC-based method.