Robust Point Sets Matching by Fusing Feature and Spatial Information Using Nonuniform Gaussian Mixture Models

Most of the traditional methods that handle the point sets matching between two images are based on local feature descriptors and the succedent mismatch eliminating strategies, which usually suffers from the sparsity of the initial match set because some correct ambiguous associations are easily filtered out by the ratio test of SIFT matching due to their second ranking in feature similarity. In this paper, we propose a nonuniform Gaussian mixture model (NGMM) for point sets matching between a pair of images which combines feature with position information of the local feature points extracted from the image pair to achieve point sets matching in a GMM framework. The proposed point set matching using an NGMM is able to change the correspondence assignments throughout the matching process and has the potential to match up even ambiguous matches correctly. The proposed NGMM framework can be either used to directly find matches between two point sets obtained from two images or applied to remove outliers in a match set. When finding matches, NGMM tries to learn a nonrigid transformation between the two point sets and provide a probability for every found match to measure the reliability of the match. Then, a probability threshold can be used to get the final robust match set. When removing outliers, NGMM requires that the vector field formed by the correct matches to be coherent and the matches contradicting the coherent vector field will be regarded as mismatches to be removed. A number of comparison and evaluation experiments reveal the good performance of the proposed NGMM framework in both finding matches and discarding mismatches.