Point Sets Matching by Feature-Aware Mixture Point Matching Algorithm

In this article we propose a new method to find matches between two images, which is based on a framework similar to the Mixture Point Matching (MPM) algorithm. The main contribution is that both feature and spatial information are considered. We treat one point set as the centroid of the Gaussian Mixture Model (GMM) and the other point set as the data. Different from traditional methods, we propose to assign each GMM component a different weight according to the feature matching score. In this way the feature information is introduced as a reasonable prior to guide the matching, and the spatial transformation offers a global constraint so that local ambiguity can be alleviated. Experiments on real data show that the proposed method is not only robust to outliers, deformation and rotation, but also can acquire the most matches while preserving high precision.