Point matching algorithm based on machine learning method

Abstract

Point matching is an important issue of computer vision and pattern recognition, and it is widely used in target recognition, medical image, pose estimation, etc. In this study, we propose a novel end-to-end model (multi-pointer network) based on machine learning method to solve this problem. We capitalize on the idea of multi-label classification to ameliorate the pointer network. Instead of outputting a member of input sequence, our model selects a set of input elements as output. Considering matching problem as a sequential manner, our model takes the coordinates of points as input and outputs correspondences directly. Using this new method, we can effectively solve the translation of the whole space and other large-scale rigid transformations. Furthermore, experiment results show that our model can be generalized to other combinatorial optimization problems in which the output is a subset of input, like Delaunay triangulation.