PROGRESSIVE FILTERING FOR FEATURE MATCHING

In this paper, we propose a simple yet efficient method termed as Progressive Filtering for Feature Matching, which is able to establish accurate correspondences between two images of common or similar scenes. Our algorithm first grids the correspondence space and calculates a typical motion vector for each cell, and then removes false matches by checking the consistency between each putative match and the typical motion vector in the corresponding cell, which is achieved by a convolution operation. By refining the typical motion vector in an iterative manner, we further introduce a progressive matching strategy based on the coarse-to-fine theory to promote the matching accuracy gradually. The density estimation is utilized to address the island samples and accelerate the convergency of the mismatch removal procedure. In addition, our method is quite efficient where the gridding strategy enables it to achieve linear time complexity. Extensive experiments on several representative real images involving different types of geometric transformations demonstrate the superiority of our approach over the state-of-the-art.