FAPP: Extremely Fast Approach to Boosting Image Matching Precision

Feature matching is a critical problem in the field of computer vision, which serves as the foundation for many high-level computer vision applications. This article aims to improve the accuracy of feature matching by eliminating mismatches in putative matches. To achieve this goal, we propose an extremely fast approach to boosting image matching precision (FAPP). The key idea behind FAPP is that correct feature matches have similar Euclidean distances, and the sine values of the angles between correct feature matches and the horizontal axis are also similar. Consequently, putative matches can be represented as 2-D coordinate points (sine value, Euclidean distance), which makes correct and incorrect feature matches have different degrees of clustering. The coordinate points are, furthermore, divided into grid spaces so that the coordinate points are distributed in different grid areas. Through adaptive parameter estimation, we determine a threshold for the number of correct feature matches within each grid, thereby eliminating false feature matches. In addition, to validate the effectiveness of FAPP, we conducted experiments on two public datasets and compared the results with several existing classical methods. The experimental outcomes demonstrate the superior performance of FAPP over the existing classical methods. Furthermore, the method has been applied to 3-D reconstruction with good results. Source code: https://github.com/caomw/fapp.