Robust line segment mismatch removal using point-pair representation and Gaussian-uniform mixture formulation

Abstract

Line segment matching (LSM) plays an important role in image matching, while it is always a challenging problem due to line fracture and high geometric complexity. In this paper, we propose a line segment mismatch removal to address the sensitivity to segment length and the fracture problem. The mismatch removal removes the massive false matches obtained by the descriptors like LBD. Specifically, we suggest calculating the endpoint error in LSM rather than the direction error in considering the error band and segment length. We propose a point-pair representation, and transform the LSM problem to be an intuitive point-pair alignment problem with a mixture model. The point-pair representation is more robust on the segment length than the linear equation representation used in RANSAC, which significantly preserves short line segments. Then, based on the point-pair representation, we propose a novel Directed Endpoint Drift method to solve the inherent fracture problem of line segments by allowing the endpoints to move along the line to complement the fracture. Compared with the recent learning-based method, the proposed method nearly doubles the number of correct matches on the remote sensing dataset. Especially, the recall is improved by 10% to 30% for both the short segments and the slightly fractured segments, compared with the popular RANSAC and MAGSAC++ methods. The code is available at https://github.com/shenliang16/DEpD.