A Robust Point Sets Matching Method

TL;DR

This paper introduces a robust iterative point sets matching method that effectively handles noise, outliers, and jitter, improving accuracy in applications like computer vision and fingerprint matching.

Contribution

It presents a novel iterative algorithm that enhances robustness to noise and outliers in point set matching by using similarity matrices and graph-based matching.

Findings

Demonstrates robustness to outliers and jitter in experiments

Effective in noisy and real-world scenarios

Outperforms some existing matching methods

Abstract

Point sets matching method is very important in computer vision, feature extraction, fingerprint matching, motion estimation and so on. This paper proposes a robust point sets matching method. We present an iterative algorithm that is robust to noise case. Firstly, we calculate all transformations between two points. Then similarity matrix are computed to measure the possibility that two transformation are both true. We iteratively update the matching score matrix by using the similarity matrix. By using matching algorithm on graph, we obtain the matching result. Experimental results obtained by our approach show robustness to outlier and jitter.