A Robust Point Sets Matching Method
This work addresses point sets matching for applications like feature extraction and motion estimation, but it appears incremental as it builds on existing graph-based matching methods.
The paper tackles the problem of robust point sets matching in computer vision by proposing an iterative algorithm that calculates transformations and similarity matrices to update matching scores, achieving robustness to noise, outliers, and jitter in experiments.
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.