Non-Rigid Point Set Registration via Adaptive Weighted Objective Function
Changcai Yang,
Yizhang Liu,
Xingyu Jiang,
Zejun Zhang,
Lifang Wei,
Taotao Lai,
Riqing Chen
Affiliations
Changcai Yang
Digital Fujian Research Institute of Big Data for Agriculture and Forestry, College of Computer and Information Science, Fujian Agriculture and Forestry University, Fuzhou, China
Yizhang Liu
Digital Fujian Research Institute of Big Data for Agriculture and Forestry, College of Computer and Information Science, Fujian Agriculture and Forestry University, Fuzhou, China
Xingyu Jiang
Electronic Information School, Wuhan University, Wuhan, China
Zejun Zhang
Digital Fujian Research Institute of Big Data for Agriculture and Forestry, College of Computer and Information Science, Fujian Agriculture and Forestry University, Fuzhou, China
Lifang Wei
Digital Fujian Research Institute of Big Data for Agriculture and Forestry, College of Computer and Information Science, Fujian Agriculture and Forestry University, Fuzhou, China
Taotao Lai
Digital Fujian Research Institute of Big Data for Agriculture and Forestry, College of Computer and Information Science, Fujian Agriculture and Forestry University, Fuzhou, China
Digital Fujian Research Institute of Big Data for Agriculture and Forestry, College of Computer and Information Science, Fujian Agriculture and Forestry University, Fuzhou, China
Non-rigid point set registration is a fundamental problem in many fields related to computer vision, medical image processing, and pattern recognition. In this paper, we develop a new point set registration method by using an adaptive weighted objective function, which formulates the alignment of two point sets as a mixture model estimation problem. The correspondences and the transformation are jointly recovered by using the expectation-maximization algorithm to obtain the promising results. First, the correspondences are established using local feature descriptors, and the adaptation parameters for the mixture model are computed from these correspondences. Then, the underlying transformation is recovered by minimizing the adaptive weighted objective function deduced from the mixture model. We demonstrate the advantages of the proposed method on various types of synthetic and real data and compare the results against those obtained using the state-of-the-art methods. The experimental results show that the proposed method is robust and outperforms the other registration approaches.
