期刊
IEEE TRANSACTIONS ON FUZZY SYSTEMS
卷 28, 期 11, 页码 2784-2798出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TFUZZ.2020.2974433
关键词
Hierarchical probabilisticmodel (HPM); hesitant fuzzy Einstein weighted averaging (HFEWA); nonrigid point set registration; symmetric cross entropy; variational Bayesian (VB)
资金
- National Natural Science Foundation of China [41971392]
- Yunnan Ten-Thousand Talents Program
Point set registration plays an important role in computer vision and pattern recognition. In this article, we propose an adaptive hierarchical probabilisticmodel (HPM) under a variational Bayesian (VB) framework for point set registration problem. The main contributions of this article are given as follows. First, a dynamic putative inlier estimation strategy is proposed through the hesitant fuzzy Einstein weighted averaging based membership calculation and component estimation using symmetric cross entropy. Second, a student-t mixture model based HPM is designed to solve outlier and occlusion problems during registration. Third, a VB-based transformation updating is proposed to construct a robust and adjustable transformation for effectively fitting target point set while further resisting outliers. The performances of the proposed method in point set and image registrations against 11 state-of-the-art methods are evaluated, in which our method gives the best performance in most scenarios.
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