Adaptive Hierarchical Probabilistic Model Using Structured Variational Inference for Point Set Registration
Adaptive Hierarchical Probabilistic Model Using Structured Variational Inference for Point Set Registration
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DOI：
10.1109/tfuzz.2020.2974433
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发表时间：
2020-11
影响因子：
11.9
通讯作者：
Qiqi He;Jie Zhou;Shijin Xu;Yang Yang-Yang;Rui Yu;Yuhe Liu
中科院分区：
文献类型：
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作者：
Qiqi He;Jie Zhou;Shijin Xu;Yang Yang-Yang;Rui Yu;Yuhe Liu
点集配准在计算机视觉和模式识别中起着重要的作用。针对点集配准问题，提出了一种变分贝叶斯框架下的自适应层次概率模型（HPM）。本文的主要贡献如下。首先，通过基于犹豫模糊爱因斯坦加权平均的隶属度计算和基于对称交叉熵的分量估计，提出了一种动态假定先验估计策略。其次，设计了一种基于学生-t混合模型的HPM，解决配准过程中的离群点和遮挡问题；第三，提出一种基于vb的变换更新方法，构建鲁棒可调变换，在有效拟合目标点集的同时进一步抵抗离群值。对比11种最先进的配准方法，本文方法在点集配准和图像配准方面的性能进行了评估，其中我们的方法在大多数情况下都具有最佳性能。
Point set registration plays an important role in computer vision and pattern recognition. In this article, we propose an adaptive hierarchical probabilistic model (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.