Accurate and Robust Non-rigid Point Set Registration using Student’s-t Mixture Model with Prior Probability Modeling

Abstract A new accurate and robust non-rigid point set registration method, named DSMM, is proposed for non-rigid point set registration in the presence of significant amounts of missing correspondences and outliers. The key idea of this algorithm is to consider the relationship between the point se...

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Autores principales:	Zhiyong Zhou, Jianfei Tu, Chen Geng, Jisu Hu, Baotong Tong, Jiansong Ji, Yakang Dai
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2018
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/c343247361e44211823689f58ededbaf
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spelling	oai:doaj.org-article:c343247361e44211823689f58ededbaf2021-12-02T15:08:05ZAccurate and Robust Non-rigid Point Set Registration using Student’s-t Mixture Model with Prior Probability Modeling10.1038/s41598-018-26288-62045-2322https://doaj.org/article/c343247361e44211823689f58ededbaf2018-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-018-26288-6https://doaj.org/toc/2045-2322Abstract A new accurate and robust non-rigid point set registration method, named DSMM, is proposed for non-rigid point set registration in the presence of significant amounts of missing correspondences and outliers. The key idea of this algorithm is to consider the relationship between the point sets as random variables and model the prior probabilities via Dirichlet distribution. We assign the various prior probabilities of each point to its correspondences in the Student’s-t mixture model. We later incorporate the local spatial representation of the point sets by representing the posterior probabilities in a linear smoothing filter and get closed-form mixture proportions, leading to a computationally efficient registration algorithm comparing to other Student’s-t mixture model based methods. Finally, by introducing the hidden random variables in the Bayesian framework, we propose a general mixture model family for generalizing the mixture-model-based point set registration, where the existing methods can be considered as members of the proposed family. We evaluate DSMM and other state-of-the-art finite mixture models based point set registration algorithms on both artificial point set and various 2D and 3D point sets, where DSMM demonstrates its statistical accuracy and robustness, outperforming the competing algorithms.Zhiyong ZhouJianfei TuChen GengJisu HuBaotong TongJiansong JiYakang DaiNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 8, Iss 1, Pp 1-17 (2018)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Zhiyong Zhou Jianfei Tu Chen Geng Jisu Hu Baotong Tong Jiansong Ji Yakang Dai Accurate and Robust Non-rigid Point Set Registration using Student’s-t Mixture Model with Prior Probability Modeling
description	Abstract A new accurate and robust non-rigid point set registration method, named DSMM, is proposed for non-rigid point set registration in the presence of significant amounts of missing correspondences and outliers. The key idea of this algorithm is to consider the relationship between the point sets as random variables and model the prior probabilities via Dirichlet distribution. We assign the various prior probabilities of each point to its correspondences in the Student’s-t mixture model. We later incorporate the local spatial representation of the point sets by representing the posterior probabilities in a linear smoothing filter and get closed-form mixture proportions, leading to a computationally efficient registration algorithm comparing to other Student’s-t mixture model based methods. Finally, by introducing the hidden random variables in the Bayesian framework, we propose a general mixture model family for generalizing the mixture-model-based point set registration, where the existing methods can be considered as members of the proposed family. We evaluate DSMM and other state-of-the-art finite mixture models based point set registration algorithms on both artificial point set and various 2D and 3D point sets, where DSMM demonstrates its statistical accuracy and robustness, outperforming the competing algorithms.
format	article
author	Zhiyong Zhou Jianfei Tu Chen Geng Jisu Hu Baotong Tong Jiansong Ji Yakang Dai
author_facet	Zhiyong Zhou Jianfei Tu Chen Geng Jisu Hu Baotong Tong Jiansong Ji Yakang Dai
author_sort	Zhiyong Zhou
title	Accurate and Robust Non-rigid Point Set Registration using Student’s-t Mixture Model with Prior Probability Modeling
title_short	Accurate and Robust Non-rigid Point Set Registration using Student’s-t Mixture Model with Prior Probability Modeling
title_full	Accurate and Robust Non-rigid Point Set Registration using Student’s-t Mixture Model with Prior Probability Modeling
title_fullStr	Accurate and Robust Non-rigid Point Set Registration using Student’s-t Mixture Model with Prior Probability Modeling
title_full_unstemmed	Accurate and Robust Non-rigid Point Set Registration using Student’s-t Mixture Model with Prior Probability Modeling
title_sort	accurate and robust non-rigid point set registration using student’s-t mixture model with prior probability modeling
publisher	Nature Portfolio
publishDate	2018
url	https://doaj.org/article/c343247361e44211823689f58ededbaf
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Accurate and Robust Non-rigid Point Set Registration using Student’s-t Mixture Model with Prior Probability Modeling

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