Nonrigid point set registration based on Laplace mixture model with local constraints
ISSN: 0144-5154
Article publication date: 7 November 2019
Issue publication date: 30 March 2020
Abstract
Purpose
This paper aims to investigate a probabilistic mixture model for the nonrigid point set registration problem in the computer vision tasks. The equations to estimate the mixture model parameters and the constraint items are derived simultaneously in the proposed strategy.
Design/methodology/approach
The problem of point set registration is expressed as Laplace mixture model (LMM) instead of Gaussian mixture model. Three constraint items, namely, distance, the transformation and the correspondence, are introduced to improve the accuracy. The expectation-maximization (EM) algorithm is used to optimize the objection function and the transformation matrix and correspondence matrix are given concurrently.
Findings
Although amounts of the researchers study the nonrigid registration problem, the LMM is not considered for most of them. The nonrigid registration problem is considered in the LMM with the constraint items in this paper. Three experiments are performed to verify the effectiveness and robustness and demonstrate the validity.
Originality/value
The novel method to solve the nonrigid point set registration problem in the presence of the constraint items with EM algorithm is put forward in this work.
Keywords
Acknowledgements
This work was supported in part by the Key Research and Development Program of Jiangsu under grants BE2017071, BE2017647 and BE2018004-04, the Projects of International Cooperation and Exchanges of Changzhou under grant CZ20170018, the Fundamental Research Funds for the Central Universities under grant 2018B47114, the Open Research Fund of State Key Laboratory of Bioelectronics, Southeast University under grant 2019005 and the State Key Laboratory of Integrated Management of Pest Insects and Rodents under grant IPM1914.
Citation
Xu, C., Yang, X. and Liu, X. (2020), "Nonrigid point set registration based on Laplace mixture model with local constraints", Assembly Automation, Vol. 40 No. 2, pp. 335-343. https://doi.org/10.1108/AA-06-2019-0108
Publisher
:Emerald Publishing Limited
Copyright © 2019, Emerald Publishing Limited