Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/81765
Title: Point set registration with a hybrid structure constraint
Authors: Sun, J
Chen, X
Sun, ZL
Lam, KM 
Zeng, ZG
Keywords: Point set registration
Gaussian mixture model
Structure constraint
Expectation-maximization algorithm
Issue Date: 2019
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE access, 24 Sept. 2019, v. 7, p. 164246-164255 How to cite?
Journal: IEEE access 
Abstract: Due to some unfavorable factors, how to accurately register point sets is still a challenging task. In this paper, an effective point set registration approach is proposed based on a hybrid structure constrain. In the proposed method, a composite weight coefficient is determined based on the amplitudes of the vector and the corresponding projection of neighbor points. Given the composite weight coefficient, a local structure constraint is constructed as a linear combination of the vectors of neighbor points. A Gaussian mixture model is established by utilizing the local structure constraint and a global structure constraint based on the motion coherence theory. In addition, an expectation-maximization algorithm is derived to solve the unknown variables in the proposed model. For the constraint terms, an update strategy is utilized to obtain the approximately optimal weight coefficients. Compared to the state-of-the-art approaches, the proposed model is more robust due to the use of multiple effective constraints. Experimental results on some widely used data sets demonstrate the effectiveness of the proposed model.
URI: http://hdl.handle.net/10397/81765
ISSN: 2169-3536
DOI: 10.1109/ACCESS.2019.2943475
Rights: This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see http://creativecommons.org/licenses/by/4.0/
The following publication J. Sun, X. Chen, Z. Sun, K. Lam and Z. Zeng, "Point Set Registration With a Hybrid Structure Constraint," in IEEE Access, vol. 7, pp. 164246-164255, 2019 is available at https://dx.doi.org/10.1109/ACCESS.2019.2943475
Appears in Collections:Journal/Magazine Article

Files in This Item:
File Description SizeFormat 
Sun_Point_Set_Registration.pdf1.75 MBAdobe PDFView/Open
Access
View full-text via PolyU eLinks SFX Query
Show full item record
PIRA download icon_1.1View/Download Contents

Page view(s)

26
Citations as of May 6, 2020

Download(s)

11
Citations as of May 6, 2020

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.