Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/81765
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dc.contributorDepartment of Electronic and Information Engineering-
dc.creatorSun, J-
dc.creatorChen, X-
dc.creatorSun, ZL-
dc.creatorLam, KM-
dc.creatorZeng, ZG-
dc.date.accessioned2020-02-10T12:29:03Z-
dc.date.available2020-02-10T12:29:03Z-
dc.identifier.issn2169-3536-
dc.identifier.urihttp://hdl.handle.net/10397/81765-
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineersen_US
dc.rightsThis work is licensed under a Creative Commons Attribution 4.0 License. For more information, see http://creativecommons.org/licenses/by/4.0/en_US
dc.rightsThe 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.2019.2943475en_US
dc.subjectPoint set registrationen_US
dc.subjectGaussian mixture modelen_US
dc.subjectStructure constrainten_US
dc.subjectExpectation-maximization algorithmen_US
dc.titlePoint set registration with a hybrid structure constrainten_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage164246-
dc.identifier.epage164255-
dc.identifier.volume7-
dc.identifier.doi10.1109/ACCESS.2019.2943475-
dcterms.abstractDue 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.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationIEEE access, 24 Sept. 2019, v. 7, p. 164246-164255-
dcterms.isPartOfIEEE access-
dcterms.issued2019-
dc.identifier.isiWOS:000498707700002-
dc.identifier.scopus2-s2.0-85075621943-
dc.description.validate202002 bcrc-
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_Scopus/WOSen_US
dc.description.pubStatusPublisheden_US
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