Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/107116
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Electrical and Electronic Engineering | - |
dc.creator | Sun, J | en_US |
dc.creator | Sun, ZL | en_US |
dc.creator | Lam, KM | en_US |
dc.creator | Zeng, Z | en_US |
dc.date.accessioned | 2024-06-13T01:04:00Z | - |
dc.date.available | 2024-06-13T01:04:00Z | - |
dc.identifier.issn | 0278-0046 | en_US |
dc.identifier.uri | http://hdl.handle.net/10397/107116 | - |
dc.language.iso | en | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers | en_US |
dc.rights | © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | en_US |
dc.rights | The following publication J. Sun, Z. -L. Sun, K. -M. Lam and Z. Zeng, "A Robust Point Set Registration Approach With Multiple Effective Constraints," in IEEE Transactions on Industrial Electronics, vol. 67, no. 12, pp. 10931-10941, Dec. 2020 is available at https://doi.org/10.1109/TIE.2019.2962433. | en_US |
dc.subject | Expectation-maximization algorithm | en_US |
dc.subject | Gaussian mixture model | en_US |
dc.subject | Point set registration | en_US |
dc.title | A robust point set registration approach with multiple effective constraints | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.spage | 10931 | en_US |
dc.identifier.epage | 10941 | en_US |
dc.identifier.volume | 67 | en_US |
dc.identifier.issue | 12 | en_US |
dc.identifier.doi | 10.1109/TIE.2019.2962433 | en_US |
dcterms.abstract | How to accurately register point sets still remains a challenging task, due to some unfavorable factors. In this article, a robust point set registration approach is proposed based on the Gaussian mixture model (GMM) with multiple effective constraints. The GMM is established by wrapping a model point set to a target point set, via a spatial transformation. Instead of a displacement model, the spatial transformation is decomposed as two types of transformations, an affine transformation and a nonaffine deformation. For the affine transformation, a constraint term of the parameter vector is applied to improve the robustness and efficiency. In order to enforce the smoothness, the square norm of the kernel Hilbert space is adopted as a coherent constraint for the nonaffine deformation. Moreover, the manifold regularization is utilized as a constraint in the proposed model, to capture the spatial geometry of point sets. In addition, the expectation-maximization algorithm is developed to solve the unknown variables of the proposed model. Compared to the state-of-The-Art approaches, the proposed model is more robust to deformation and rotation, due to the use of multiple effective constraints. Experimental results on several widely used data sets demonstrate the effectiveness of the proposed model. | - |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | IEEE transactions on industrial electronics, Dec. 2020, v. 67, no. 12, p. 10931-10941 | en_US |
dcterms.isPartOf | IEEE transactions on industrial electronics | en_US |
dcterms.issued | 2020-12 | - |
dc.identifier.scopus | 2-s2.0-85090552386 | - |
dc.identifier.eissn | 1557-9948 | en_US |
dc.description.validate | 202403 bckw | - |
dc.description.oa | Accepted Manuscript | en_US |
dc.identifier.FolderNumber | EIE-0115 | - |
dc.description.fundingSource | Others | en_US |
dc.description.fundingText | National Natural Science Foundation of China | en_US |
dc.description.pubStatus | Published | en_US |
dc.identifier.OPUS | 50281590 | - |
dc.description.oaCategory | Green (AAM) | en_US |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Lam_Robust_Point_Set.pdf | Pre-Published version | 527.07 kB | Adobe PDF | View/Open |
Page views
1
Citations as of Jun 30, 2024
Downloads
2
Citations as of Jun 30, 2024
SCOPUSTM
Citations
5
Citations as of Jun 21, 2024
WEB OF SCIENCETM
Citations
5
Citations as of Jun 27, 2024
![](/image/google_scholar.jpg)
Google ScholarTM
Check
Altmetric
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.