Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/107123
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Electrical and Electronic Engineering | en_US |
dc.creator | Chen, X | en_US |
dc.creator | Sun, ZL | en_US |
dc.creator | Lam, KM | en_US |
dc.creator | Zeng, Z | en_US |
dc.date.accessioned | 2024-06-13T01:04:03Z | - |
dc.date.available | 2024-06-13T01:04:03Z | - |
dc.identifier.issn | 2329-9266 | en_US |
dc.identifier.uri | http://hdl.handle.net/10397/107123 | - |
dc.language.iso | en | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers | en_US |
dc.rights | © 2020 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 X. Chen, Z. -L. Sun, K. -M. Lam and Z. Zeng, "A local deviation constraint based non-rigid structure from motion approach," in IEEE/CAA Journal of Automatica Sinica, vol. 7, no. 5, pp. 1455-1464, September 2020 is available at https://doi.org/10.1109/JAS.2020.1003006. | en_US |
dc.subject | Augmented Lagrange multipliers (ALM) | en_US |
dc.subject | Column-spacefitting | en_US |
dc.subject | Non-rigid structure from motion (NRSFM) | en_US |
dc.title | A local deviation constraint based non-rigid structure from motion approach | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.spage | 1455 | en_US |
dc.identifier.epage | 1464 | en_US |
dc.identifier.volume | 7 | en_US |
dc.identifier.issue | 5 | en_US |
dc.identifier.doi | 10.1109/JAS.2020.1003006 | en_US |
dcterms.abstract | In many traditional non-rigid structure from motion NRSFM approaches, the estimation results of part feature points may significantly deviate from their true values because only the overall estimation error is considered in their models. Aimed at solving this issue, a local deviation-constrained-based column-space-fitting approach is proposed in this paper to alleviate estimation deviation. In our work, an effective model is first constructed with two terms: the overall estimation error, which is computed by a linear subspace representation, and a constraint term, which is based on the variance of the reconstruction error for each frame. Furthermore, an augmented Lagrange multipliers ALM iterative algorithm is presented to optimize the proposed model. Moreover, a convergence analysis is performed with three steps for the optimization process. As both the overall estimation error and the local deviation are utilized, the proposed method can achieve a good estimation performance and a relatively uniform estimation error distribution for different feature points. Experimental results on several widely used synthetic sequences and real sequences demonstrate the effectiveness and feasibility of the proposed algorithm. | en_US |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | IEEE - CAA journal of automatica sinica, Sept. 2020, v. 7, no. 5, p. 1455-1464 | en_US |
dcterms.isPartOf | IEEE - CAA journal of automatica sinica | en_US |
dcterms.issued | 2020-09 | - |
dc.identifier.scopus | 2-s2.0-85078135203 | - |
dc.identifier.eissn | 2329-9274 | en_US |
dc.description.validate | 202403 bckw | en_US |
dc.description.oa | Accepted Manuscript | en_US |
dc.identifier.FolderNumber | EIE-0166 | - |
dc.description.fundingSource | Others | en_US |
dc.description.fundingText | National Natural Science Foundation of China; Anhui Province Key Laboratory of Non-Destructive Evaluation | en_US |
dc.description.pubStatus | Published | en_US |
dc.identifier.OPUS | 20253462 | - |
dc.description.oaCategory | Green (AAM) | en_US |
Appears in Collections: | Journal/Magazine Article |
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File | Description | Size | Format | |
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Lam_Local_Deviation_Constraint.pdf | Pre-Published version | 337.26 kB | Adobe PDF | View/Open |
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