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Article: A subspace method for projective reconstruction from multiple images with missing data

TitleA subspace method for projective reconstruction from multiple images with missing data
Authors
KeywordsFactorization method
Multiple views
Structure from motion
Subspace method
Issue Date2006
PublisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/imavis
Citation
Image And Vision Computing, 2006, v. 24 n. 5, p. 515-524 How to Cite?
AbstractIn this paper, we consider the problem of projective reconstruction based on the subspace method. Unlike existing subspace methods which require that all the points are visible in all views, we propose an algorithm to estimate projective shape, projective depths and missing data iteratively. All these estimation problems are formulated within a subspace framework in terms of the minimization of a single consistent objective function, hence ensuring the convergence of the iterative solution. Experimental results using both synthetic data and real images are provided to illustrate the performance of the proposed method. © 2006.
Persistent Identifierhttp://hdl.handle.net/10722/73654
ISSN
2015 Impact Factor: 1.766
2015 SCImago Journal Rankings: 1.700
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorTang, WKen_HK
dc.contributor.authorHung, YSen_HK
dc.date.accessioned2010-09-06T06:53:28Z-
dc.date.available2010-09-06T06:53:28Z-
dc.date.issued2006en_HK
dc.identifier.citationImage And Vision Computing, 2006, v. 24 n. 5, p. 515-524en_HK
dc.identifier.issn0262-8856en_HK
dc.identifier.urihttp://hdl.handle.net/10722/73654-
dc.description.abstractIn this paper, we consider the problem of projective reconstruction based on the subspace method. Unlike existing subspace methods which require that all the points are visible in all views, we propose an algorithm to estimate projective shape, projective depths and missing data iteratively. All these estimation problems are formulated within a subspace framework in terms of the minimization of a single consistent objective function, hence ensuring the convergence of the iterative solution. Experimental results using both synthetic data and real images are provided to illustrate the performance of the proposed method. © 2006.en_HK
dc.languageengen_HK
dc.publisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/imavisen_HK
dc.relation.ispartofImage and Vision Computingen_HK
dc.rightsImage and Vision Computing. Copyright © Elsevier BV.en_HK
dc.subjectFactorization methoden_HK
dc.subjectMultiple viewsen_HK
dc.subjectStructure from motionen_HK
dc.subjectSubspace methoden_HK
dc.titleA subspace method for projective reconstruction from multiple images with missing dataen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0262-8856&volume=24&spage=515&epage=524&date=2006&atitle=A+Subspace+Method+for+Projective+Reconstruction+from+Multiple+Images+with+Missing+Dataen_HK
dc.identifier.emailTang, WK:wktang@hku.hken_HK
dc.identifier.emailHung, YS:yshung@eee.hku.hken_HK
dc.identifier.authorityTang, WK=rp00175en_HK
dc.identifier.authorityHung, YS=rp00220en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.imavis.2006.02.003en_HK
dc.identifier.scopuseid_2-s2.0-33646893927en_HK
dc.identifier.hkuros117238en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-33646893927&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume24en_HK
dc.identifier.issue5en_HK
dc.identifier.spage515en_HK
dc.identifier.epage524en_HK
dc.identifier.isiWOS:000238626600010-
dc.publisher.placeNetherlandsen_HK
dc.identifier.scopusauthoridTang, WK=36790135500en_HK
dc.identifier.scopusauthoridHung, YS=8091656200en_HK

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