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dc.contributor.authorAgudo Martínez, Antonio
dc.contributor.authorMoreno-Noguer, Francesc
dc.contributor.otherInstitut de Robòtica i Informàtica Industrial
dc.date.accessioned2017-11-10T12:08:42Z
dc.date.available2017-11-10T12:08:42Z
dc.date.issued2017
dc.identifier.citationAgudo, A., Moreno-Noguer, F. Global model with local interpretation for dynamic shape reconstruction. A: Winter Vision Meeting: Workshop on Applications of Computer Vision. "Libro de actos 2017 IEEE Winter Conference on Applications of Computer Vision". Santa Rosa: 2017, p. 264-272.
dc.identifier.urihttp://hdl.handle.net/2117/110250
dc.description© 20xx 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.
dc.description.abstractThe most standard approach to resolve the inherent ambiguities of the non-rigid structure from motion problem is using low-rank models that approximate deforming shapes by a linear combination of rigid basis. These models are typically global, i.e., each shape basis contributes equally to all points of the surface. While this approach has been shown effective to represent smooth deformations, its performance degrades for surfaces composed of various regions, each following a different deformation rule. Piecewise methods attempt to capture this type of behavior by locally modeling surface patches, although they subsequently require enforcing global constraints to assemble back the patches. In this paper we propose an approach that combines the best of global and local models: it locally considers low-rank models but, by construction, does not need to impose global constraints to guarantee local patch continuity. We achieve this by a simple expectation maximization strategy that besides learning global shape bases, it locally adapts their contribution to each specific surface region. Furthermore, as a side contribution, in order to split the surface into different local patches, we propose a novel physically-based mesh segmentation approach that obeys an energy criterion. The complete framework is evaluated in both synthetic and real datasets, and shows an improved performance to competing methods.
dc.format.extent9 p.
dc.language.isoeng
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Spain
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subjectÀrees temàtiques de la UPC::Informàtica::Automàtica i control
dc.subject.othercomputer vision
dc.subject.otheroptimisation
dc.subject.otherpattern clustering
dc.titleGlobal model with local interpretation for dynamic shape reconstruction
dc.typeConference report
dc.contributor.groupUniversitat Politècnica de Catalunya. ROBiri - Grup de Robòtica de l'IRI
dc.identifier.doi10.1109/WACV.2017.36
dc.description.peerreviewedPeer Reviewed
dc.subject.inspecClassificació INSPEC::Pattern recognition::Computer vision
dc.relation.publisherversionhttp://ieeexplore.ieee.org/document/7926619/
dc.rights.accessOpen Access
drac.iddocument21560975
dc.description.versionPostprint (author's final draft)
dc.relation.projectidinfo:eu-repo/grantAgreement/EC/H2020/687534/EU/Tight integration of EGNSS and on-board sensors for port vehicle automation/LOGIMATIC
upcommons.citation.authorAgudo, A., Moreno-Noguer, F.
upcommons.citation.contributorWinter Vision Meeting: Workshop on Applications of Computer Vision
upcommons.citation.pubplaceSanta Rosa
upcommons.citation.publishedtrue
upcommons.citation.publicationNameLibro de actos 2017 IEEE Winter Conference on Applications of Computer Vision
upcommons.citation.startingPage264
upcommons.citation.endingPage272


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