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dc.contributor.authorLin, Xiao
dc.contributor.authorCasas Pla, Josep Ramon
dc.contributor.authorPardàs Feliu, Montse
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions
dc.date.accessioned2017-02-16T11:45:35Z
dc.date.available2017-11-24T01:30:58Z
dc.date.issued2016
dc.identifier.citationLin, X., Casas, J., Pardas, M. 3D point cloud video segmentation based on interaction analysis. A: Workshop on Video Segmentation. "Computer Vision – ECCV 2016 Workshops". Amsterdam: Springer, 2016, p. 821-835.
dc.identifier.isbn978-3-319-49408-1
dc.identifier.urihttp://hdl.handle.net/2117/101137
dc.description.abstractGiven the widespread availability of point cloud data from consumer depth sensors, 3D segmentation becomes a promising building block for high level applications such as scene understanding and interaction analysis. It benefits from the richer information contained in actual world 3D data compared to apparent (projected) data in 2D images. This also implies that the classical color segmentation challenges have recently shifted to RGBD data, whereas new emerging challenges are added as 3D information from depth measurements is usually noisy, sparse and unorganized. We present a novel segmentation approach for 3D point cloud video based on low level features and oriented to the analysis of object interactions. A hierarchical representation of the input point cloud is proposed to efficiently segment 3D data at the finer level, and to temporally establish the correspondence between segments, while dynamically managing the object split and merge at the coarser level. Experiments illustrate promising results and its potential application in object interaction analysis.
dc.format.extent15 p.
dc.language.isoeng
dc.publisherSpringer
dc.subjectÀrees temàtiques de la UPC::Enginyeria de la telecomunicació::Telemàtica i xarxes d'ordinadors::Internet
dc.subject.lcshCloud computing
dc.subject.lcshThree-dimensional imaging
dc.title3D point cloud video segmentation based on interaction analysis
dc.typeConference lecture
dc.subject.lemacImatges tridimensionals
dc.subject.lemacComputació en núvol
dc.contributor.groupUniversitat Politècnica de Catalunya. GPI - Grup de Processament d'Imatge i Vídeo
dc.identifier.doi10.1007/978-3-319-49409-8_67
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttp://link.springer.com/chapter/10.1007/978-3-319-49409-8_67
dc.rights.accessOpen Access
drac.iddocument19706119
dc.description.versionPostprint (author's final draft)
dc.relation.projectidinfo:eu-repo/grantAgreement/MINECO/1PE/TEC2013-43935-R
upcommons.citation.authorLin, X.; Casas, J.; Pardas, M.
upcommons.citation.contributorWorkshop on Video Segmentation
upcommons.citation.pubplaceAmsterdam
upcommons.citation.publishedtrue
upcommons.citation.publicationNameComputer Vision – ECCV 2016 Workshops
upcommons.citation.startingPage821
upcommons.citation.endingPage835


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