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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.accessioned2017-02-15T14:58:25Z
dc.date.available2017-02-15T14:58:25Z
dc.date.issued2016
dc.identifier.citationLin, X., Casas, J., Pardas, M. 3D point cloud video segmentation oriented to the analysis of interactions. A: European Signal Processing Conference. "2016 24th European Signal Processing Conference (EUSIPCO)". Budapest: Institute of Electrical and Electronics Engineers (IEEE), 2016, p. 903-907.
dc.identifier.isbn978-1-5090-1891-8
dc.identifier.urihttp://hdl.handle.net/2117/101101
dc.description.abstractGiven the widespread availability of point cloud data from consumer depth sensors, 3D point cloud 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 real world 3D data compared to 2D images. This also implies that the classical color segmentation challenges have shifted to RGBD data, and new challenges have also emerged as the depth information is usually noisy, sparse and unorganized. Meanwhile, the lack of 3D point cloud ground truth labeling also limits the development and comparison among methods in 3D point cloud segmentation. In this paper, we present two contributions: a novel graph based point cloud segmentation method for RGBD stream data with interacting objects and a new ground truth labeling for a previously published data set. This data set focuses on interaction (merge and split between ’object’ point clouds), which differentiates itself from the few existing labeled RGBD data sets which are more oriented to Simultaneous Localization And Mapping (SLAM) tasks. The proposed point cloud segmentation method is evaluated with the 3D point cloud ground truth labeling. Experiments show the promising result of our approach.
dc.format.extent5 p.
dc.language.isoeng
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
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.subject.otherImage segmentation
dc.subject.otherComputer graphics
dc.subject.otherGraph theory
dc.subject.otherImage colour analysis
dc.title3D point cloud video segmentation oriented to the analysis of interactions
dc.typeConference lecture
dc.subject.lemacComputació en núvol
dc.subject.lemacImatges tridimensionals
dc.contributor.groupUniversitat Politècnica de Catalunya. GPI - Grup de Processament d'Imatge i Vídeo
dc.identifier.doi10.1109/EUSIPCO.2016.7760379
dc.relation.publisherversionhttp://www.eurasip.org/Proceedings/Eusipco/Eusipco2016/papers/1570251637.pdf
dc.rights.accessOpen Access
drac.iddocument19705956
dc.description.versionPostprint (published version)
dc.relation.projectidinfo:eu-repo/grantAgreement/MINECO/1PE/TEC2013-43935-R
upcommons.citation.authorLin, X., Casas, J., Pardas, M.
upcommons.citation.contributorEuropean Signal Processing Conference
upcommons.citation.pubplaceBudapest
upcommons.citation.publishedtrue
upcommons.citation.publicationName2016 24th European Signal Processing Conference (EUSIPCO)
upcommons.citation.startingPage903
upcommons.citation.endingPage907


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