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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.accessioned2018-07-23T10:22:43Z
dc.date.issued2017
dc.identifier.citationLin, X., Casas, J., Pardas, M. 3D point cloud segmentation using a fully connected conditional random field. A: European Signal Processing Conference. "2017 25th European Signal Processing Conference: EUSIPCO 2017: Kos, Greece: 28 August-2 September 2017". Institute of Electrical and Electronics Engineers (IEEE), 2017, p. 66-70.
dc.identifier.isbn978-1-5386-0751-0
dc.identifier.urihttp://hdl.handle.net/2117/119742
dc.description.abstractTraditional image segmentation methods working with low level image features are usually difficult to adapt to higher level tasks, such as object recognition and scene understanding. Object segmentation emerges as a new challenge in this research field. It aims at obtaining more meaningful segments related to semantic objects in the scene by analyzing a combination of different information. 3D point cloud data obtained from consumer depth sensors has been exploited to tackle many computer vision problems due to its richer information about the geometry of 3D scenes compared to 2D images. Meanwhile, new challenges have also emerged as the depth information is usually noisy, sparse and unorganized. In this paper, we present a novel point cloud segmentation approach for segmenting interacting objects in a stream of point clouds by exploiting spatio-temporal coherence. We pose the problem as an energy minimization task in a fully connected conditional random field with the energy function defined based on both current and previous information. We compare different methods and prove the improved segmentation performance and robustness of the proposed approach in sequences with over 2k frames.
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::Processament del senyal
dc.subject.lcshSignal processing
dc.subject.otherComputer vision
dc.subject.otherImage segmentation
dc.subject.otherImage sequences
dc.subject.otherMinimisation
dc.subject.otherObject recognition
dc.title3D point cloud segmentation using a fully connected conditional random field
dc.typeConference lecture
dc.subject.lemacTractament del senyal
dc.contributor.groupUniversitat Politècnica de Catalunya. GPI - Grup de Processament d'Imatge i Vídeo
dc.identifier.doi10.23919/EUSIPCO.2017.8081170
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttps://ieeexplore.ieee.org/document/8081170/
dc.rights.accessRestricted access - publisher's policy
drac.iddocument23245965
dc.description.versionPostprint (published version)
dc.date.lift10000-01-01
upcommons.citation.authorLin, X., Casas, J., Pardas, M.
upcommons.citation.contributorEuropean Signal Processing Conference
upcommons.citation.publishedtrue
upcommons.citation.publicationName2017 25th European Signal Processing Conference: EUSIPCO 2017: Kos, Greece: 28 August-2 September 2017
upcommons.citation.startingPage66
upcommons.citation.endingPage70


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