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dc.contributor.authorPulgarin Giraldo, Juan Diegospa
dc.contributor.authorAlvarez Meza, Andres Marinospa
dc.contributor.authorRuales Tores, A. A.spa
dc.contributor.authorCastellanos Dominguez, Germanspa
dc.coverage.spatialUniversidad Autónoma de Occidente. Calle 25 115-85. Km 2 vía Cali-Jamundíspa
dc.date.accessioned2019-11-28T20:35:57Zspa
dc.date.available2019-11-28T20:35:57Zspa
dc.date.issued2017-05-27spa
dc.identifier.citationPulgarin-Giraldo J.D., Ruales-Torres A.A., Alvarez-Meza A.M., Castellanos-Dominguez G. (2017) Relevant Kinematic Feature Selection to Support Human Action Recognition in MoCap Data. In: Ferrández Vicente J., Álvarez-Sánchez J., de la Paz López F., Toledo Moreo J., Adeli H. (eds) Biomedical Applications Based on Natural and Artificial Computing. IWINAC 2017. Lecture Notes in Computer Science, vol 10338. Springer, Chameng
dc.identifier.issn1611-3349 (en línea)spa
dc.identifier.issn0302-9743 (impresa)spa
dc.identifier.issn978-3-319-59773-7 (en línea)spa
dc.identifier.issn9783319597720 (impreso)spa
dc.identifier.urihttp://hdl.handle.net/10614/11615spa
dc.identifier.urihttps://link.springer.com/chapter/10.1007/978-3-319-59740-9_23spa
dc.identifier.urihttps://link.springer.com/content/pdf/10.1007%2F978-3-319-59740-9.pdfspa
dc.description.abstractThis paper presents a feature selection comparison oriented to human action recognition only with the kinematic features of skeleton representation. For this purpose, three relevance methods are used to rank the contribution of kinematic features for classifying an action is employed. Particularly, the method with the best results includes the supervised information regarding the action to find out a relevant set of features, encoding the most discriminative information. Experimental results are obtained on a well-known public data (MSR Action3D). Results are encouraging to use kernel theory methods to get better kinematic feature selection for each action with a good generalization indistinct to the subjecteng
dc.formatapplication/pdfspa
dc.format.extentPáginas 501-509spa
dc.language.isoengeng
dc.publisherSpringer, Chameng
dc.relationBiomedical Applications Based on Natural and Artificial Computing : International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2017, Corunna, Spain, June 19-23, 2017, Proceedings, Part II. Páginas 501-509, (2017)eng
dc.relation.haspartLecture Notes in Computer Science. 10337. Theoretical Computer Science and General Issues. 10337eng
dc.rightsDerechos Reservados - Universidad Autónoma de Occidentespa
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/spa
dc.sourceinstname:Universidad Autónoma de Occidentespa
dc.sourcereponame:Repositorio Institucional UAOspa
dc.subjectCenter kernel alignmenteng
dc.subjectFeature selectioneng
dc.subjectHuman motioneng
dc.subjectKinematicseng
dc.subjectRelevanceeng
dc.subjectReliefFeng
dc.subjectMotion capture dataeng
dc.subjectPrincipal Component Analysiseng
dc.titleRelevant Kinematic Feature Selection to Support Human Action Recognition in MoCap dataeng
dc.typeCapítulo - Parte de Librospa
dc.subject.lembKinematicseng
dc.subject.lembHuman beings--Attitude and movementeng
dc.subject.lembCinemáticaspa
dc.subject.lembActitud y movimiento del hombrespa
dc.subject.armarcMotioneng
dc.subject.armarcMechanical movementseng
dc.subject.armarcMovimientospa
dc.subject.armarcMovimientos mecánicosspa
dc.creator.emailjdpulgaring@unal.edu.cospa
dc.identifier.doihttps://doi.org/10.1007/978-3-319-59773-7_51spa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.rights.creativecommonsAtribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)spa
dc.type.coarhttp://purl.org/coar/resource_type/c_3248spa
dc.type.contentTextspa
dc.type.driverinfo:eu-repo/semantics/bookPartspa
dc.type.redcolhttps://purl.org/redcol/resource_type/CAP_LIBspa
oaire.accessrightshttp://purl.org/coar/access_right/c_abf2spa
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85spa
dc.type.versioninfo:eu-repo/semantics/publishedVersionspa
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