Evolutionary optimization applied for fine-tuning parameter estimation in optical flow-based environments

dc.contributor.authorPereira, Danillo Roberto
dc.contributor.authorDelpiano, José
dc.contributor.authorPapa, João Paulo [UNESP]
dc.contributor.institutionUniversidade dos Andes (UANDES)
dc.contributor.institutionUniversidade do Oeste Paulista (UNOESTE)Universidade Estadual Paulista (Unesp)
dc.date.accessioned2015-11-03T18:26:18Z
dc.date.available2015-11-03T18:26:18Z
dc.date.issued2014-01-01
dc.description.abstractOptical flow methods are accurate algorithms for estimating the displacement and velocity fields of objects in a wide variety of applications, being their performance dependent on the configuration of a set of parameters. Since there is a lack of research that aims to automatically tune such parameters, in this work we have proposed an evolutionary-based framework for such task, thus introducing three techniques for such purpose: Particle Swarm Optimization, Harmony Search and Social-Spider Optimization. The proposed framework has been compared against with the well-known Large Displacement Optical Flow approach, obtaining the best results in three out eight image sequences provided by a public dataset. Additionally, the proposed framework can be used with any other optimization technique.en
dc.description.affiliationUniv Western Sao Paulo UNOESTE, Presidente Prudente, Brazil
dc.description.affiliationUniv Los Andes, Santiago, Chile
dc.description.affiliationSao Paulo State Univ UNESP, Bauru, Brazil
dc.description.affiliationUnespUniversidade Estadual Paulista (UNESP), Bauru, Brazil
dc.format.extent125-132
dc.identifierhttp://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6915299
dc.identifier.citation2014 27th Sibgrapi Conference On Graphics, Patterns And Images (sibgrapi). New York: Ieee, p. 125-132, 2014.
dc.identifier.doi10.1109/SIBGRAPI.2014.22
dc.identifier.lattes9039182932747194
dc.identifier.urihttp://hdl.handle.net/11449/130383
dc.identifier.wosWOS:000352613900017
dc.language.isoeng
dc.publisherIeee
dc.relation.ispartof2014 27th Sibgrapi Conference On Graphics, Patterns And Images (sibgrapi)
dc.rights.accessRightsAcesso aberto
dc.sourceWeb of Science
dc.subjectSocial-Spider optimizationen
dc.subjectOptical flowen
dc.subjectEvolutionary optimization methodsen
dc.titleEvolutionary optimization applied for fine-tuning parameter estimation in optical flow-based environmentsen
dc.typeTrabalho apresentado em evento
dcterms.licensehttp://www.ieee.org/publications_standards/publications/rights/rights_policies.html
dcterms.rightsHolderIeee
unesp.author.lattes9039182932747194
unesp.author.orcid0000-0002-6494-7514[3]
unesp.campusUniversidade Estadual Paulista (Unesp), Faculdade de Ciências, Baurupt
unesp.departmentComputação - FCpt

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