Evolutionary optimization applied for fine-tuning parameter estimation in optical flow-based environments
dc.contributor.author | Pereira, Danillo Roberto | |
dc.contributor.author | Delpiano, José | |
dc.contributor.author | Papa, João Paulo [UNESP] | |
dc.contributor.institution | Universidade dos Andes (UANDES) | |
dc.contributor.institution | Universidade do Oeste Paulista (UNOESTE)Universidade Estadual Paulista (Unesp) | |
dc.date.accessioned | 2015-11-03T18:26:18Z | |
dc.date.available | 2015-11-03T18:26:18Z | |
dc.date.issued | 2014-01-01 | |
dc.description.abstract | Optical 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.affiliation | Univ Western Sao Paulo UNOESTE, Presidente Prudente, Brazil | |
dc.description.affiliation | Univ Los Andes, Santiago, Chile | |
dc.description.affiliation | Sao Paulo State Univ UNESP, Bauru, Brazil | |
dc.description.affiliationUnesp | Universidade Estadual Paulista (UNESP), Bauru, Brazil | |
dc.format.extent | 125-132 | |
dc.identifier | http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6915299 | |
dc.identifier.citation | 2014 27th Sibgrapi Conference On Graphics, Patterns And Images (sibgrapi). New York: Ieee, p. 125-132, 2014. | |
dc.identifier.doi | 10.1109/SIBGRAPI.2014.22 | |
dc.identifier.lattes | 9039182932747194 | |
dc.identifier.uri | http://hdl.handle.net/11449/130383 | |
dc.identifier.wos | WOS:000352613900017 | |
dc.language.iso | eng | |
dc.publisher | Ieee | |
dc.relation.ispartof | 2014 27th Sibgrapi Conference On Graphics, Patterns And Images (sibgrapi) | |
dc.rights.accessRights | Acesso aberto | |
dc.source | Web of Science | |
dc.subject | Social-Spider optimization | en |
dc.subject | Optical flow | en |
dc.subject | Evolutionary optimization methods | en |
dc.title | Evolutionary optimization applied for fine-tuning parameter estimation in optical flow-based environments | en |
dc.type | Trabalho apresentado em evento | |
dcterms.license | http://www.ieee.org/publications_standards/publications/rights/rights_policies.html | |
dcterms.rightsHolder | Ieee | |
unesp.author.lattes | 9039182932747194 | |
unesp.author.orcid | 0000-0002-6494-7514[3] | |
unesp.campus | Universidade Estadual Paulista (Unesp), Faculdade de Ciências, Bauru | pt |
unesp.department | Computação - FC | pt |