Evolutionary optimization applied for fine-tuning parameter estimation in optical flow-based environments
Autor(a) principal: | |
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Data de Publicação: | 2014 |
Outros Autores: | , |
Tipo de documento: | Artigo de conferência |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6915299 http://hdl.handle.net/11449/130383 |
Resumo: | 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. |
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Evolutionary optimization applied for fine-tuning parameter estimation in optical flow-based environmentsSocial-Spider optimizationOptical flowEvolutionary optimization methodsOptical 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.Univ Western Sao Paulo UNOESTE, Presidente Prudente, BrazilUniv Los Andes, Santiago, ChileSao Paulo State Univ UNESP, Bauru, BrazilUniversidade Estadual Paulista (UNESP), Bauru, BrazilIeeeUniversidade dos Andes (UANDES)Universidade do Oeste Paulista (UNOESTE)Universidade Estadual Paulista (Unesp)Pereira, Danillo RobertoDelpiano, JoséPapa, João Paulo [UNESP]2015-11-03T18:26:18Z2015-11-03T18:26:18Z2014-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject125-132http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=69152992014 27th Sibgrapi Conference On Graphics, Patterns And Images (sibgrapi). New York: Ieee, p. 125-132, 2014.http://hdl.handle.net/11449/13038310.1109/SIBGRAPI.2014.22WOS:0003526139000179039182932747194Web of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPeng2014 27th Sibgrapi Conference On Graphics, Patterns And Images (sibgrapi)info:eu-repo/semantics/openAccess2024-04-23T16:11:12Zoai:repositorio.unesp.br:11449/130383Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T13:55:58.102021Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Evolutionary optimization applied for fine-tuning parameter estimation in optical flow-based environments |
title |
Evolutionary optimization applied for fine-tuning parameter estimation in optical flow-based environments |
spellingShingle |
Evolutionary optimization applied for fine-tuning parameter estimation in optical flow-based environments Pereira, Danillo Roberto Social-Spider optimization Optical flow Evolutionary optimization methods |
title_short |
Evolutionary optimization applied for fine-tuning parameter estimation in optical flow-based environments |
title_full |
Evolutionary optimization applied for fine-tuning parameter estimation in optical flow-based environments |
title_fullStr |
Evolutionary optimization applied for fine-tuning parameter estimation in optical flow-based environments |
title_full_unstemmed |
Evolutionary optimization applied for fine-tuning parameter estimation in optical flow-based environments |
title_sort |
Evolutionary optimization applied for fine-tuning parameter estimation in optical flow-based environments |
author |
Pereira, Danillo Roberto |
author_facet |
Pereira, Danillo Roberto Delpiano, José Papa, João Paulo [UNESP] |
author_role |
author |
author2 |
Delpiano, José Papa, João Paulo [UNESP] |
author2_role |
author author |
dc.contributor.none.fl_str_mv |
Universidade dos Andes (UANDES) Universidade do Oeste Paulista (UNOESTE)Universidade Estadual Paulista (Unesp) |
dc.contributor.author.fl_str_mv |
Pereira, Danillo Roberto Delpiano, José Papa, João Paulo [UNESP] |
dc.subject.por.fl_str_mv |
Social-Spider optimization Optical flow Evolutionary optimization methods |
topic |
Social-Spider optimization Optical flow Evolutionary optimization methods |
description |
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. |
publishDate |
2014 |
dc.date.none.fl_str_mv |
2014-01-01 2015-11-03T18:26:18Z 2015-11-03T18:26:18Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/conferenceObject |
format |
conferenceObject |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6915299 2014 27th Sibgrapi Conference On Graphics, Patterns And Images (sibgrapi). New York: Ieee, p. 125-132, 2014. http://hdl.handle.net/11449/130383 10.1109/SIBGRAPI.2014.22 WOS:000352613900017 9039182932747194 |
url |
http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6915299 http://hdl.handle.net/11449/130383 |
identifier_str_mv |
2014 27th Sibgrapi Conference On Graphics, Patterns And Images (sibgrapi). New York: Ieee, p. 125-132, 2014. 10.1109/SIBGRAPI.2014.22 WOS:000352613900017 9039182932747194 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
2014 27th Sibgrapi Conference On Graphics, Patterns And Images (sibgrapi) |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
125-132 |
dc.publisher.none.fl_str_mv |
Ieee |
publisher.none.fl_str_mv |
Ieee |
dc.source.none.fl_str_mv |
Web of Science reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1808128291491545088 |