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Fuzzy dynamic model for feature tracking

2010-07Conference paper Restricted access
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datacite.subject.fosCiências Naturais::Ciências da Computação e da Informação
datacite.subject.fosCiências Naturais::Matemáticas
datacite.subject.sdg03:Saúde de Qualidade
datacite.subject.sdg07:Energias Renováveis e Acessíveis
datacite.subject.sdg11:Cidades e Comunidades Sustentáveis
dc.contributor.authorCouto, Pedro
dc.contributor.authorLopes, Nuno Vieira
dc.contributor.authorBustince, Humberto
dc.contributor.authorMelo-Pinto, Pedro
dc.date.accessioned2025-12-03T12:03:22Z
dc.date.available2025-12-03T12:03:22Z
dc.date.issued2010-07
dc.descriptionEISBN - 978-1-4244-6921-5
dc.descriptionConference date - 18 July 2010 - 23 July 2010; Conference code - 82124
dc.description.abstractFeature tracking is one of the most challenging and important tasks in Motion Analysis which plays an important role in several areas of Computer Vision. In this work, a novel approach for feature tracking based on Fuzzy concepts is introduced. Fuzzy Sets related with both cinematic (movement model) and non cinematic (image gray levels) properties are constructed in order to model the feature motion. Meanwhile cinematic related fuzzy sets model the feature movement characteristics, the non cinematic fuzzy sets model the feature visible image related properties. The final motion model is obtained through the fusion of these fuzzy models by means of a fuzzy inference engine. Experimental results are presented showing that the approach successfully copes with usual difficulties within this problem.eng
dc.identifier.citationP. Couto, N. V. Lopes, H. Bustince and P. Melo-Pinto, "Fuzzy dynamic model for feature tracking," International Conference on Fuzzy Systems, Barcelona, Spain, 2010, pp. 1-8, doi: https://doi.org/10.1109/FUZZY.2010.5583979.
dc.identifier.doi10.1109/fuzzy.2010.5583979
dc.identifier.isbn978-1-4244-6919-2
dc.identifier.isbn978-1-4244-6921-5
dc.identifier.issn1098-7584
dc.identifier.urihttp://hdl.handle.net/10400.8/14815
dc.language.isoeng
dc.peerreviewedyes
dc.publisherIEEE Canada
dc.relation.hasversionhttps://ieeexplore.ieee.org/document/5583979
dc.relation.ispartofInternational Conference on Fuzzy Systems
dc.rights.uriN/A
dc.subjectPixel
dc.subjectEngines
dc.subjectFuzzy sets
dc.subjectTracking
dc.subjectKalman filters
dc.subjectShape
dc.subjectAcceleration
dc.titleFuzzy dynamic model for feature trackingeng
dc.typeconference paper
dspace.entity.typePublication
oaire.citation.conferenceDate2010-07
oaire.citation.conferencePlaceBarcelona, Spain
oaire.citation.title2010 IEEE World Congress on Computational Intelligence, WCCI 2010
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNameVieira Lopes
person.givenNameNuno
person.identifier.ciencia-idE117-67AC-0B45
person.identifier.orcid0000-0002-2232-1839
person.identifier.scopus-author-id26031536700
relation.isAuthorOfPublication0d39fc9c-397b-4d8a-857a-3d4da42277a6
relation.isAuthorOfPublication.latestForDiscovery0d39fc9c-397b-4d8a-857a-3d4da42277a6

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Feature tracking is one of the most challenging and important tasks in Motion Analysis which plays an important role in several areas of Computer Vision. In this work, a novel approach for feature tracking based on Fuzzy concepts is introduced. Fuzzy Sets related with both cinematic (movement model) and non cinematic (image gray levels) properties are constructed in order to model the feature motion. Meanwhile cinematic related fuzzy sets model the feature movement characteristics, the non cinematic fuzzy sets model the feature visible image related properties. The final motion model is obtained through the fusion of these fuzzy models by means of a fuzzy inference engine. Experimental results are presented showing that the approach successfully copes with usual difficulties within this problem.
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