A method for mixed states texture segmentation with simultaneous parameter estimation

Autores
Mailing, Agustin Beltran; Cernuschi Frias, Bruno
Año de publicación
2011
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
In this work a method for mixed-state model motion texture segmentation and parameter estimation is presented. We use the Expectation Maximization algorithm for mixture parameter estimation, introducing the Gibbs distribution for moving points, excluding zero discrete component associated with no motion regions. We use then the a posteriori probabilities to generate an alternative field to segment the textures according to its statistical parameters.
Fil: Mailing, Agustin Beltran. Universidad de Buenos Aires. Facultad de Ingeniería; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Saavedra 15. Instituto Argentino de Matemática Alberto Calderón; Argentina
Fil: Cernuschi Frias, Bruno. Universidad de Buenos Aires. Facultad de Ingeniería; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Saavedra 15. Instituto Argentino de Matemática Alberto Calderón; Argentina
Materia
EXPECTATION MAXIMIZATION
MARKOV RANDOM FIELDS
MOTION TEXTURES
PSEUDO-LIKELIHOOD
SEGMENTATION
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/93032
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/93032
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling A method for mixed states texture segmentation with simultaneous parameter estimationMailing, Agustin BeltranCernuschi Frias, BrunoEXPECTATION MAXIMIZATIONMARKOV RANDOM FIELDSMOTION TEXTURESPSEUDO-LIKELIHOODSEGMENTATIONhttps://purl.org/becyt/ford/2.2https://purl.org/becyt/ford/2In this work a method for mixed-state model motion texture segmentation and parameter estimation is presented. We use the Expectation Maximization algorithm for mixture parameter estimation, introducing the Gibbs distribution for moving points, excluding zero discrete component associated with no motion regions. We use then the a posteriori probabilities to generate an alternative field to segment the textures according to its statistical parameters.Fil: Mailing, Agustin Beltran. Universidad de Buenos Aires. Facultad de Ingeniería; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Saavedra 15. Instituto Argentino de Matemática Alberto Calderón; ArgentinaFil: Cernuschi Frias, Bruno. Universidad de Buenos Aires. Facultad de Ingeniería; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Saavedra 15. Instituto Argentino de Matemática Alberto Calderón; ArgentinaElsevier Science2011-11info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/93032Mailing, Agustin Beltran; Cernuschi Frias, Bruno; A method for mixed states texture segmentation with simultaneous parameter estimation; Elsevier Science; Pattern Recognition Letters; 32; 15; 11-2011; 1982-19890167-8655CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.patrec.2011.07.022info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/abs/pii/S0167865511002431info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T09:59:31Zoai:ri.conicet.gov.ar:11336/93032instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-03 09:59:31.888CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv A method for mixed states texture segmentation with simultaneous parameter estimation
title A method for mixed states texture segmentation with simultaneous parameter estimation
spellingShingle A method for mixed states texture segmentation with simultaneous parameter estimation
Mailing, Agustin Beltran
EXPECTATION MAXIMIZATION
MARKOV RANDOM FIELDS
MOTION TEXTURES
PSEUDO-LIKELIHOOD
SEGMENTATION
title_short A method for mixed states texture segmentation with simultaneous parameter estimation
title_full A method for mixed states texture segmentation with simultaneous parameter estimation
title_fullStr A method for mixed states texture segmentation with simultaneous parameter estimation
title_full_unstemmed A method for mixed states texture segmentation with simultaneous parameter estimation
title_sort A method for mixed states texture segmentation with simultaneous parameter estimation
dc.creator.none.fl_str_mv Mailing, Agustin Beltran
Cernuschi Frias, Bruno
author Mailing, Agustin Beltran
author_facet Mailing, Agustin Beltran
Cernuschi Frias, Bruno
author_role author
author2 Cernuschi Frias, Bruno
author2_role author
dc.subject.none.fl_str_mv EXPECTATION MAXIMIZATION
MARKOV RANDOM FIELDS
MOTION TEXTURES
PSEUDO-LIKELIHOOD
SEGMENTATION
topic EXPECTATION MAXIMIZATION
MARKOV RANDOM FIELDS
MOTION TEXTURES
PSEUDO-LIKELIHOOD
SEGMENTATION
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.2
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv In this work a method for mixed-state model motion texture segmentation and parameter estimation is presented. We use the Expectation Maximization algorithm for mixture parameter estimation, introducing the Gibbs distribution for moving points, excluding zero discrete component associated with no motion regions. We use then the a posteriori probabilities to generate an alternative field to segment the textures according to its statistical parameters.
Fil: Mailing, Agustin Beltran. Universidad de Buenos Aires. Facultad de Ingeniería; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Saavedra 15. Instituto Argentino de Matemática Alberto Calderón; Argentina
Fil: Cernuschi Frias, Bruno. Universidad de Buenos Aires. Facultad de Ingeniería; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Saavedra 15. Instituto Argentino de Matemática Alberto Calderón; Argentina
description In this work a method for mixed-state model motion texture segmentation and parameter estimation is presented. We use the Expectation Maximization algorithm for mixture parameter estimation, introducing the Gibbs distribution for moving points, excluding zero discrete component associated with no motion regions. We use then the a posteriori probabilities to generate an alternative field to segment the textures according to its statistical parameters.
publishDate 2011
dc.date.none.fl_str_mv 2011-11
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/93032
Mailing, Agustin Beltran; Cernuschi Frias, Bruno; A method for mixed states texture segmentation with simultaneous parameter estimation; Elsevier Science; Pattern Recognition Letters; 32; 15; 11-2011; 1982-1989
0167-8655
CONICET Digital
CONICET
url http://hdl.handle.net/11336/93032
identifier_str_mv Mailing, Agustin Beltran; Cernuschi Frias, Bruno; A method for mixed states texture segmentation with simultaneous parameter estimation; Elsevier Science; Pattern Recognition Letters; 32; 15; 11-2011; 1982-1989
0167-8655
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1016/j.patrec.2011.07.022
info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/abs/pii/S0167865511002431
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier Science
publisher.none.fl_str_mv Elsevier Science
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.13397

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