A MATLAB SMO implementation to train a SVM classifier: Application to multi-style license plate numbers recognition

Autores
Negri, Pablo Augusto
Año de publicación
2018
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
This paper implements the Support Vector Machine (SVM) training procedure proposed by John Platt denominated Sequential Minimimal Optimization (SMO). The application of this system involves a multi-style license plate characters recognition identifying numbers from “0” to “9”. In order to be robust against license plates with different character/background colors, the characters (numbers) visual information is encoded using Histograms of Oriented Gradients (HOG). A reliability measure to validate the system outputs is also proposed. Several tests are performed to evaluate the sensitivity of the algorithm to different parameters and kernel functions.
Fil: Negri, Pablo Augusto. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigación En Ciencias de la Computación. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigación En Ciencias de la Computacion; Argentina
Materia
HISTOGRAM OF ORIENTED GRADIENTS
MULTI-STYLE LICENSE PLATE RECOGNITION
SEQUENTIAL MINIMAL OPTIMIZATION
SUPPORT VECTOR MACHINE
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:11336/98798
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/98798
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling A MATLAB SMO implementation to train a SVM classifier: Application to multi-style license plate numbers recognitionNegri, Pablo AugustoHISTOGRAM OF ORIENTED GRADIENTSMULTI-STYLE LICENSE PLATE RECOGNITIONSEQUENTIAL MINIMAL OPTIMIZATIONSUPPORT VECTOR MACHINEhttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1This paper implements the Support Vector Machine (SVM) training procedure proposed by John Platt denominated Sequential Minimimal Optimization (SMO). The application of this system involves a multi-style license plate characters recognition identifying numbers from “0” to “9”. In order to be robust against license plates with different character/background colors, the characters (numbers) visual information is encoded using Histograms of Oriented Gradients (HOG). A reliability measure to validate the system outputs is also proposed. Several tests are performed to evaluate the sensitivity of the algorithm to different parameters and kernel functions.Fil: Negri, Pablo Augusto. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigación En Ciencias de la Computación. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigación En Ciencias de la Computacion; ArgentinaImage Processing on Line2018-05info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/98798Negri, Pablo Augusto; A MATLAB SMO implementation to train a SVM classifier: Application to multi-style license plate numbers recognition; Image Processing on Line; Image Processing On Line; 8; 5-2018; 37-502105-1232CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.5201/ipol.2018.173info:eu-repo/semantics/altIdentifier/url/http://www.ipol.im/pub/art/2018/173/info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-22T12:14:52Zoai:ri.conicet.gov.ar:11336/98798instacron: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:34982025-10-22 12:14:52.82CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv A MATLAB SMO implementation to train a SVM classifier: Application to multi-style license plate numbers recognition
title A MATLAB SMO implementation to train a SVM classifier: Application to multi-style license plate numbers recognition
spellingShingle A MATLAB SMO implementation to train a SVM classifier: Application to multi-style license plate numbers recognition
Negri, Pablo Augusto
HISTOGRAM OF ORIENTED GRADIENTS
MULTI-STYLE LICENSE PLATE RECOGNITION
SEQUENTIAL MINIMAL OPTIMIZATION
SUPPORT VECTOR MACHINE
title_short A MATLAB SMO implementation to train a SVM classifier: Application to multi-style license plate numbers recognition
title_full A MATLAB SMO implementation to train a SVM classifier: Application to multi-style license plate numbers recognition
title_fullStr A MATLAB SMO implementation to train a SVM classifier: Application to multi-style license plate numbers recognition
title_full_unstemmed A MATLAB SMO implementation to train a SVM classifier: Application to multi-style license plate numbers recognition
title_sort A MATLAB SMO implementation to train a SVM classifier: Application to multi-style license plate numbers recognition
dc.creator.none.fl_str_mv Negri, Pablo Augusto
author Negri, Pablo Augusto
author_facet Negri, Pablo Augusto
author_role author
dc.subject.none.fl_str_mv HISTOGRAM OF ORIENTED GRADIENTS
MULTI-STYLE LICENSE PLATE RECOGNITION
SEQUENTIAL MINIMAL OPTIMIZATION
SUPPORT VECTOR MACHINE
topic HISTOGRAM OF ORIENTED GRADIENTS
MULTI-STYLE LICENSE PLATE RECOGNITION
SEQUENTIAL MINIMAL OPTIMIZATION
SUPPORT VECTOR MACHINE
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv This paper implements the Support Vector Machine (SVM) training procedure proposed by John Platt denominated Sequential Minimimal Optimization (SMO). The application of this system involves a multi-style license plate characters recognition identifying numbers from “0” to “9”. In order to be robust against license plates with different character/background colors, the characters (numbers) visual information is encoded using Histograms of Oriented Gradients (HOG). A reliability measure to validate the system outputs is also proposed. Several tests are performed to evaluate the sensitivity of the algorithm to different parameters and kernel functions.
Fil: Negri, Pablo Augusto. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigación En Ciencias de la Computación. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigación En Ciencias de la Computacion; Argentina
description This paper implements the Support Vector Machine (SVM) training procedure proposed by John Platt denominated Sequential Minimimal Optimization (SMO). The application of this system involves a multi-style license plate characters recognition identifying numbers from “0” to “9”. In order to be robust against license plates with different character/background colors, the characters (numbers) visual information is encoded using Histograms of Oriented Gradients (HOG). A reliability measure to validate the system outputs is also proposed. Several tests are performed to evaluate the sensitivity of the algorithm to different parameters and kernel functions.
publishDate 2018
dc.date.none.fl_str_mv 2018-05
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/11336/98798
Negri, Pablo Augusto; A MATLAB SMO implementation to train a SVM classifier: Application to multi-style license plate numbers recognition; Image Processing on Line; Image Processing On Line; 8; 5-2018; 37-50
2105-1232
CONICET Digital
CONICET
url http://hdl.handle.net/11336/98798
identifier_str_mv Negri, Pablo Augusto; A MATLAB SMO implementation to train a SVM classifier: Application to multi-style license plate numbers recognition; Image Processing on Line; Image Processing On Line; 8; 5-2018; 37-50
2105-1232
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.5201/ipol.2018.173
info:eu-repo/semantics/altIdentifier/url/http://www.ipol.im/pub/art/2018/173/
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
dc.publisher.none.fl_str_mv Image Processing on Line
publisher.none.fl_str_mv Image Processing on Line
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
_version_ 1846782559403376640
score 12.982451

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