A New Edge Detection Method Based on Global Evaluation Using Supervised Classification Algorithms

Traditionally, the last step of edge detection algorithms, which is called scaling-evaluation, produces the final output classifying each pixel as edge or nonedge. This last step is usually done based on local evaluation methods. The local evaluation makes this classification based on measures obtai...

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Detalles Bibliográficos
Autores: Flores Vidal, Pablo Arcadio, Villarino, Guillermo, Gómez González, Daniel, Montero De Juan, Francisco Javier
Tipo de recurso: artículo
Fecha de publicación:2019
País:España
Institución:Universidad Complutense de Madrid (UCM)
Repositorio:Docta Complutense
Idioma:inglés
OAI Identifier:oai:docta.ucm.es:20.500.14352/12426
Acceso en línea:https://hdl.handle.net/20.500.14352/12426
Access Level:acceso abierto
Palabra clave:51:004
Image processing
Edge detection
Global evaluation
Edge segments
Supervised classification
Cibernética matemática
1207.03 Cibernética
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oai_identifier_str oai:docta.ucm.es:20.500.14352/12426
network_acronym_str ES
network_name_str España
repository_id_str
spelling A New Edge Detection Method Based on Global Evaluation Using Supervised Classification AlgorithmsFlores Vidal, Pablo ArcadioVillarino, GuillermoGómez González, DanielMontero De Juan, Francisco Javier51:004Image processingEdge detectionGlobal evaluationEdge segmentsSupervised classificationCibernética matemática1207.03 CibernéticaTraditionally, the last step of edge detection algorithms, which is called scaling-evaluation, produces the final output classifying each pixel as edge or nonedge. This last step is usually done based on local evaluation methods. The local evaluation makes this classification based on measures obtained for every pixel. By contrast, in this work, we propose a global evaluation approach based on the idea of edge list to produce a solution that suits more with the human perception. In particular, we propose a new evaluation method that can be combined with any classical edge detection algorithm in an easy way to produce a novel edge detection algorithm. The new global evaluation method is divided in four steps: in first place we build the edge lists, that we have called edge segments. In second place we extract the characteristics associated to each segment: length, intensity, location, and so on. In the third step we learn the characteristics that make a segment good enough to become an edge. At the fourth step, we apply the classification task. In this work we have built the ground truth of edge list necessary for the supervised classification. Finally, we test the effectiveness of this algorithm against other classical algorithms based on local evaluation approach.Atlantis PressUniversidad Complutense de Madrid20192019-01-0120192019-01-01journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/20.500.14352/12426reponame:Docta Complutenseinstname:Universidad Complutense de Madrid (UCM)InglésengMinisterio de Economía y Competitividad http://dx.doi.org/10.13039/501100003329 Not available TIN2015-66471-P TECNICAS DE OBTENCION, PROCESAMIENTO Y REPRESENTACION DE INFORMACION DIFUSA PARA LA TOMA DE DECISIONESopen accesshttp://purl.org/coar/access_right/c_abf2Atribución-NoComercial 3.0 Españahttps://creativecommons.org/licenses/by-nc/3.0/es/info:eu-repo/semantics/openAccessoai:docta.ucm.es:20.500.14352/124262026-06-02T12:44:21Z
dc.title.none.fl_str_mv A New Edge Detection Method Based on Global Evaluation Using Supervised Classification Algorithms
title A New Edge Detection Method Based on Global Evaluation Using Supervised Classification Algorithms
spellingShingle A New Edge Detection Method Based on Global Evaluation Using Supervised Classification Algorithms
Flores Vidal, Pablo Arcadio
51:004
Image processing
Edge detection
Global evaluation
Edge segments
Supervised classification
Cibernética matemática
1207.03 Cibernética
title_short A New Edge Detection Method Based on Global Evaluation Using Supervised Classification Algorithms
title_full A New Edge Detection Method Based on Global Evaluation Using Supervised Classification Algorithms
title_fullStr A New Edge Detection Method Based on Global Evaluation Using Supervised Classification Algorithms
title_full_unstemmed A New Edge Detection Method Based on Global Evaluation Using Supervised Classification Algorithms
title_sort A New Edge Detection Method Based on Global Evaluation Using Supervised Classification Algorithms
dc.creator.none.fl_str_mv Flores Vidal, Pablo Arcadio
Villarino, Guillermo
Gómez González, Daniel
Montero De Juan, Francisco Javier
author Flores Vidal, Pablo Arcadio
author_facet Flores Vidal, Pablo Arcadio
Villarino, Guillermo
Gómez González, Daniel
Montero De Juan, Francisco Javier
author_role author
author2 Villarino, Guillermo
Gómez González, Daniel
Montero De Juan, Francisco Javier
author2_role author
author
author
dc.contributor.none.fl_str_mv Universidad Complutense de Madrid
dc.subject.none.fl_str_mv 51:004
Image processing
Edge detection
Global evaluation
Edge segments
Supervised classification
Cibernética matemática
1207.03 Cibernética
topic 51:004
Image processing
Edge detection
Global evaluation
Edge segments
Supervised classification
Cibernética matemática
1207.03 Cibernética
description Traditionally, the last step of edge detection algorithms, which is called scaling-evaluation, produces the final output classifying each pixel as edge or nonedge. This last step is usually done based on local evaluation methods. The local evaluation makes this classification based on measures obtained for every pixel. By contrast, in this work, we propose a global evaluation approach based on the idea of edge list to produce a solution that suits more with the human perception. In particular, we propose a new evaluation method that can be combined with any classical edge detection algorithm in an easy way to produce a novel edge detection algorithm. The new global evaluation method is divided in four steps: in first place we build the edge lists, that we have called edge segments. In second place we extract the characteristics associated to each segment: length, intensity, location, and so on. In the third step we learn the characteristics that make a segment good enough to become an edge. At the fourth step, we apply the classification task. In this work we have built the ground truth of edge list necessary for the supervised classification. Finally, we test the effectiveness of this algorithm against other classical algorithms based on local evaluation approach.
publishDate 2019
dc.date.none.fl_str_mv 2019
2019-01-01
2019
2019-01-01
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/20.500.14352/12426
url https://hdl.handle.net/20.500.14352/12426
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv Ministerio de Economía y Competitividad http://dx.doi.org/10.13039/501100003329 Not available TIN2015-66471-P TECNICAS DE OBTENCION, PROCESAMIENTO Y REPRESENTACION DE INFORMACION DIFUSA PARA LA TOMA DE DECISIONES
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Atribución-NoComercial 3.0 España
https://creativecommons.org/licenses/by-nc/3.0/es/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Atribución-NoComercial 3.0 España
https://creativecommons.org/licenses/by-nc/3.0/es/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Atlantis Press
publisher.none.fl_str_mv Atlantis Press
dc.source.none.fl_str_mv reponame:Docta Complutense
instname:Universidad Complutense de Madrid (UCM)
instname_str Universidad Complutense de Madrid (UCM)
reponame_str Docta Complutense
collection Docta Complutense
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.300719

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