Skeletonization of sparse shapes using dynamic competitive neural networks

Autores
Hasperué, Waldo; Corbalán, Leonardo César; Lanzarini, Laura Cristina; Bria, Oscar N.
Año de publicación
2007
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
The detection of regions and objects in digital images is a topic of utmost importance for solving several problems related to the area of pattern recognition. In this direction, skeletonization algorithms are a widely used tool since they allow us to reduce the quantity of available data, easing the detection of characteristics for their recognition and classification. In addition, this transformation of the original data in its essential characteristics eases the elimination of local noise which is present in the data input. This paper proposes a new skeletonization strategy applicable to sparse images from a competitive, dynamic neural network trained with the AVGSOM method. The strategy developed in this paper determines the arc making up the skeleton combining AVGSOM non-supervised learning with a minimum spanning tree. The proposed method has been applied in images with different spanning shape and degree. In particular, the results obtained have been compared to existing solutions, showing successful results. Finally, some conclusions, together with some future lines of work, are presented.
Facultad de Informática
Materia
Ciencias Informáticas
Skeletonization
Dynamic self-organizing maps
Neural networks
Digital image processing
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc/4.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/127869
Acceder
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oai_identifier_str oai:sedici.unlp.edu.ar:10915/127869
network_acronym_str SEDICI
repository_id_str 1329
network_name_str SEDICI (UNLP)
spelling Skeletonization of sparse shapes using dynamic competitive neural networksHasperué, WaldoCorbalán, Leonardo CésarLanzarini, Laura CristinaBria, Oscar N.Ciencias InformáticasSkeletonizationDynamic self-organizing mapsNeural networksDigital image processingThe detection of regions and objects in digital images is a topic of utmost importance for solving several problems related to the area of pattern recognition. In this direction, skeletonization algorithms are a widely used tool since they allow us to reduce the quantity of available data, easing the detection of characteristics for their recognition and classification. In addition, this transformation of the original data in its essential characteristics eases the elimination of local noise which is present in the data input. This paper proposes a new skeletonization strategy applicable to sparse images from a competitive, dynamic neural network trained with the AVGSOM method. The strategy developed in this paper determines the arc making up the skeleton combining AVGSOM non-supervised learning with a minimum spanning tree. The proposed method has been applied in images with different spanning shape and degree. In particular, the results obtained have been compared to existing solutions, showing successful results. Finally, some conclusions, together with some future lines of work, are presented.Facultad de Informática2007-10-02info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArticulohttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdf33-42http://sedici.unlp.edu.ar/handle/10915/127869enginfo:eu-repo/semantics/altIdentifier/url/http://journal.iberamia.org/public/ia-old/articles/540/article%20%281%29.pdfinfo:eu-repo/semantics/altIdentifier/issn/1137-3601info:eu-repo/semantics/altIdentifier/issn/1988-3064info:eu-repo/semantics/altIdentifier/doi/10.4114/ia.v11i35.898info:eu-repo/semantics/reference/hdl/10915/22675info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc/4.0/Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-29T11:31:02Zoai:sedici.unlp.edu.ar:10915/127869Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 11:31:02.871SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Skeletonization of sparse shapes using dynamic competitive neural networks
title Skeletonization of sparse shapes using dynamic competitive neural networks
spellingShingle Skeletonization of sparse shapes using dynamic competitive neural networks
Hasperué, Waldo
Ciencias Informáticas
Skeletonization
Dynamic self-organizing maps
Neural networks
Digital image processing
title_short Skeletonization of sparse shapes using dynamic competitive neural networks
title_full Skeletonization of sparse shapes using dynamic competitive neural networks
title_fullStr Skeletonization of sparse shapes using dynamic competitive neural networks
title_full_unstemmed Skeletonization of sparse shapes using dynamic competitive neural networks
title_sort Skeletonization of sparse shapes using dynamic competitive neural networks
dc.creator.none.fl_str_mv Hasperué, Waldo
Corbalán, Leonardo César
Lanzarini, Laura Cristina
Bria, Oscar N.
author Hasperué, Waldo
author_facet Hasperué, Waldo
Corbalán, Leonardo César
Lanzarini, Laura Cristina
Bria, Oscar N.
author_role author
author2 Corbalán, Leonardo César
Lanzarini, Laura Cristina
Bria, Oscar N.
author2_role author
author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
Skeletonization
Dynamic self-organizing maps
Neural networks
Digital image processing
topic Ciencias Informáticas
Skeletonization
Dynamic self-organizing maps
Neural networks
Digital image processing
dc.description.none.fl_txt_mv The detection of regions and objects in digital images is a topic of utmost importance for solving several problems related to the area of pattern recognition. In this direction, skeletonization algorithms are a widely used tool since they allow us to reduce the quantity of available data, easing the detection of characteristics for their recognition and classification. In addition, this transformation of the original data in its essential characteristics eases the elimination of local noise which is present in the data input. This paper proposes a new skeletonization strategy applicable to sparse images from a competitive, dynamic neural network trained with the AVGSOM method. The strategy developed in this paper determines the arc making up the skeleton combining AVGSOM non-supervised learning with a minimum spanning tree. The proposed method has been applied in images with different spanning shape and degree. In particular, the results obtained have been compared to existing solutions, showing successful results. Finally, some conclusions, together with some future lines of work, are presented.
Facultad de Informática
description The detection of regions and objects in digital images is a topic of utmost importance for solving several problems related to the area of pattern recognition. In this direction, skeletonization algorithms are a widely used tool since they allow us to reduce the quantity of available data, easing the detection of characteristics for their recognition and classification. In addition, this transformation of the original data in its essential characteristics eases the elimination of local noise which is present in the data input. This paper proposes a new skeletonization strategy applicable to sparse images from a competitive, dynamic neural network trained with the AVGSOM method. The strategy developed in this paper determines the arc making up the skeleton combining AVGSOM non-supervised learning with a minimum spanning tree. The proposed method has been applied in images with different spanning shape and degree. In particular, the results obtained have been compared to existing solutions, showing successful results. Finally, some conclusions, together with some future lines of work, are presented.
publishDate 2007
dc.date.none.fl_str_mv 2007-10-02
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Articulo
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://sedici.unlp.edu.ar/handle/10915/127869
url http://sedici.unlp.edu.ar/handle/10915/127869
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://journal.iberamia.org/public/ia-old/articles/540/article%20%281%29.pdf
info:eu-repo/semantics/altIdentifier/issn/1137-3601
info:eu-repo/semantics/altIdentifier/issn/1988-3064
info:eu-repo/semantics/altIdentifier/doi/10.4114/ia.v11i35.898
info:eu-repo/semantics/reference/hdl/10915/22675
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc/4.0/
Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc/4.0/
Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)
dc.format.none.fl_str_mv application/pdf
33-42
dc.source.none.fl_str_mv reponame:SEDICI (UNLP)
instname:Universidad Nacional de La Plata
instacron:UNLP
reponame_str SEDICI (UNLP)
collection SEDICI (UNLP)
instname_str Universidad Nacional de La Plata
instacron_str UNLP
institution UNLP
repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
repository.mail.fl_str_mv alira@sedici.unlp.edu.ar
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score 13.069144

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