Limited receptive area neural classifier for texture recognition of metal surfaces

Autores
Martín, Anabel; Baidyk, Tatiana; Makeyev, Oleksandr
Año de publicación
2006
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
The Limited Receptive Area (LIRA) neural classifier is proposed for texture recognition of mechanically treated metal surfaces. It can be used in systems that have to recognize position and orientation of complex work pieces in the task of assembly of micromechanical devices. The performance of the proposed classifier was tested on specially created image database in recognition of four texture types that correspond to metal surfaces after:milling, polishing with sandpaper, turning with lathe and polishing with file. The promising recognition rate of 99.7% was obtained
IFIP International Conference on Artificial Intelligence in Theory and Practice - Machine Vision
Red de Universidades con Carreras en Informática (RedUNCI)
Materia
Ciencias Informáticas
Object recognition
Scene Analysis
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/23948
Acceder
id SEDICI_3a9150412680cb19c2ab797f37c8057e
oai_identifier_str oai:sedici.unlp.edu.ar:10915/23948
network_acronym_str SEDICI
repository_id_str 1329
network_name_str SEDICI (UNLP)
spelling Limited receptive area neural classifier for texture recognition of metal surfacesMartín, AnabelBaidyk, TatianaMakeyev, OleksandrCiencias InformáticasObject recognitionScene AnalysisThe Limited Receptive Area (LIRA) neural classifier is proposed for texture recognition of mechanically treated metal surfaces. It can be used in systems that have to recognize position and orientation of complex work pieces in the task of assembly of micromechanical devices. The performance of the proposed classifier was tested on specially created image database in recognition of four texture types that correspond to metal surfaces after:milling, polishing with sandpaper, turning with lathe and polishing with file. The promising recognition rate of 99.7% was obtainedIFIP International Conference on Artificial Intelligence in Theory and Practice - Machine VisionRed de Universidades con Carreras en Informática (RedUNCI)2006-08info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdfhttp://sedici.unlp.edu.ar/handle/10915/23948enginfo:eu-repo/semantics/altIdentifier/isbn/0-387-34654-6info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/2.5/ar/Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-10-22T16:37:13Zoai:sedici.unlp.edu.ar:10915/23948Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-10-22 16:37:14.061SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Limited receptive area neural classifier for texture recognition of metal surfaces
title Limited receptive area neural classifier for texture recognition of metal surfaces
spellingShingle Limited receptive area neural classifier for texture recognition of metal surfaces
Martín, Anabel
Ciencias Informáticas
Object recognition
Scene Analysis
title_short Limited receptive area neural classifier for texture recognition of metal surfaces
title_full Limited receptive area neural classifier for texture recognition of metal surfaces
title_fullStr Limited receptive area neural classifier for texture recognition of metal surfaces
title_full_unstemmed Limited receptive area neural classifier for texture recognition of metal surfaces
title_sort Limited receptive area neural classifier for texture recognition of metal surfaces
dc.creator.none.fl_str_mv Martín, Anabel
Baidyk, Tatiana
Makeyev, Oleksandr
author Martín, Anabel
author_facet Martín, Anabel
Baidyk, Tatiana
Makeyev, Oleksandr
author_role author
author2 Baidyk, Tatiana
Makeyev, Oleksandr
author2_role author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
Object recognition
Scene Analysis
topic Ciencias Informáticas
Object recognition
Scene Analysis
dc.description.none.fl_txt_mv The Limited Receptive Area (LIRA) neural classifier is proposed for texture recognition of mechanically treated metal surfaces. It can be used in systems that have to recognize position and orientation of complex work pieces in the task of assembly of micromechanical devices. The performance of the proposed classifier was tested on specially created image database in recognition of four texture types that correspond to metal surfaces after:milling, polishing with sandpaper, turning with lathe and polishing with file. The promising recognition rate of 99.7% was obtained
IFIP International Conference on Artificial Intelligence in Theory and Practice - Machine Vision
Red de Universidades con Carreras en Informática (RedUNCI)
description The Limited Receptive Area (LIRA) neural classifier is proposed for texture recognition of mechanically treated metal surfaces. It can be used in systems that have to recognize position and orientation of complex work pieces in the task of assembly of micromechanical devices. The performance of the proposed classifier was tested on specially created image database in recognition of four texture types that correspond to metal surfaces after:milling, polishing with sandpaper, turning with lathe and polishing with file. The promising recognition rate of 99.7% was obtained
publishDate 2006
dc.date.none.fl_str_mv 2006-08
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
info:eu-repo/semantics/publishedVersion
Objeto de conferencia
http://purl.org/coar/resource_type/c_5794
info:ar-repo/semantics/documentoDeConferencia
format conferenceObject
status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/23948
url http://sedici.unlp.edu.ar/handle/10915/23948
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/isbn/0-387-34654-6
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)
dc.format.none.fl_str_mv application/pdf
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 12.982451

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