Parallel recognition and classification of objects

Autores
Felice, Rodrigo; Ruscitti, Fernando; Naiouf, Marcelo; De Giusti, Armando Eduardo
Año de publicación
1999
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
The development of parallel algorithms for an automatic recognition and classification of objects from an industrial line (either production or packaging) is presented. This kind of problem introduces a temporal restriction on images processing, a parallel resolution being therefore required. We have chosen simple objects (fruits, eggs, etc.), which are classified according to characteristics such as shape, color, size, defects (stains, loss of color), etc. By means of this classification, objects can be sent, for example, to different sectors of the line. Algorithms parallelization on a heterogeneous computers network with a PVM (Parallel Virtual Machine) support is studied in this paper. Finally, some quantitative results obtained from the application of the algorithm on a representative sample of real images are presented.
Facultad de Informática
Materia
Ciencias Informáticas
Parallelism and concurrency
Image processing software
Computer vision
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc/3.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/9379
Acceder
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network_name_str SEDICI (UNLP)
spelling Parallel recognition and classification of objectsFelice, RodrigoRuscitti, FernandoNaiouf, MarceloDe Giusti, Armando EduardoCiencias InformáticasParallelism and concurrencyImage processing softwareComputer visionThe development of parallel algorithms for an automatic recognition and classification of objects from an industrial line (either production or packaging) is presented. This kind of problem introduces a temporal restriction on images processing, a parallel resolution being therefore required. We have chosen simple objects (fruits, eggs, etc.), which are classified according to characteristics such as shape, color, size, defects (stains, loss of color), etc. By means of this classification, objects can be sent, for example, to different sectors of the line. Algorithms parallelization on a heterogeneous computers network with a PVM (Parallel Virtual Machine) support is studied in this paper. Finally, some quantitative results obtained from the application of the algorithm on a representative sample of real images are presented.Facultad de Informática1999-03info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArticulohttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttp://sedici.unlp.edu.ar/handle/10915/9379enginfo:eu-repo/semantics/altIdentifier/url/http://journal.info.unlp.edu.ar/wp-content/uploads/2015/papers_01/ARTIC.PDFinfo:eu-repo/semantics/altIdentifier/issn/1666-6038info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc/3.0/Creative Commons Attribution-NonCommercial 3.0 Unported (CC BY-NC 3.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-29T10:50:39Zoai:sedici.unlp.edu.ar:10915/9379Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 10:50:39.661SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Parallel recognition and classification of objects
title Parallel recognition and classification of objects
spellingShingle Parallel recognition and classification of objects
Felice, Rodrigo
Ciencias Informáticas
Parallelism and concurrency
Image processing software
Computer vision
title_short Parallel recognition and classification of objects
title_full Parallel recognition and classification of objects
title_fullStr Parallel recognition and classification of objects
title_full_unstemmed Parallel recognition and classification of objects
title_sort Parallel recognition and classification of objects
dc.creator.none.fl_str_mv Felice, Rodrigo
Ruscitti, Fernando
Naiouf, Marcelo
De Giusti, Armando Eduardo
author Felice, Rodrigo
author_facet Felice, Rodrigo
Ruscitti, Fernando
Naiouf, Marcelo
De Giusti, Armando Eduardo
author_role author
author2 Ruscitti, Fernando
Naiouf, Marcelo
De Giusti, Armando Eduardo
author2_role author
author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
Parallelism and concurrency
Image processing software
Computer vision
topic Ciencias Informáticas
Parallelism and concurrency
Image processing software
Computer vision
dc.description.none.fl_txt_mv The development of parallel algorithms for an automatic recognition and classification of objects from an industrial line (either production or packaging) is presented. This kind of problem introduces a temporal restriction on images processing, a parallel resolution being therefore required. We have chosen simple objects (fruits, eggs, etc.), which are classified according to characteristics such as shape, color, size, defects (stains, loss of color), etc. By means of this classification, objects can be sent, for example, to different sectors of the line. Algorithms parallelization on a heterogeneous computers network with a PVM (Parallel Virtual Machine) support is studied in this paper. Finally, some quantitative results obtained from the application of the algorithm on a representative sample of real images are presented.
Facultad de Informática
description The development of parallel algorithms for an automatic recognition and classification of objects from an industrial line (either production or packaging) is presented. This kind of problem introduces a temporal restriction on images processing, a parallel resolution being therefore required. We have chosen simple objects (fruits, eggs, etc.), which are classified according to characteristics such as shape, color, size, defects (stains, loss of color), etc. By means of this classification, objects can be sent, for example, to different sectors of the line. Algorithms parallelization on a heterogeneous computers network with a PVM (Parallel Virtual Machine) support is studied in this paper. Finally, some quantitative results obtained from the application of the algorithm on a representative sample of real images are presented.
publishDate 1999
dc.date.none.fl_str_mv 1999-03
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/10915/9379
url http://sedici.unlp.edu.ar/handle/10915/9379
dc.language.none.fl_str_mv eng
language eng
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info:eu-repo/semantics/altIdentifier/issn/1666-6038
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc/3.0/
Creative Commons Attribution-NonCommercial 3.0 Unported (CC BY-NC 3.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc/3.0/
Creative Commons Attribution-NonCommercial 3.0 Unported (CC BY-NC 3.0)
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 13.070432

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