A comparison of diferent evolutive niching strategies for identifying a set of selfsimilar contractions for the IFS inverse problem

Autores
Ivanissevich, María Laura; Cofiño, Antonio S.; Gutiérrez, José Manuel
Año de publicación
2000
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
The key problem in fractal image compression is that of obtaining the IFS code (a set of linear transformations) which approximates a given image with a certain prescribed accuracy (inverse IFS problem). In this paper, we analyze and compare the performance of sharing and crowding niching techniques for identifying optimal selfsimilar transformations likely to represent a selfsimilar area within the image. The best results are found using the deterministic crowding method. We also present an interactive Matlab program implementing the algorithms described in the paper
I Workshop de Agentes y Sistemas Inteligentes (WASI)
Red de Universidades con Carreras en Informática (RedUNCI)
Materia
Ciencias Informáticas
evolutive algorithms
iterated function system (IFS)
Fractals
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:10915/23456
Acceder
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network_name_str SEDICI (UNLP)
spelling A comparison of diferent evolutive niching strategies for identifying a set of selfsimilar contractions for the IFS inverse problemIvanissevich, María LauraCofiño, Antonio S.Gutiérrez, José ManuelCiencias Informáticasevolutive algorithmsiterated function system (IFS)FractalsThe key problem in fractal image compression is that of obtaining the IFS code (a set of linear transformations) which approximates a given image with a certain prescribed accuracy (inverse IFS problem). In this paper, we analyze and compare the performance of sharing and crowding niching techniques for identifying optimal selfsimilar transformations likely to represent a selfsimilar area within the image. The best results are found using the deterministic crowding method. We also present an interactive Matlab program implementing the algorithms described in the paperI Workshop de Agentes y Sistemas Inteligentes (WASI)Red de Universidades con Carreras en Informática (RedUNCI)2000-10info: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/10915/23456enginfo: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:UNLP2025-09-29T10:55:26Zoai:sedici.unlp.edu.ar:10915/23456Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 10:55:26.867SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv A comparison of diferent evolutive niching strategies for identifying a set of selfsimilar contractions for the IFS inverse problem
title A comparison of diferent evolutive niching strategies for identifying a set of selfsimilar contractions for the IFS inverse problem
spellingShingle A comparison of diferent evolutive niching strategies for identifying a set of selfsimilar contractions for the IFS inverse problem
Ivanissevich, María Laura
Ciencias Informáticas
evolutive algorithms
iterated function system (IFS)
Fractals
title_short A comparison of diferent evolutive niching strategies for identifying a set of selfsimilar contractions for the IFS inverse problem
title_full A comparison of diferent evolutive niching strategies for identifying a set of selfsimilar contractions for the IFS inverse problem
title_fullStr A comparison of diferent evolutive niching strategies for identifying a set of selfsimilar contractions for the IFS inverse problem
title_full_unstemmed A comparison of diferent evolutive niching strategies for identifying a set of selfsimilar contractions for the IFS inverse problem
title_sort A comparison of diferent evolutive niching strategies for identifying a set of selfsimilar contractions for the IFS inverse problem
dc.creator.none.fl_str_mv Ivanissevich, María Laura
Cofiño, Antonio S.
Gutiérrez, José Manuel
author Ivanissevich, María Laura
author_facet Ivanissevich, María Laura
Cofiño, Antonio S.
Gutiérrez, José Manuel
author_role author
author2 Cofiño, Antonio S.
Gutiérrez, José Manuel
author2_role author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
evolutive algorithms
iterated function system (IFS)
Fractals
topic Ciencias Informáticas
evolutive algorithms
iterated function system (IFS)
Fractals
dc.description.none.fl_txt_mv The key problem in fractal image compression is that of obtaining the IFS code (a set of linear transformations) which approximates a given image with a certain prescribed accuracy (inverse IFS problem). In this paper, we analyze and compare the performance of sharing and crowding niching techniques for identifying optimal selfsimilar transformations likely to represent a selfsimilar area within the image. The best results are found using the deterministic crowding method. We also present an interactive Matlab program implementing the algorithms described in the paper
I Workshop de Agentes y Sistemas Inteligentes (WASI)
Red de Universidades con Carreras en Informática (RedUNCI)
description The key problem in fractal image compression is that of obtaining the IFS code (a set of linear transformations) which approximates a given image with a certain prescribed accuracy (inverse IFS problem). In this paper, we analyze and compare the performance of sharing and crowding niching techniques for identifying optimal selfsimilar transformations likely to represent a selfsimilar area within the image. The best results are found using the deterministic crowding method. We also present an interactive Matlab program implementing the algorithms described in the paper
publishDate 2000
dc.date.none.fl_str_mv 2000-10
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
info:eu-repo/semantics/publishedVersion
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dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/23456
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dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
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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)
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instname_str Universidad Nacional de La Plata
instacron_str UNLP
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repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
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