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dc.date.accessioned 2012-11-01T16:22:44Z
dc.date.available 2012-11-01T16:22:44Z
dc.date.issued 2000-10
dc.identifier.uri http://sedici.unlp.edu.ar/handle/10915/23456
dc.description.abstract 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 en
dc.language en es
dc.subject evolutive algorithms en
dc.subject Fractals es
dc.subject iterated function system (IFS) en
dc.title A comparison of diferent evolutive niching strategies for identifying a set of selfsimilar contractions for the IFS inverse problem en
dc.type Objeto de conferencia es
sedici.creator.person Ivanissevich, María Laura es
sedici.creator.person Cofiño, Antonio S. es
sedici.creator.person Gutiérrez, José Manuel es
sedici.description.note I Workshop de Agentes y Sistemas Inteligentes (WASI) es
sedici.subject.materias Ciencias Informáticas es
sedici.description.fulltext true es
mods.originInfo.place Red de Universidades con Carreras en Informática (RedUNCI) es
sedici.subtype Objeto de conferencia es
sedici.rights.license Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)
sedici.rights.uri http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
sedici.date.exposure 2000-10
sedici.relation.event VI Congreso Argentino de Ciencias de la Computación es
sedici.description.peerReview peer-review es


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Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5) Excepto donde se diga explícitamente, este item se publica bajo la siguiente licencia Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)