Please use this identifier to cite or link to this item: http://ricaxcan.uaz.edu.mx/jspui/handle/20.500.11845/1864
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dc.contributor31249es_ES
dc.contributor20608-
dc.contributor.otherhttps://orcid.org/0000-0002-7337-8974-
dc.coverage.spatialGlobales_ES
dc.creatorGutiérrez Mata, Osvaldo-
dc.creatorDe la Rosa Vargas, José Ismael-
dc.creatorVilla Hernández, José de Jesús-
dc.creatorGonzález Elías, Efrén-
dc.creatorEscalante, Nivia-
dc.date.accessioned2020-05-02T15:19:34Z-
dc.date.available2020-05-02T15:19:34Z-
dc.date.issued2011-10-
dc.identifierinfo:eu-repo/semantics/publishedVersiones_ES
dc.identifier.urihttp://ricaxcan.uaz.edu.mx/jspui/handle/20.500.11845/1864-
dc.identifier.urihttps://doi.org/10.48779/zkxj-cb26-
dc.description.abstractIn this work, a novel model of Markov random field is presented, named Semi-Huber potential function, applied to image segmentation in presence of noise. The main difference with respect to other models that have been taken as a reference, is that the number of parameters in the proposed model is significatively smaller. The idea is to choose adequate parameter values heuristically for a good segmentation of the image. In that sense, experiment results show that the proposed model allows a faster and easier parameter adjustment with razonable computation times.es_ES
dc.language.isoenges_ES
dc.publisherCentro de Investigación en Matemáticas, A.C.es_ES
dc.relation.urigeneralPublices_ES
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 Estados Unidos de América*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/us/*
dc.sourceVIII Taller-Escuela de Procesamiento de Imágenes - CIMAT, Guanajuato, Guanajuato, Octubre de 2011 (Memorias en CD).es_ES
dc.subject.classificationINGENIERIA Y TECNOLOGIA [7]es_ES
dc.subject.otherImage segmentationes_ES
dc.subject.otherMarkov Random Fieldses_ES
dc.titleSemi-Huber potential function for image segmentationes_ES
dc.typeinfo:eu-repo/semantics/conferencePaperes_ES
Appears in Collections:*Documentos Académicos*-- M. en Ciencias del Proc. de la Info.

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