{"id":5464,"date":"1995-01-01T00:00:00","date_gmt":"1995-01-01T00:00:00","guid":{"rendered":"https:\/\/www.deberes.net\/tesis\/1995\/01\/01\/campo-aleatorio-de-markov-con-vecindad-funcional-modelo-generalizado-de-texturas\/"},"modified":"1995-01-01T00:00:00","modified_gmt":"1995-01-01T00:00:00","slug":"campo-aleatorio-de-markov-con-vecindad-funcional-modelo-generalizado-de-texturas","status":"publish","type":"post","link":"https:\/\/www.deberes.net\/tesis\/matematicas\/campo-aleatorio-de-markov-con-vecindad-funcional-modelo-generalizado-de-texturas\/","title":{"rendered":"Campo aleatorio de markov con vecindad funcional: modelo generalizado de texturas."},"content":{"rendered":"<h2>Tesis doctoral de <strong> Antonio Mosquera Gonzalez <\/strong><\/h2>\n<p>En este trabajo se plantea un modelo estocastico de texturas en base a un campo aleatorio de markov con un nuevo sistema de vecinos no estandar, denominado sistema de vecindad funcional, que permite modelar y sintetizar tanto micro como macrotexturas en una misma escala de resolucion. Se implementa un proceso de sintesis que permite visualizar representaciones del modelo de campo aleatorio de markov con vecindad funcional considerado.  se desarrollo un procedimiento de estimacion de los parametros del modelo a partir de muestras de textura.  mediante problemas de clasificacion de texturas se muestra la validez del modelo para caracterizar muestras homogeneas de micro y macrotexturas.  por otra parte, se aborda el problema de la segmentacion de imagenes de la textura. Este se plantea como un problema de optimizacion de una funcion que integra informacion estadistica de clases en el espacio de medida con informacion espacial sobre la distribucion de los pixels en el espacio imagen.  para realizar la segmentacion se implementan dos esquemas computacionales basados en redes neuronales artificiales.<\/p>\n<p>&nbsp;<\/p>\n<h3>Datos acad\u00e9micos de la tesis doctoral \u00ab<strong>Campo aleatorio de markov con vecindad funcional: modelo generalizado de texturas.<\/strong>\u00ab<\/h3>\n<ul>\n<li><strong>T\u00edtulo de la tesis:<\/strong>\u00a0 Campo aleatorio de markov con vecindad funcional: modelo generalizado de texturas. <\/li>\n<li><strong>Autor:<\/strong>\u00a0 Antonio Mosquera Gonzalez <\/li>\n<li><strong>Universidad:<\/strong>\u00a0 Santiago de compostela<\/li>\n<li><strong>Fecha de lectura de la tesis:<\/strong>\u00a0 01\/01\/1995<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<h3>Direcci\u00f3n y tribunal<\/h3>\n<ul>\n<li><strong>Director de la tesis<\/strong>\n<ul>\n<li>Diego Cabello Ferrer<\/li>\n<\/ul>\n<\/li>\n<li><strong>Tribunal<\/strong>\n<ul>\n<li>Presidente del tribunal: Jose Mira Mira <\/li>\n<li>Ram\u00f3n Ruiz Merino (vocal)<\/li>\n<li>Santiago Candela Sola (vocal)<\/li>\n<li>Roque Mar\u00edn Morales (vocal)<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Tesis doctoral de Antonio Mosquera Gonzalez En este trabajo se plantea un modelo estocastico de texturas en base a un [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center 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