{"id":72594,"date":"2005-04-02T00:00:00","date_gmt":"2005-04-02T00:00:00","guid":{"rendered":"https:\/\/www.deberes.net\/tesis\/sin-categoria\/local-structure-tensor-for-multidimensional-signal-processing-applications-to-medical-image-analysis\/"},"modified":"2005-04-02T00:00:00","modified_gmt":"2005-04-02T00:00:00","slug":"local-structure-tensor-for-multidimensional-signal-processing-applications-to-medical-image-analysis","status":"publish","type":"post","link":"https:\/\/www.deberes.net\/tesis\/ciencias-tecnologicas\/local-structure-tensor-for-multidimensional-signal-processing-applications-to-medical-image-analysis\/","title":{"rendered":"Local structure tensor for multidimensional signal processing. applications to medical image analysis"},"content":{"rendered":"<h2>Tesis doctoral de <strong> Ra\u00fal San Jos\u00e9 Est\u00e9par <\/strong><\/h2>\n<p>Feature extraction and, particularly, orientation estimation of multidimensional images is of paramount importance for the image processing and computer vision communities. This dissertation focuses on this topic; specifically, we deal with the problem of local structure tensor (lst) estimation, as a mean of characterizing the local behavior of a multidimensional signal. the lst can be seen as a measure of the uncertainty of a multidimensional signal with respect to a given orientation.  lst estimation can be achieved by estimating the local energy of a signal in different orientations. Then, the lst is computed as a linear combination of the local energy for each orientation with a tensor basis whose elements are calculated for each orientation. This kind of methods for the estimation of the lst is based on quadrature filters to obtain the local energy of the signal.<\/p>\n<p>&nbsp;<\/p>\n<h3>Datos acad\u00e9micos de la tesis doctoral \u00ab<strong>Local structure tensor for multidimensional signal processing. applications to medical image analysis<\/strong>\u00ab<\/h3>\n<ul>\n<li><strong>T\u00edtulo de la tesis:<\/strong>\u00a0 Local structure tensor for multidimensional signal processing. applications to medical image analysis <\/li>\n<li><strong>Autor:<\/strong>\u00a0 Ra\u00fal San Jos\u00e9 Est\u00e9par <\/li>\n<li><strong>Universidad:<\/strong>\u00a0 Valladolid<\/li>\n<li><strong>Fecha de lectura de la tesis:<\/strong>\u00a0 04\/02\/2005<\/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>Carlos Alberola Lopez<\/li>\n<\/ul>\n<\/li>\n<li><strong>Tribunal<\/strong>\n<ul>\n<li>Presidente del tribunal: narciso Garc\u00eda santos <\/li>\n<li>philippe Thiran jean (vocal)<\/li>\n<li>hans Knutsson (vocal)<\/li>\n<li>ioannis Dimitriadis (vocal)<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Tesis doctoral de Ra\u00fal San Jos\u00e9 Est\u00e9par Feature extraction and, particularly, orientation estimation of multidimensional images is of paramount importance [&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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