Recherche

Recherche simple

Recherche avancée

Panier électronique

Votre panier ne contient aucune notice

Connexion à la base

Identification

(Identifiez-vous pour accéder aux fonctions de mise à jour. Utilisez votre login-password de courrier électronique)

Entrepôt OAI-PMH

Soumettre une requête

	Consulter la notice détaillée
	Version complète en ligne
	Version complète en ligne accessible uniquement depuis l'Ircam
	Ajouter la notice au panier
	Retirer la notice du panier

English version

(full translation not yet available)

Liste complète des articles

Consultation des notices

Vue détaillée

Vue Refer

Vue Labintel

Vue BibTeX

%0 Journal Article
%A Lanchantin, Pierre
%A Lapuyade-Lahorgue, Jérôme
%A Pieczynski, Wojciech
%T Unsupervised Segmentation of Randomly Switching Data Hidden With Non-Gaussian Correlated Noise : hidden Markov chains, Triplet Markov Chains, Copulas, non-Gaussian correlated noise
%D 2011
%B Signal Processing
%V 91
%N 2
%F Lanchantin11a
%X Hidden Markov chains (HMC) are a very powerful tool in hidden data restoration and are currently used to solve a wide range of problems. However, when these data are not stationary, estimating the parameters, which are required for unsupervised processing, poses a problem. Moreover, taking into account correlated non-Gaussian noise is difficult without model approximations. The aim of this paper is to propose a simultaneous solution to both of these problems using triplet Markov chains (TMC) and copulas. The interest of the proposed models and related processing is validated by different experiments some of which are related to semi-supervised and unsupervised image segmentation.
%1 1
%2 3