Unsupervised Segmentation of Randomly Switching Data Hidden With Non-Gaussian Correlated Noise - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue Signal Processing Année : 2011

Unsupervised Segmentation of Randomly Switching Data Hidden With Non-Gaussian Correlated Noise

Pierre Lanchantin
  • Fonction : Auteur
  • PersonId : 919059
Wojciech Pieczynski
  • Fonction : Auteur
  • PersonId : 861861

Résumé

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.

Mots clés

NA
Fichier non déposé

Dates et versions

hal-01106545 , version 1 (20-01-2015)

Identifiants

  • HAL Id : hal-01106545 , version 1

Citer

Pierre Lanchantin, Jérôme Lapuyade-Lahorgue, Wojciech Pieczynski. Unsupervised Segmentation of Randomly Switching Data Hidden With Non-Gaussian Correlated Noise : Hidden Markov chains, Triplet Markov Chains, Copulas, non-Gaussian correlated noise. Signal Processing, 2011, 91 (2). ⟨hal-01106545⟩
76 Consultations
0 Téléchargements

Partager

Gmail Facebook X LinkedIn More