HOGMep: Variational Bayes and higher-order graphical models applied to joint image recovery and segmentation

Abstract : Variational Bayesian approaches have been successfully applied to image segmentation. They usually rely on a Potts model for the hidden label variables and a Gaussian assumption on pixel intensities within a given class. Such models may however be limited, especially in the case of multicomponent images. We overcome this limitation with HOGMep, a Bayesian formulation based on a higher-order graphical model (HOGM) on labels and a Multivari-ate Exponential Power (MEP) prior for intensities in a class. Then, we develop an efficient statistical estimation method to solve the associated problem. Its flexibility accommodates to a broad range of applications, demonstrated on multicomponent image segmentation and restoration.
Liste complète des métadonnées

https://hal-ifp.archives-ouvertes.fr/hal-01862840
Contributeur : Laurent Duval <>
Soumis le : lundi 27 août 2018 - 19:22:50
Dernière modification le : lundi 1 octobre 2018 - 17:00:03

Fichier

Pirayre_A_2017_p-icip_BVMCsegm...
Fichiers produits par l'(les) auteur(s)

Identifiants

Citation

Aurélie Pirayre, Yuling Zheng, Laurent Duval, Jean-Christophe Pesquet. HOGMep: Variational Bayes and higher-order graphical models applied to joint image recovery and segmentation. 2017 IEEE International Conference on Image Processing (ICIP), Sep 2017, Beijing, China. IEEE, 2018, 〈10.1109/ICIP.2017.8296988〉. 〈hal-01862840〉

Partager

Métriques

Consultations de la notice

127

Téléchargements de fichiers

15