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

Aurélie Pirayre 1 Yuling Zheng 2 Laurent Duval 1 Jean-Christophe Pesquet 3, 4, 5
1 IFPEN - IFP Energies nouvelles
2 CRL - IBM-China Research Laboratory
3 LIGM - Laboratoire d'Informatique Gaspard-Monge
4 CVN - Centre de vision numérique
5 GALEN - Organ Modeling through Extraction, Representation and Understanding of Medical Image Content
Inria Saclay - Ile de France, Ecole Centrale Paris
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.
Keywords : higher-order graphical models variational Bayes multicomponent images segmentation restoration
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Conference papers
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IFP | ENPC | TDS-MACS | INRIA2017 | INSMI | UPEC-UPEM | INRIA | UNIV-PARIS-SACLAY | PARISTECH | CENTRALESUPELEC | CVN | LIGM_SIGNAL | LIGM

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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. ⟨10.1109/ICIP.2017.8296988⟩. ⟨hal-01862840⟩

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