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Conference papers
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.
Cited literature [31 references]
https://hal-ifp.archives-ouvertes.fr/hal-01862840
Contributor : Laurent Duval <>
Submitted on : Monday, August 27, 2018 - 7:22:50 PM
Last modification on : Wednesday, April 8, 2020 - 3:26:54 PM
Document(s) archivé(s) le : Wednesday, November 28, 2018 - 4:38:45 PM
Contributor : Laurent Duval <>
Submitted on : Monday, August 27, 2018 - 7:22:50 PM
Last modification on : Wednesday, April 8, 2020 - 3:26:54 PM
Document(s) archivé(s) le : Wednesday, November 28, 2018 - 4:38:45 PM
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