Bayesian multifractal analysis of multi-temporal images using smooth priors - ENS de Lyon - École normale supérieure de Lyon Accéder directement au contenu
Communication Dans Un Congrès Année : 2016

Bayesian multifractal analysis of multi-temporal images using smooth priors

Résumé

Texture analysis can be conducted within the mathematical framework of multifractal analysis (MFA) via the study of the regularity fluctuations of image amplitudes. Successfully used in various applications, however MFA remains limited to the independent analysis of single images while, in an increasing number of applications, data are multi-temporal. The present contribution addresses this limitation and introduces a Bayesian framework that enables the joint estimation of multifractal parameters for multi-temporal images. It builds on a recently proposed Gaussian model for wavelet leaders parameterized by the multifractal attributes of interest. A joint Bayesian model is formulated by assigning a Gaussian prior to the second derivatives of time evolution of the multifractal attributes associated with multi-temporal images. This Gaussian prior ensures that the multifractal parameters have a smooth temporal evolution. The associated Bayesian estimators are then approximated using a Hamiltonian Monte-Carlo algorithm. The benefits of the proposed procedure are illustrated on synthetic data.
Fichier principal
Vignette du fichier
combrexelles_17198.pdf (597.31 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-01461698 , version 1 (13-02-2017)

Identifiants

  • HAL Id : hal-01461698 , version 1
  • OATAO : 17198

Citer

Sébastien Combrexelle, Herwig Wendt, Jean-Yves Tourneret, Patrice Abry, Stephen Mclaughlin. Bayesian multifractal analysis of multi-temporal images using smooth priors. IEEE Workshop on statistical signal processing (SSP 2016), Jun 2016, Palma de Mallorca, Spain. pp. 1-5. ⟨hal-01461698⟩
194 Consultations
95 Téléchargements

Partager

Gmail Facebook X LinkedIn More