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- Marcelo Pereyra, Nick Whiteley, Christophe Andrieu, Jean-Yves Tourneret
- 2014 IEEE Workshop on Statistical Signal…
- 2014

This paper addresses the problem of estimating the Potts-Markov random field parameter β jointly with the unknown parameters of a Bayesian image segmentation model. We propose a new adaptive Markov chain Monte Carlo (MCMC) algorithm for performing joint maximum marginal likelihood estimation of β and maximum-a-posteriori unsupervised image… (More)

- Marcelo Pereyra, Nicolas Dobigeon, Hadj Batatia, Jean-Yves Tourneret
- 2011 IEEE International Conference on Acoustics…
- 2011

This paper addresses the problem of estimating the statistical distribution of multiple-tissue non-stationary ultrasound images of skin. The distribution of multiple-tissue images is modeled as a finite mixture of Heavy-Tailed Rayleigh distributions. An original Bayesian algorithm combined with a Markov chain Monte Carlo method is then derived to jointly… (More)

- Marcelo Pereyra
- Statistics and Computing
- 2016

- Marcelo Pereyra, Nicolas Dobigeon, Hadj Batatia, Jean-Yves Tourneret
- IEEE Trans. Med. Imaging
- 2012

This paper addresses the problem of jointly estimating the statistical distribution and segmenting lesions in multiple-tissue high-frequency skin ultrasound images. The distribution of multiple-tissue images is modeled as a spatially coherent finite mixture of heavy-tailed Rayleigh distributions. Spatial coherence inherent to biological tissues is modeled… (More)

- Marcelo Pereyra, Hadj Batatia
- IEEE transactions on ultrasonics, ferroelectrics…
- 2012

Starting from the widely accepted point-scattering model, this paper establishes, through analytical developments, that ultrasound signals backscattered from skin tissues converge to a complex Levy flight random process with non- Gaussian α-stable statistics. The envelope signal follows a generalized (heavy-tailed) Rayleigh distribution. It is shown that… (More)

- Marcelo Pereyra, Nicolas Dobigeon, Hadj Batatia, Jean-Yves Tourneret
- IEEE Transactions on Image Processing
- 2013

This paper addresses the problem of estimating the Potts parameter β jointly with the unknown parameters of a Bayesian model within a Markov chain Monte Carlo (MCMC) algorithm. Standard MCMC methods cannot be applied to this problem because performing inference on β requires computing the intractable normalizing constant of the Potts model. In… (More)

- Marcelo Pereyra, José M. Bioucas-Dias, Mário A. T. Figueiredo
- 2015 23rd European Signal Processing Conference…
- 2015

This paper presents two hierarchical Bayesian methods for performing maximum-a-posteriori inference when the value of the regularisation parameter is unknown. The methods are useful for models with homogenous regularisers (i.e., prior sufficient statistics), including all norms, composite norms and compositions of norms with linear operators. A key… (More)

- Yoann Altmann, Marcelo Pereyra, Stephen McLaughlin
- IEEE Transactions on Computational Imaging
- 2015

This paper presents a new Bayesian model and algorithm for nonlinear unmixing of hyperspectral images. The proposed model represents the pixel reflectances as linear combinations of the endmembers, corrupted by nonlinear (with respect to the endmembers) terms and additive Gaussian noise. Prior knowledge about the problem is embedded in a hierarchical model… (More)

- Marcelo Pereyra, Nicolas Dobigeon, Hadj Batatia, Jean-Yves Tourneret
- 2011 19th European Signal Processing Conference
- 2011

This paper addresses the problem of jointly estimating the statistical distribution and segmenting multiple-tissue high-frequency ultrasound images. The distribution of multiple-tissue images is modeled as a spatially coherent finite mixture of heavy-tailed Rayleigh distributions. Spatial coherence inherent to biological tissues is introduced into the model… (More)

This paper addresses the problem of estimating the Potts parameter β jointly with the unknown parameters of a Bayesian model within a Markov chain Monte Carlo (MCMC) algorithm. Standard MCMC methods cannot be applied to this problem because performing inference on β requires computing the intractable normalizing constant of the Potts model. In the proposed… (More)