709 citations · 4.7k across the 56 of their papers we have counts for
13 papers · 1 filter
EEG reconstruction and skull conductivity estimation using a Bayesian model promoting structured sparsity
Facundo Costa, Hadj Batatia, Thomas Oberlin +1
M/EEG source localization is an open research issue. To solve it, it is important to have good knowledge of several physical parameters to build a reliable head operator. Amongst t…
Bayesian Structured Sparsity Priors for EEG Source Localization Technical Report
Facundo Costa, Hadj Batatia, Thomas Oberlin +1
This report introduces a new hierarchical Bayesian model for the EEG source localization problem. This model promotes structured sparsity to search for focal brain activity. This s…
Hyperspectral unmixing with spectral variability using a perturbed linear mixing model
Pierre-Antoine Thouvenin, Nicolas Dobigeon, Jean-Yves Tourneret
Given a mixed hyperspectral data set, linear unmixing aims at estimating the reference spectral signatures composing the data - referred to as endmembers - their abundance fraction…
Unsupervised Unmixing of Hyperspectral Images Accounting for Endmember Variability
Abderrahim Halimi, Nicolas Dobigeon, Jean-Yves Tourneret
This paper presents an unsupervised Bayesian algorithm for hyperspectral image unmixing accounting for endmember variability. The pixels are modeled by a linear combination of endm…
A Hamiltonian Monte Carlo Method for Non-Smooth Energy Sampling
Lotfi Chaari, Jean-Yves Tourneret, Caroline Chaux +1
Efficient sampling from high-dimensional distributions is a challenging issue which is encountered in many large data recovery problems involving Markov chain Monte Carlo schemes.…
Joint Bayesian estimation of close subspaces from noisy measurements
Olivier Besson, Nicolas Dobigeon, Jean-Yves Tourneret
In this letter, we consider two sets of observations defined as subspace signals embedded in noise and we wish to analyze the distance between these two subspaces. The latter entai…
Residual component analysis of hyperspectral images -- Application to joint nonlinear unmixing and nonlinearity detection
Yoann Altmann, Nicolas Dobigeon, Steve McLaughlin +1
This paper presents a nonlinear mixing model for joint hyperspectral image unmixing and nonlinearity detection. The proposed model assumes that the pixel reflectances are linear co…
Unsupervised Post-Nonlinear Unmixing of Hyperspectral Images Using a Hamiltonian Monte Carlo Algorithm
Yoann Altmann, Nicolas Dobigeon, Jean-Yves Tourneret
This paper presents a nonlinear mixing model for hyperspectral image unmixing. The proposed model assumes that the pixel reflectances are post-nonlinear functions of unknown pure s…
Minimum mean square distance estimation of a subspace
Olivier Besson, Nicolas Dobigeon, Jean-Yves Tourneret
We consider the problem of subspace estimation in a Bayesian setting. Since we are operating in the Grassmann manifold, the usual approach which consists of minimizing the mean squ…
Enhancing hyperspectral image unmixing with spatial correlations
Olivier Eches, Nicolas Dobigeon, Jean-Yves Tourneret
This paper describes a new algorithm for hyperspectral image unmixing. Most of the unmixing algorithms proposed in the literature do not take into account the possible spatial corr…
A Hierarchical Bayesian Model for Frame Representation
L. Chaâri, J. -C. Pesquet, J. -Y. Tourneret +2
In many signal processing problems, it may be fruitful to represent the signal under study in a frame. If a probabilistic approach is adopted, it becomes then necessary to estimate…
Bayesian orthogonal component analysis for sparse representation
Nicolas Dobigeon, Jean-Yves Tourneret
This paper addresses the problem of identifying a lower dimensional space where observed data can be sparsely represented. This under-complete dictionary learning task can be formu…
Bayesian separation of spectral sources under non-negativity and full additivity constraints
Nicolas Dobigeon, Said Moussaoui, Jean-Yves Tourneret +1
This paper addresses the problem of separating spectral sources which are linearly mixed with unknown proportions. The main difficulty of the problem is to ensure the full additivi…