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- Cornelia Paula Vacar, Jean-François Giovannelli, Yannick Berthoumieu
- 2011 IEEE International Conference on Acoustics…
- 2011

We have studied two efficient sampling methods, Langevin and Hessian adapted Metropolis Hastings (MH), applied to a parameter estimation problem of the mathematical model (Lorentzian, Laplacian, Gaussian) that describes the Power Spectral Density (PSD) of a texture. The novelty brought by this paper consists in the exploration of textured images modeled by… (More)

- Cornelia Paula Vacar, Jean-François Giovannelli, Yannick Berthoumieu
- IEEE Signal Processing Letters
- 2014

This letter addresses an estimation problem based on blurred and noisy observations of textured images. The goal is jointly estimating the 1) image model parameters, 2) parametric point spread function (semi-blind deconvolution) and 3) signal and noise levels. It is an intricate problem due to the data model non-linearity w.r.t. these parameters. We resort… (More)

- Cornelia Paula Vacar, J. Giovannelli, A. Roman
- 2012 19th IEEE International Conference on Image…
- 2012

The paper presents a model selection method for texture images, more specifically, it finds the most adequate model for the pixels' interaction. This approach relies on a Bayesian framework, that probabilities all the quantities and determines the joint a posteriori law for the models and the parameters. In order to compute the a posteriori model… (More)

- Roxana-Gabriela Rosu, Jean-François Giovannelli, Audrey Giremus, Cornelia Paula Vacar
- 2015 IEEE International Conference on Acoustics…
- 2015

The paper presents a method for estimating the parameter of a Potts model jointly with the unknowns of an image segmentation problem. The method addresses piecewise constant images degraded by additive noise. The proposed solution follows a Bayesian approach, that yields the posterior law for all the unknowns (labels, gray levels, noise level and Potts… (More)

- Cornelia Paula Vacar, Farida Cheriet
- 2014 IEEE International Conference on Image…
- 2014

The optical flow estimation is addressed in the context of video sequences, where temporal information can be exploited to increase the accuracy and the convergence speed of the algorithm. This paper presents an unsupervised optical flow algorithm based on robust Student's t data and regularization terms, which automatically tunes the relative weight of the… (More)

- Cornelia Paula Vacar, Jean-François Giovannelli, Yannick Berthoumieu
- IEEE Transactions on Signal Processing
- 2016

A Bayesian method for texture model choice from blurred and noisy (i.e., indirect) observations is presented. The textures are modeled by stationary Random Fields, with various distribution laws, either Gaussian or Scale Mixtures of Gaussians. The power spectral densities of the fields are modeled by parametric functions and the aim is to select the most… (More)

The paper presents a model selection method for texture images, more specifically, it finds the most adequate model for the pixels’ interaction. This approach relies on a Bayesian framework, that probabilizes all the quantities and determines the joint a posteriori law for the models and the parameters. In order to compute the a posteriori model… (More)

- Jean-François Giovannelli, Cornelia Paula Vacar
- 2017 25th European Signal Processing Conference…
- 2017

The paper tackles the problem of joint deconvolution and segmentation specifically for textured images. The images are composed of patches of textures that belong to a set of K possible classes. Each class of image is described by a Gaussian random field and the classes are modelled by a Potts field. The method relies on a hierarchical model and a Bayesian… (More)

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