Cornelia Paula Vacar

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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)
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)
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)
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)
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)
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)
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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