Patrice Brault

Learn More
The segmentation of an image can be presented as an inverse ill-posed problem. The segmentation problem is presented as, knowing an observed image g, how to obtain an original image f in which a classification in statistically homogeneous regions must be established. The inversion technique we use is done in a Bayesian probabilistic framework. Prior(More)
This paper describes a new fully unsupervised image segmentation method based on a Bayesian approach and a Potts-Markov Random Field (PMRF) model that are performed in the wavelet domain. A Bayesian segmentation model, based on a PMRF in the direct domain, has already been successfully developed and tested in [23, 12]. This model performs a fully(More)
  • P. Brault
  • 2003
This work presents a new scheme for hybrid video compression. It is also aimed at showing the applicability of the scheme to scene analysis. The originality of this contribution is first to use spatio-temporal wavelet families tuned to motion. In this sense it differs from approaches based on motion unwarping then filtering with traditional wavelets. Here(More)
— In this paper, a method for enhancing low contrast images is proposed. This method, called Gaussian Mixture Model based Contrast Enhancement (GMMCE), brings into play the Gaussian mixture modeling of histograms to model the content of the images. Based on the fact that each homogeneous area in natural images has a Gaussian-shaped histogram, it decomposes(More)
Prediction methods by template matching are often mentioned to improve video coding efficiency. They are based on a Markovian model to find the most similar patterns of texture in previously encoded information. These kinds of methods are more efficient than H.264/AVC intra prediction modes in many cases, such as complex texture coding. However, the(More)
This paper presents an image, then a video, compression scheme based on a restoration process applied to a transform domain. Most of the method relies on the ability to suppress, then automatically restore some transformed coefficients in a coding scheme. We use a total variation (TV) minimization model in order to voluntarily predict canceled coefficients.(More)
We describe a new fully unsupervised image segmenta-tion method based on a Bayesian approach and a Potts-Markov random field (PMRF) model that are performed in the wavelet domain. A Bayesian segmentation model, based on a PMRF in the direct domain, has already been successfully developed and tested. This model performs a fully unsupervised segmentation, on(More)
Partie I : Estimation de mouvement par ondelettes spatio-temporelles adaptées au mouvement Partie II : Segmentation et estimation de mouvement par modèles de Markov cachés et approche bayésienne dans les domaines direct et ondelette. (Version préliminaire, approuvée pour soutenance et reproduction) Soutenue le 29 novembre 2005 devant les membres du jury : A(More)