Patrice Brault

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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 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)
We have recently demonstrated that fully unsupervised segmentations of still images and 2D+T sequences is possible by Bayesian methods, on the basis of a Hidden Markovian Model (HMM) and a Potts-Markov Random Field (PMRF), in the pixel domain. The use of a high number of iterations to reach convergence in a segmentation where the number of segments, or(More)
  • P. Brault
  • Proceedings of the 3rd IEEE International…
  • 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)
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)
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)
We describe 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. This model performs a fully unsupervised segmentation, on(More)
Motion analysis and in particular, speed and rotation analysis, has been introduced in the 80s using the continuous wavelet transform(CWT) with Morlet wavelets. The motion-tuned WT appeared to be an efficient framework and an alternative to the optical flow (OF), the block matching (BM) or the phase difference, for the study of motion. In particular it has(More)