I am pleased to announce that the following paper has been accepted to the Mirage 2007 (http://acivs.org/mirage2007/) an international conference on Computer Vision / Computer Graphics Collaboration Techniques and Applications. J. Darbon. A Note on the Dicerete Binary Mumford-Shah Model. This paper is concerned itself with the analysis of the twophase Mumford-Shah model also known as the active contour without edges model introduced by Chan and Vese. It consists of approximating an observed image by a piecewise constant image which can take only two values. First we show that this model with the L1-norm as data fidelity yields a contrast invariant filter which is a well known property of morphological filters. Then we consider a discrete version of the original problem. We show that an inclusion property holds for the minimizers. The latter is used to design an efficient graph-cut based algorithm which computes an exact minimizer. Some preliminary results are presented *************************************************************************** I am also pleased to announce that the following twin papers have been published in the Journal of Mathmematical Imaging and Vision (http://www.springerlink.com/content/u8388810354q/?p=e3972a8e80d5433daf4bc20…) J. Darbon and M. Sigelle Image Restoration with Discrete Constrained Total Variation Part I: Fast and Exact Optimization Journal of Mathematical Imaging and Vision. Journal of Mathematical Imaging and Vision. Vol 26 n.3, pp. 277-291, December 2006. This paper deals with the total variation minimization problem in image restoration for convex data fidelity functionals. We propose a new and fast algorithm which computes an exact solution in the discrete framework. Our method relies on the decomposition of an image into its level sets. It maps the original problems into independent binary Markov Random Field optimization problems at each level. Exact solutions of these binary problems are found thanks to minimum cost cut techniques in graphs. These binary solutions are proved to be monotone increasing with levels and yield thus an exact solution of the discrete original problem. Furthermore we show that minimization of total variation under L1 data fidelity term yields a self-dual contrast invariant filter. Finally we present some results. J. Darbon and M. Sigelle Image Restoration with Discrete Constrained Total Variation Part II: Levelable Functions, Convex and Non-Convex Cases In Part II of this paper we extend the results obtained in Part I for total variation minimization in image restoration towards the following directions: first we investigate the decomposability property of energies on levels, which leads us to introduce the concept of levelable regularization functions (which TV is the paradigm of). We show that convex levelable posterior energies can be minimized exactly using the level-independant cut optimization scheme seen in Part I. Next we extend this graph cut scheme to the case of non-convex levelable energies.We present convincing restoration results for images corrupted with impulsive noise. We also provide a minimum-cost based algorithm which computes a global minimizer for Markov Random Field with convex priors. Last we show that non-levelable models with convex local conditional posterior energies such as the class of generalized Gaussian models can be exactly minimized with a generalized coupled Simulated Annealing. ---------------------------------------------------------------- This message was sent using IMP, the Internet Messaging Program.