Jérôme Lapuyade-Lahorgue

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Hidden Markov chains (HMC) are a very powerful tool in hidden data restoration and are currently used to solve a wide range of problems. However, when these data are not stationary, estimating the parameters, which are required for unsupervised processing, poses a problem. Moreover, taking into account correlated non-Gaussian noise is difficult without(More)
The hidden Markov chain (HMC) model is a couple of random sequences (X,Y), in which X is an unobservable Markov chain, and Y is its observable noisy version. Classically, the distribution p(y|x) is simple enough to ensure the Markovianity of p(x|y), that enables one to use different Bayesian restoration techniques. HMC model has recently been extended to(More)
Parametric modeling and estimation of non-Gaussian multidimensional probability density function is a difficult problem whose solution is required by many applications in signal and image processing. A lot of efforts have been devoted to escape the usual Gaussian assumption by developing perturbed Gaussian models such as Spherically Invariant Random Vectors(More)
In the classical hidden Markov chain (HMC) model we have a hidden chain X , which is a Markov one and an observed chain Y . HMC are widely used; however, in some situations they have to be replaced by the more general “hidden semi-Markov chains” (HSMC), which are particular “triplet Markov chains” (TMC) ) , , ( Y U X T = , where the auxiliary chain U models(More)
The hidden Markov chain (HMC) model is a couple of random sequences ) , ( Y X , in which X is an unobservable Markov chain, and Y is its observable noisy version. Classically, the distribution ) ( x y p is simple enough to ensure the Markovianity of ) ( y x p , that enables one to use different Bayesian restoration techniques. HMC model has recently been(More)
The hidden Markov chain (HMC) model is a couple of random sequences (X,Y), in which X is an unobservable Markov chain, and Y is its observable ‘‘noisy version’’. The chain X is a Markov one and the components of Y are independent conditionally on X. Such a model can be extended in two directions: (i) X is a semi-Markov chain and (ii) the distribution of Y(More)
PURPOSE Accurate tumor delineation in positron emission tomography (PET) images is crucial in oncology. Although recent methods achieved good results, there is still room for improvement regarding tumors with complex shapes, low signal-to-noise ratio, and high levels of uptake heterogeneity. METHODS The authors developed and evaluated an original(More)
Determining the number of components in dimensionality reduction techniques is still one of the open problems of research on data analysis. These methods are often used in knowledge extraction of multivariate great dimensional data, but very often the number of components is assumed to be known. One of the classical methods to estimate this dimensionality(More)
We consider three approaches to the modeling of systems with repairable components by a multivariate stochastic on-off process. First, we discuss the Palm calculus framework for stationary processes and its power in the derivation of general formulae for joint downtime statistics in the case of statistically independent components. Second, a class of(More)
During my fellowship, I was incorporated into the image group on the “Human Motion Imitation” (HUMIM) project. This project consists in proposing a human motion model implying different mathematical techniques. Consequently, this project implies different skills in computer science, human physiology, mechanics and physics, optimisation’s problems,(More)