Séminaires I2M

Sparse dictionary learning in the presence of noise and outliers.\nBy Rémi Gribonval\, INRIA Rennes - Bretagne Atlantique.\n\nA popular approach within the signal processing and machine [...]

Estimation of frequency modulations on wideband signals\, applications to audio signal analysis\n\nBy Harold Omer\, LATP.\n\nAbstract:\nThe problem of joint estimation of power spectrum and modulation from [...]

Pédagogies innovantes actives - retour d'expérience et discussion\nPar Bertrand David\, Télécom ParisTech.\n\nDownload slides\n\nAprès de plusieurs réformes de son cycle master (2A et 3A)\, Telecom Paristech [...]

A Markov point process for Fiber extraction\nBy Claire Coiffard\, IRMA\, Unistra.\n\n\nAbstract:\nThe aim of this work is to extract fibers in an image. We use a [...]

Social Sparsity: application to audio inpainting.\n\nBy Matthieu Kowalski\, L2S.\n\nAbstract:\nAudio inpainting problem is under consideration\, using iterative\nthresholding algorithms build on the "social sparsity" principle.\nFirst\, we present [...]

Bayesian approaches via sparsity enforcing priors for acoustic source imaging with robustness, super resolution and wide dynamic range. By Ning Chu, L2S. Acoustic imaging is [...]

Dictionary learning and application to EEG\n\nBy Quentin Barthelemy, CEA. This presentation addresses the issue of representing electroencephalographic (EEG) signals in an efficient way. While classical [...]

Harmonic analysis on manifolds: recent applications in neuroimaging data By Julien Lefèvre, LSIS. This talk aims at introducing some mathematical tools of harmonic analysis on [...]

By Bob Sturm, Aalborg University Copenhagen. Abstract coming soon.

On the article "PhaseLift: Exact and Stable Signal Recovery from Magnitude Measurements via Convex Programming", Emmanuel J. Candès, Thomas Strohmer and Vladislav Voroninski see the [...]

By Michael Unser\, EPFL.\n\nTutorial: Sparse stochastic processes and biomedical image reconstruction\n\nSparse stochastic processes are continuous-domain processes that admit a parsimonious representation in some matched wavelet-like [...]

Transformation-invariant analysis of visual signals with manifold models By Elif Vural, EPFL. Manifold models provide low-dimensional representations that are useful for processing and analyzing visual [...]

Subspaces methods in passive radar.\nBy Ghislain Gassier\, IM2NP.\nA bistatic passive Doppler radar system exploits already existing RF transmitters (illuminators of opportunity) to detect and localize [...]

Title: Cramer-Rao Bound and application to radar and optical polarimetry. By Philippe Réfregier, Institut Fresnel. During this talk, we will try to illustrate estimation precision [...]

Fully unsupervised detection and clustering of EEG epileptic spikes. By Johann Marchi, INS. Nowadays, large amounts of electroencephalogram (EEG) data remain unexploited because of a [...]

Online confusion learning and passive-aggressive scheme By Liva Ralaivola, LIF. This work provides the first — to the best of our knowledge — analysis of [...]

A Well-founded PAC-Bayesian Majority Vote applied to the Nearest Neighbor Rule\n\nBy Emilie Morvant\, LIF.\n\nThe Nearest Neighbor (NN) [1] rule is probably the best-known classification method. [...]

13h30 Optimization of High Dimensional Functions: Application to a Pulse Shaping Problem, Mattias Gybels, LIF. 14h Nonlinear functional data analysis with reproducing kernels, Hachem Kadri, [...]

A multicomponent proximal algorithm for Empirical Mode Decomposition. By Nelly Pustelnik, ENS Lyon The Empirical Mode Decomposition (EMD) is known to be a powerful tool [...]

Learning from structured fMRI patterns using graph kernels. (by Sylvain Takerkart, LIF). Classification of medical images in multi-subjects settings is a difficult challenge due to [...]