Sylvain Lesage

Sylvain Lesage
PQ

PhD

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13
Publications
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558
Citations

Publications

Publications (13)
Article
Full-text available
Shift-invariant dictionaries are generated by taking all the possible shifts of a few short patterns. They are helpful to represent long signals where the same pattern can appear several times at different positions. We present an algorithm that learns shift invariant dictionaries from long training signals. This algorithm is an extension of K-SVD....
Article
Full-text available
Real-world phenomena involve complex interactions between multiple signal modalities. As a consequence, humans are used to integrate at each instant perceptions from all their senses in order to enrich their understanding of the surrounding world. This paradigm can be also extremely useful in many signal processing and computer vision problems invo...
Article
Full-text available
Les décompositions parcimonieuses décrivent un signal comme une combinaison d'un petit nombre de formes de base, appelées atomes. Le dictionnaire d'atomes, crucial pour l'efficacité de la décomposition, peut résulter d'un choix a priori (ondelettes, Gabor, ...) qui fixe la structure du dictionnaire, ou d'un apprentissage à partir d'exemples représe...
Conference Paper
Full-text available
This paper presents a methodology for extracting meaningful synchronous structures from multi-modal signals. Simultaneous processing of multi-modal data can reveal information that is unavailable when handling the sources separately. However, in natural high-dimensional data, the statistical dependencies between modalities are, most of the time, no...
Conference Paper
Full-text available
The performance of approximation using redundant expansions rely on having dictionaries adapted to the signals. In natural high-dimensional data, the statistical dependencies are, most of the time, not obvious. Learning fundamental patterns is an alternative to analytical design of bases and is nowadays a popular problem in the field of approximati...
Article
Full-text available
In this survey, we highlight the appealing features and challenges of Sparse Component Analysis (SCA) for blind source separation (BSS). SCA is a simple yet powerful framework to separate several sources from few sensors, even when the independence assumption is dropped. So far, SCA has been most successfully applied when the sources can be represe...
Conference Paper
This paper focuses on under-determined source separation when the mixing parameters are known. The approach is based on a sparse decomposition of the mixture. In the proposed method, the mixture is decomposed with Matching Pursuit by introducing a new class of multi-channel dictionaries, where the atoms are given by a spatial direction and a wavefo...
Article
Full-text available
The performances of approximation using redundant expansions rely on having dictionaries adapted to the signals. In natural high-dimensional data, the statistical dependencies are, most of the time, not obvious. Learning fundamental patterns is an alternative to analytical design of bases and is nowadays a popular problem in the field of approximat...
Article
Full-text available
Cet article traite de la séparation de sources dans le cas sous-déterminé quand la matrice de mélange est connue. On se place dans le cadre des approches basées sur la décomposition parcimonieuse du mélange. Dans la nouvelle méthode proposée, on décompose le mélange par Matching Pursuit en introduisant une nouvelle classe de dictionnaires multi-can...
Conference Paper
Full-text available
We propose a new method to learn overcomplete dictionaries for sparse coding structured as unions of orthonormal bases. The interest of such a structure is manifold. Indeed, it seems that many signals or images can be modeled as the superimposition of several layers with sparse decompositions in as many bases. Moreover, in such dictionaries, the ef...
Article
Full-text available
Sparse approximation using redundant dictionaries is an efficient tool for many applications in the field of signal processing. The performances largely depend on the adaptation of the dictionary to the signal to decompose. As the statistical dependencies are most of the time not obvious in natural high-dimensional data, learning fundamental patter...

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