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A Bayesian Approach to Video Expansions on Parametric Over-Complete 2-D Dictionaries

Publications associées (41)

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Convexity in source separation: Models, geometry, and algorithms

Volkan Cevher, Afsaneh Asaei, Quoc Tran Dinh, Luca Baldassarre

Source separation, or demixing, is the process of extracting multiple components entangled within a signal. Contemporary signal processing presents a host of difficult source separation problems, from interference cancellation to background subtraction, bl ...

2013

Learning of Structured Graph Dictionaries

Pascal Frossard, Xiaowen Dong, Xue Zhang

We propose a method for learning dictionaries towards sparse approximation of signals defined on vertices of arbitrary graphs. Dictionaries are expected to describe effectively the main spatial and spectral components of the signals of interest, so that th ...

2012

Dictionary learning in stereo imaging

Pascal Frossard, Ivana Tosic

This paper presents a new method for learning overcomplete dictionaries adapted to efficient joint representation of stereo images. We first formulate a sparse stereo image model where the multi-view correlation is described by local geometric transforms o ...

Institute of Electrical and Electronics Engineers2011

Bayesian Rendezvous for Distributed Robotic Systems

Alcherio Martinoli, Sven Adrian Gowal

In this paper, we state, using thorough mathe- matical analysis, sufficient conditions to perform a rendezvous maneuver with a group of differential-wheeled robots endowed with an on-board, noisy, local positioning system. In particular, we extend the exis ...

Ieee Service Center, 445 Hoes Lane, Po Box 1331, Piscataway, Nj 08855-1331 Usa2011

A New Hybrid Bayesian-Variational Particle Filter With Application To Mitotic Cell Tracking

Michaël Unser, Ricard Delgado Gonzalo

Tracking algorithms are traditionally based on either a variational approach or a Bayesian one. In the variational case, a cost function is established between two consecutive frames and minimized by standard optimization algorithms. In the Bayesian case, ...

Ieee Service Center, 445 Hoes Lane, Po Box 1331, Piscataway, Nj 08855-1331 Usa2011

A New Hybrid Bayesian-Variational Particle Filter with Application to Mitotic Cell Tracking

Michaël Unser, Ricard Delgado Gonzalo

IEEE2011

Simple Methods for Evaluating and Comparing Binary Experiments

Thomas Alois Weber

We consider a confidence parametrization of binary information sources in terms of appropriate likelihood ratios. This parametrization is used for Bayesian belief updates and for the equivalent comparison of binary experiments. In contrast to the standard ...

2010

A union of incoherent spaces model for classification

Pierre Vandergheynst, Karin Schnass

We present a new and computationally efficient scheme for classifying signals into a fixed number of known classes. We model classes as subspaces in which the corresponding data is well represented by a dictionary of features. In order to ensure low miscla ...

Ieee Service Center, 445 Hoes Lane, Po Box 1331, Piscataway, Nj 08855-1331 Usa2010

Dictionary Identification - Sparse Matrix-Factorisation via $\ell_1$-Minimisation

Rémi Gribonval, Karin Schnass

This article treats the problem of learning a dictionary providing sparse representations for a given signal class, via

\ell_1

-minimisation. The problem can also be seen as factorising a

\ddim \times \nsig

matrix $Y=(y_1 \ldots y_\nsig), , y_n\in \R^\ ...

Institute of Electrical and Electronics Engineers2010

Multichannel thresholding with sensing dictionaries

Pierre Vandergheynst, Rémi Gribonval, Karin Schnass

This paper shows introduces the use sensing dictionaries for p-thresholding, an algorithm to compute simultaneous sparse approximations of multichannel signals over redundant dictionaries. We do both a worst case and average case recovery analyses of this ...

2007