Concept

Matching pursuit

Related publications (175)

Low bit-rate compression of omnidirectional images

Omnidirectional images represent a special type of images that are captured by vision sensors with a 360-degree field of view. This work targets the compression of such images by taking into account their particular geometry. We first map omnidirectional i ...

2009

Spread spectrum for imaging techniques in radio interferometry

Pierre Vandergheynst, Yves Wiaux, Gilles Puy

We consider the probe of astrophysical signals through radio interferometers with small field of view and baselines with non-negligible and constant component in the pointing direction. In this context, the visibilities measured essentially identify with a ...

Wiley-Blackwell2009

Spread spectrum for imaging techniques in radio interferometry and strings detection

Yves Wiaux, Gilles Puy

This poster is a summary of recent work published in: Spread spectrum for imaging techniques in radio interferometry, Y. Wiaux, G. Puy, Y. Boursier, and P. Vandergheynst, Mon. Not. R. Astron. Soc., 2009, Preprint arXiv:0907.0944v1. We consider the probe of ...

2009

Near-Optimal Bayesian Localization via Incoherence and Sparsity

Volkan Cevher

This paper exploits recent developments in sparse approximation and compressive sensing to efficiently perform localization in a sensor network. We introduce a Bayesian framework for the localization problem and provide sparse approximations to its optimal ...

2009

Recovery of clustered sparse signals from compressive measurements

Volkan Cevher

We introduce a new signal model, called (K,C)-sparse, to capture K-sparse signals in N dimensions whose nonzero coefficients are contained within at most C clusters, with C < K < N. In contrast to the existing work in the sparse approximation and compress ...

2009

Greedy dictionary selection for sparse representation

Volkan Cevher

We develop an efficient learning framework to construct signal dictionaries for sparse representation by selecting the dictionary columns from multiple candidate bases. By sparse, we mean that only a few dictionary elements, compared to the ambient signal ...

2009

CMOS Compressed Imaging by Random Convolution

Pierre Vandergheynst, Alexandre Schmid, Yusuf Leblebici, Laurent Jacques, Vahid Majidzadeh Bafar

We present a CMOS imager with built-in capability to perform Compressed Sensing coding by Random Convolution. It is achieved by a shift register set in a pseudo-random configuration. It acts as a convolutive filter on the imager focal plane, the current is ...

2009

Compressive sensing recovery of spike trains using a structured sparsity model

Volkan Cevher

The theory of Compressive Sensing (CS) exploits a well-known concept used in signal compression – sparsity – to design new, efficient techniques for signal acquisition. CS theory states that for a length-N signal x with sparsity level K, M = O(K log(N/K)) ...

2009

CMOS Compressed Imaging by Random Convolutions

Pierre Vandergheynst, Alexandre Schmid, Yusuf Leblebici, Laurent Jacques, Vahid Majidzadeh Bafar

IEEE Service Center, 445 Hoes Lane, Po Box 1331, Piscataway, Nj 08855-1331 Usa2009

Dictionary preconditioning for greedy algorithms

Pierre Vandergheynst, Karin Schnass

This article presents an alteration of greedy algorithms like thresholding or (Orthogonal) Matching Pursuit which improves their performance in finding sparse signal representations in redundant dictionaries. These algorithms can be split into a sensing an ...

2008

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