SPARS'05
November, 16-18, 2005
Rennes, France
16-18 novembre 2005


TECHNICAL SESSIONS
SESSIONS TECHNIQUES


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Wednesday, November 16, 2005
Technical Session 1
Data-driven dictionaries
Mercredi 16 novembre 2005

9:00 - 10:00 Keynote / Exposé invité : Stéphane MALLAT, Ecole Poytechnique, France
10:00 - 10:30

L-CAMP: Extremely local MR representations.
Youngmi Hur & Amos Ron (University of Wisconsin-Madison, USA)
10:30 - 11:00
Coffee break / Pause café
11:00 - 11:30

Learning redundant dictionnaries with translation invariance property: the MoTIF algorithm.
Philippe Jost, Pierre Vandergheynst (EPFL, Lausanne, Suisse)
Sylvain Lesage & Rémi Gribonval (IRISA-INRIA, Rennes, France)
11:30 - 12:00

K-SVD: design of dictionaries for sparse representation.
Michal Aharon, Michael Elad & Alfred M. Bruckstein (The Technion, Haifa, Israel)
12:00 - 12:30

Texture classification using sparse representations by learned compound dictionaries.
J. Herredsvela, K. Engan & T. O. Gulsrud & K. Skretting (University of Stavanger, Norway)

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Wednesday, November 16, 2005
Technical Session 2
Sparse audio models
Mercredi 16 novembre 2005

15:00 - 16:00 Keynote / Exposé invité : Sparsity in Audio Signal Processing and Coding
Mike DAVIES, Queen Mary, University of London, UK
16:00 - 16:30
Coffee break / Pause café
16:30 - 17:00

A study of Bernoulli and stuctured random waveform models for audio signals.
Matthieu Kowalski & Bruno Torrésani (LATP, CMI, Marseille, France)
17:00 - 17:30

Blind one-microphone speech separation: a spectral learning approach.
Francis R. Bach (Ecole des mines, Paris, France)
Michael I. Jordan (EECS Departement, Berkeley, USA)


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Thursday, November 17, 2005
Technical Session 3
Source separation
Jeudi 17 novembre 2005

9:00 - 10:00 Keynote / Exposé invité : Michael ZIBULEVSKY, Technion Institute, Israël
10:00 - 10:30

Underdetermined sparse blind source separation with delays.
Rayan Saab, Özgür Yilmaz, Martin J. McKeown, Rafeef Abugharbieh (University of British Columbia, Vancouver, Canada)
10:30 - 11:00
Coffee break / Pause café
11:00 - 11:30

Extending the DUET blind source separation technique.
Thomas Melia & Scott Rickard (University College, Dublin, Ireland)
11:30 - 12:00

Shift-Invariant sparse coding for single channel blind source separation.
Thomas Blumensath, Mike Davies (Queen Mary, University of London, UK))
12:00 - 12:30

A study of the effect of source sparsity for various transforms on blind audio source separation performance.
Vincent Y.F. Tan & Cédric Févotte (Cambridge University, Cambridge, UK)


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Thursday, November 17, 2005
Technical Session 4
Sparsity: Theory & Analysis
Jeudi 17 novembre 2005

15:00 - 15:30

Shrinkage for redundant representations.
Michael Elad (The Technion, Haifa, Israël)
15:30 - 16:00

Geometry and homotopy for L1 sparse representations.
Mark D. Plumbley (Queen Mary University, London, UK)
16:00 - 16:30
Coffee break / Pause café
16:30 - 17:00

Sparse approximation by linear programming using an L1 data-fidelity term.
Lorenzo Granai & Pierre Vandergheynst (EPFL, Lausanne, Suisse)
17:00 - 17:30

Parameterized lifting for sparse signal representations using the Gini index.
Niall Hurley, Scott Rickard & Paul Curran (University College, Dublin, Ireland)

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Friday, November 18, 2005
Technical Session 5
Multiple sensors & compressed sensing
Vendredi 18 novembre 2005


9:00 - 10:00 Keynote / Exposé invité :
Sublinear Algorithms for Compressed Sensing
Joel TROPP, Michigan University, USA
10:00 - 10:30

Application of "compressed sensing" for rapid MR imaging.
Michael Lustig, Juan M. Santos, Jin-Hyung Lee, David L. Donoho & John M. Pauly (Stanford University, Stanford, USA)
10:30 - 11:00
Joint sparsity models for distributed compressed sensing.
Marco F. Duarte, Shriram Sarvotham, Michael Wakin, Dror Baron & Richard Baraniuk (Rice University, Houston, USA)
11:00 - 11:30

Coffee break / Pause café
11:30 - 12:00

Fast reconstruction of piecewise smooth signals from incoherent projections.
Marco Duarte, Michael Wakin & Richard Baraniuk (Rice University, Houston, USA)
12:00 - 12:30

Finding sparse representations in multiple response models via Bayesian learning.
David Wipf & Bhaskar Rao (University of California, San Diego, USA)