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Sparse Grouping and Invariant
Representations for Estimation and Recognition
Guoshen Yu
Doctoral thesis
Ecole Polytechnique
Defended on June 30, 2009
Abstract: This thesis develops
several contributions for signal and image processing as well as for
computer vision. The first part includes a new audio denoising algorithm and
a super-resolution algorithm for image zooming. These algorithms are based
on some new sparse representations by blocks. A time-frequency block
thresholding procedure is introduced for the audio denoising, which enables
noise reduction without introducing artifacts, with the results superior to
the state-of-the-art. This first part also develops a general approach to
solve inverse problems with some piecewise linear sparser representations
over the blocks. The application to the image super-resolution allows
obtaining a fast algorithm, which clearly improves the PSNR relatively to
the existing algorithms.
The second part of the thesis introduces an algorithm (ASIFT) of
establishing correspondences between images, which is invariant to affine
transforms. It is demonstrated that this algorithm satisfies the invariance
constraints and it is able to make correspondences between objects observed
under arbitrary angles. Its numeric complexity is of the same order as the
most efficient algorithms, with a significantly higher robustness thanks to
its affine invariance.
The third part of the thesis introduces a biologically plausible
implementation of visual grouping. Inspired by the mechanism of neural
synchronization in perceptual grouping, a general algorithm based on neural
oscillators is proposed to make visual grouping. The same algorithm is shown
to achieve promising results on several classical visual grouping problems,
including point clustering, contour integration, and image segmentation.
Jury:
| Emmanuel
Bacry |
Examiner, Ecole
Polytechnique, France |
| Michael
Elad |
Reviewer,
Technion, Israel |
| Henri
Maître |
Examiner,
Telecom ParisTech, France |
| St└phane
Mallat |
Advisor, Ecole
Polytechnique, France |
|
Jean-Michel Morel |
Examiner, ENS
Cachan, France |
| Jean
Ponce |
Reviewer, ENS,
France |
|
Guillermo Sapiro |
Examiner,
University of Minnesota, USA |
|
Jean-Jacques Slotine |
Examiner, MIT,
USA |
Download (pdf, 7MB) |