Dr. Laurent Perrinet Profile

Dr. Laurent Perrinet

at Ctr National de la Recherche Scientifique

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Proceedings Article | 19 May 2008 Paper

Sparse spike coding : applications of neuroscience to the processing of natural images

Laurent Perrinet

Proceedings Volume 7000, 70000F (2008) https://doi.org/10.1117/12.787076

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If modern computers are sometimes superior to cognition in some specialized tasks such as playing chess or browsing a large database, they can't beat the efficiency of biological vision for such simple tasks as recognizing a relative or following an object in a complex background. We present in this paper our attempt at outlining the dynamical, parallel and event-based representation for vision in the architecture of the central nervous system. We will illustrate this by showing that in a signal matching framework, a L/LN (linear/non-linear) cascade may efficiently transform a sensory signal into a neural spiking signal and we apply this framework to a model retina. However, this code gets redundant when using an over-complete basis as is necessary for modeling the primary visual cortex: we therefore optimize the efficiency cost by increasing the sparseness of the code. This is implemented by propagating and canceling redundant information using lateral interactions. We compare the eciency of this representation in terms of compression as the reconstruction quality as a function of the coding length. This will correspond to a modification of the Matching Pursuit algorithm where the ArgMax function is optimized for competition, or Competition Optimized Matching Pursuit (COMP). We will particularly focus on bridging neuroscience and image processing and on the advantages of such an interdisciplinary approach.

Proceedings Article | 29 June 2005 Paper

Sparse Gabor wavelets by local operations

Sylvain Fischer, Rafael Redondo, Laurent Perrinet, Gabriel Cristobal

Proceedings Volume 5839, (2005) https://doi.org/10.1117/12.608403

KEYWORDS: Wavelets, Transform theory, Image compression, Image filtering, Denoising, Quantization, Image enhancement, Head, Image denoising, Wavelet transforms

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Efficient sparse coding of overcomplete transforms remains still an open problem. Different methods have been proposed in the literature, but most of them are limited by a heavy computational cost and by difficulties to find the optimal solutions. We propose here an algorithm suitable for Gabor wavelets and based on biological models. It is composed by local operations between neighboring transform coefficients and achieves a sparse representation with a relatively low computational cost. Used with a chain coder, this sparse Gabor wavelet transform is suitable for image compression but is also of interest also for other applications, in particular for edge and contour extraction and image denoising.

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