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Communication Dans Un Congrès Année : 2008

Spiking neuron network for image segmentation

Résumé

The process of segmenting images is one of the most critical ones in automatic image analysis whose goal can be regarded as to find what objects are presented in images. Artificial neural networks have been well developed. First two generations of neural networks have a lot of successful applications. Spiking Neuron Networks (SNNs) are often referred to as the 3rd generation of neural networks which have potential to solve problems related to biological stimuli. They derive their strength and interest from an accurate modeling of synaptic interactions between neurons, taking into account the time of spike emission. SNNs overcome the computational power of neural networks made of threshold or sigmoidal units. Based on dynamic eventdriven processing, they open up new horizons for developing models with an exponential capacity of memorizing and a strong ability to fast adaptation. Moreover, SNNs add a new dimension, the temporal axis, to the representation capacity and the processing abilities of neural networks. In this paper, we present how SNN can be applied with efficacy in image segmentation.
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Dates et versions

hal-00329522 , version 1 (11-10-2008)

Identifiants

  • HAL Id : hal-00329522 , version 1

Citer

Boudjelal Meftah, M. Debakla, M. Zaagane, A. Benyettou, Olivier Lezoray. Spiking neuron network for image segmentation. Maghrebian Conference on Information Technologies, 2008, Algeria. pp.13-17. ⟨hal-00329522⟩
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