Gray-Level Object Segmentation with a Network of FitzHugh-Nagumo Oscillators
نویسندگان
چکیده
In this paper we adopt a temporal coding approach to neuronal modeling of the visual cortex, using oscillations. We propose a hierarchy of three processing modules corresponding to di erent levels of representation. The rst layer encodes the input image (stimulus) into an array of units, while the second layer consists of a network of FitzHugh-Nagumo oscillators. The dynamical behaviour of the coupled oscillators is rigorously investigated and a stimulus-driven synchronization theorem is derived. However, this module reveals itself insu cient to correctly encode and segregate di erent objects when they have similar gray{levels in the input image. Therefore, a third layer connected in a feedback loop with the oscillators is added. This ensures synchronization (resp. desynchronization) of neuron ensembles representing the same (resp. a di erent) object. Simulation results are presented using synthetic as well as real and noisy gray{level images.
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