A Color Image Segmentation Using Inhibitory Connected Pulse Coupled Neural Network

Authors:
Hiroaki Kurokawa;Shuzo Kaneko;Masato Yonekawa
Affiliations:
Tokyo University of Technology, Tokyo, Japan;Tokyo University of Technology, Tokyo, Japan;Tokyo University of Technology, Tokyo, Japan
Venue:
Advances in Neuro-Information Processing
Year:
2009

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Abstract

A Pulse Coupled Neural Network (PCNN) is a kind of numerical model of cat visual cortex and it can explain synchronous dynamics of neurons' activity in the visual cortex. On the other hand, as an engineering application, it is shown that the PCNN can applied to the image processing, e.g. segmentation, edge enhancement, and so on. The PCNN model consists of neurons and two kind of inputs, namely feeding inputs and linking inputs with leaky integrators. These inputs lead to discrete time evolution of its internal state and neurons generate spike output according to the internal state. The linking and feeding inputs are received from the neurons' receptive field which is defined by excitatory synaptic weights. In this study, we propose a PCNN with inhibitory connections and describe an application to a color image segmentation. In proposed model, inhibitory connections are defined by negative synaptic weights among specific neurons which detect RGB component of particular pixel of the image. Simulation results show successful results for the color image segmentation.