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.
Abstract.A Pulse Coupled Neural Network (PCNN) is proposed as a numerical model of cat visual cortex, and it has been applied to the engineering fields especially in an image processing, e.g., segmentation, edge enhancement, and so on. The PCNN model consists of neurons with two kind of inputs, namely feeding input and linking input and they each have a lot of parameters. The Parameters are used to be defined empirically and the optimization of parameters has been known as one of the remaining problem of PCNN. According to the recent studies, parameters in PCNN will be able to be given using parameter learning rule or evolutionary programming. However these methods require teaching images for the learning. In this study, we propose a parameter adjustment method of PCNN for the image segmentation. The proposed method changes the parameters through the iterations of trial of segmentation and the method doesn't require any teaching signal or teaching pattern. The successful results are obtained in the simulations, and we conclude that the proposed method shows good performance for the parameter adjustment of PCNNs.
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