Iterative segmentation using pulse-coupled neural networks

Recent studies of the visual cortices of cats and monkeys has led to the development of a new class of artificial neuron models. Eckhorn and his co-workers have developed one such neuron model. They have demonstrated that the recurrent networks of Eckhorn's neurons are capable of duplicating some of the neuro-physiological phenomena observed in cat's visual cortex. We have modified Eckhorn's neuron model in a way that the resulting neuron, referred to as the pulsed coupled neuron, becomes more suitable for image processing applications than his original model. It has been shown that a single layered laterally connected pulse coupled neural network (PCNN) is capable of smoothing, segmenting digital images. This paper describes an iterative segmentation scheme that utilizes smoothing, segmentation and feature extraction capabilities of PCNN. The knowledge driven iterative segmentation technique is powerful, flexible and has potential in real-time image processing systems.