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Proceedings Paper

Iterative segmentation using pulse-coupled neural networks
Author(s): Heggere S. Ranganath; Govindaraj Kuntimad
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Paper Abstract

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.

Paper Details

Date Published: 22 March 1996
PDF: 12 pages
Proc. SPIE 2760, Applications and Science of Artificial Neural Networks II, (22 March 1996); doi: 10.1117/12.235943
Show Author Affiliations
Heggere S. Ranganath, Univ. of Alabama in Huntsville (United States)
Govindaraj Kuntimad, Rockwell International Corp./Rocketdyne Div. (United States)


Published in SPIE Proceedings Vol. 2760:
Applications and Science of Artificial Neural Networks II
Steven K. Rogers; Dennis W. Ruck, Editor(s)

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