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Proceedings Paper

Neural network for image segmentation
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Paper Abstract

Image analysis is an important requirement of many artificial intelligence systems. Though great effort has been devoted to inventing efficient algorithms for image analysis, there is still much work to be done. It is natural to turn to mammalian vision systems for guidance because they are the best known performers of visual tasks. The pulse- coupled neural network (PCNN) model of the cat visual cortex has proven to have interesting properties for image processing. This article describes the PCNN application to the processing of images of heterogeneous materials; specifically PCNN is applied to image denoising and image segmentation. Our results show that PCNNs do well at segmentation if we perform image smoothing prior to segmentation. We use PCNN for obth smoothing and segmentation. Combining smoothing and segmentation enable us to eliminate PCNN sensitivity to the setting of the various PCNN parameters whose optimal selection can be difficult and can vary even for the same problem. This approach makes image processing based on PCNN more automatic in our application and also results in better segmentation.

Paper Details

Date Published: 13 October 2000
PDF: 8 pages
Proc. SPIE 4120, Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation III, (13 October 2000); doi: 10.1117/12.403632
Show Author Affiliations
Alexei N. Skourikhine, Los Alamos National Lab. (United States)
Lakshman Prasad, Los Alamos National Lab. (United States)
Bernd R. Schlei, Los Alamos National Lab. (United States)

Published in SPIE Proceedings Vol. 4120:
Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation III
Bruno Bosacchi; David B. Fogel; James C. Bezdek, Editor(s)

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