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Proceedings Paper

Image segmentation using modified neural network techniques
Author(s): V. E. Gold; Darrel L. Chenoweth; John E. Selvage
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Paper Abstract

This paper describes an application of neural networks in segmenting gray shade images. It describes a method for ranking pixel features relative to their ability to discriminate among different image segment classes. A neural classifier is proposed which operates on pixel feature vectors as inputs to the network, each feature having a variable weight. The weights are iteratively changed to obtain dense and highly separated clusters. The resulting weights are indicative of the usefulness, or rank, of the features.

Paper Details

Date Published: 4 March 1996
PDF: 10 pages
Proc. SPIE 2664, Applications of Artificial Neural Networks in Image Processing, (4 March 1996); doi: 10.1117/12.234265
Show Author Affiliations
V. E. Gold, Univ. of Louisville (United States)
Darrel L. Chenoweth, Univ. of Louisville (United States)
John E. Selvage, Univ. of Louisville (United States)

Published in SPIE Proceedings Vol. 2664:
Applications of Artificial Neural Networks in Image Processing
Nasser M. Nasrabadi; Aggelos K. Katsaggelos, Editor(s)

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