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Proceedings Paper

Image processing with the random neural network
Author(s): Erol Gelenbe; Hakan Bakircioglu; Taskin Kocak
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Paper Abstract

Enhancing image quality and combining observations into a coherent description are essential tools in various image processing applications such as multimedia publishing, target recognition, and medical imaging. In this paper we propose two novel approaches for image enlargement and image fusion using the Random Neural Network (RNN) model, which has already been successfully applied to the problems such as still and moving image compression, and image segmentation. The advantage of the RNN model is that it is closer to biophysical reality and mathematically more tractable than standard neural methods, especially when used as a recurrent structure.

Paper Details

Date Published: 1 April 1998
PDF: 12 pages
Proc. SPIE 3307, Applications of Artificial Neural Networks in Image Processing III, (1 April 1998); doi: 10.1117/12.304658
Show Author Affiliations
Erol Gelenbe, Duke Univ. (United States)
Hakan Bakircioglu, Duke Univ. (United States)
Taskin Kocak, Duke Univ. (United States)

Published in SPIE Proceedings Vol. 3307:
Applications of Artificial Neural Networks in Image Processing III
Nasser M. Nasrabadi; Aggelos K. Katsaggelos, Editor(s)

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