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Proceedings Paper

Comparison of artificial and natural neural computation: an application to automatic target recognition
Author(s): Karina E. Waldemark; Vlatko Becanovic; Jason M. Kinser; Thomas Lindblad; Clark S. Lindsey; Geza Szekely
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Paper Abstract

We make a few simple comparisons of the principles and performance for noise reduction and edge detection with conventional methods versus neural network methods. Noise reduction methods discussed include the wavelet packet transform. Edge detection is discussed from the point of view of the Sobel and Canny transforms. An approach using the IBM ZISC036 neural network chip is also discussed. In all cases, the results are compare to that of the biologically inspired PCNN. An application of the `best if both worlds' is demonstrated in a foveation/object isolation application for ATR.

Paper Details

Date Published: 22 March 1999
PDF: 15 pages
Proc. SPIE 3728, Ninth Workshop on Virtual Intelligence/Dynamic Neural Networks, (22 March 1999); doi: 10.1117/12.343035
Show Author Affiliations
Karina E. Waldemark, Royal Institute of Technology (Sweden)
Vlatko Becanovic, Royal Institute of Technology (Sweden)
Jason M. Kinser, George Mason Univ. (United States)
Thomas Lindblad, Royal Institute of Technology (Sweden)
Clark S. Lindsey, Royal Institute of Technology (Sweden)
Geza Szekely, ATOMKI Institute of Nuclear Research (Sweden)


Published in SPIE Proceedings Vol. 3728:
Ninth Workshop on Virtual Intelligence/Dynamic Neural Networks
Thomas Lindblad; Mary Lou Padgett; Jason M. Kinser, Editor(s)

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