Comparison of artificial and natural neural computation: an application to automatic target recognition

Karina E. Waldemark; Vlatko Becanovic; Jason M. Kinser; Thomas Lindblad; Clark S. Lindsey; Geza Szekely

doi:10.1117/12.343035

22 March 1999 Comparison of artificial and natural neural computation: an application to automatic target recognition

Karina E. Waldemark, Vlatko Becanovic, Jason M. Kinser, Thomas Lindblad, Clark S. Lindsey, Geza Szekely

Proceedings Volume 3728, Ninth Workshop on Virtual Intelligence/Dynamic Neural Networks; (1999) https://doi.org/10.1117/12.343035
Event: Ninth Workshop on Virtual Intelligence/Dynamic Neural Networks: Neural Networks Fuzzy Systems, Evolutionary Systems and Virtual Re, 1998, Stockholm, Sweden

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.

Citation Download Citation

Karina E. Waldemark, Vlatko Becanovic, Jason M. Kinser, Thomas Lindblad, Clark S. Lindsey, and Geza Szekely "Comparison of artificial and natural neural computation: an application to automatic target recognition", Proc. SPIE 3728, Ninth Workshop on Virtual Intelligence/Dynamic Neural Networks, (22 March 1999); https://doi.org/10.1117/12.343035

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