Nonlinear signal and image processing with polynomial neural nets

Sheldon B. Gardner

doi:10.1117/12.150954

27 August 1993 Nonlinear signal and image processing with polynomial neural nets

Sheldon B. Gardner

Proceedings Volume 1961, Visual Information Processing II; (1993) https://doi.org/10.1117/12.150954
Event: Optical Engineering and Photonics in Aerospace Sensing, 1993, Orlando, FL, United States

Abstract

Many natural backgrounds, such as radar clutter from the ocean surface, which were previously thought to be random may be chaotic. Because of the finite dimensionality of a chaotic background, a non-linear signal processor can be trained as a global predictor. The results of a continuing study of polynomial neural nets (PNN), used for global prediction, are described. Encouraging results have been obtained with PNNs for both signal processing (time series) and images. Since PNNs can be trained to predict chaotic backgrounds, threshold target images can be detected by subtracting the predicted background from the target plus background. In this paper we summarize the basis for PNN processing and present recent PNN image processing results using as a chaotic background video images of the ocean surface.

Citation Download Citation

Sheldon B. Gardner "Nonlinear signal and image processing with polynomial neural nets", Proc. SPIE 1961, Visual Information Processing II, (27 August 1993); https://doi.org/10.1117/12.150954

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