Pulse-coupled neural networks can benefit ATR

John L. Johnson

doi:10.1117/12.343032

22 March 1999 Pulse-coupled neural networks can benefit ATR

John L. Johnson

Proceedings Volume 3728, Ninth Workshop on Virtual Intelligence/Dynamic Neural Networks; (1999) https://doi.org/10.1117/12.343032
Event: Ninth Workshop on Virtual Intelligence/Dynamic Neural Networks: Neural Networks Fuzzy Systems, Evolutionary Systems and Virtual Re, 1998, Stockholm, Sweden

Abstract

The first problem confronting the developers of algorithms for reliable automatic object recognition systems is basic intensity segmentation and noise smoothing. The benefits of using PCNNs for this are described. The next issue for the developer is the mixture of syntactical and statistical techniques. For many, only the latter is included due to the lack of abundance of fast, simple and effective syntactical algorithms. Relational maps and model-based algorithms are generally computationally intensive as compared to a straightforward statistical method such as a classifier net. It is described how the time signals of a nonadaptive PCNN incorporate some syntactical information which in turn has been shown to be compatible with a statistical classifier.

Citation Download Citation

John L. Johnson "Pulse-coupled neural networks can benefit ATR", Proc. SPIE 3728, Ninth Workshop on Virtual Intelligence/Dynamic Neural Networks, (22 March 1999); https://doi.org/10.1117/12.343032

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