Timed neural nets for moving target recognition

Dipak Basu; Stephen Lucci; Izidor Gertner; Harold R. Finz

doi:10.1117/12.177757

10 June 1994 Timed neural nets for moving target recognition

Dipak Basu, Stephen Lucci, Izidor Gertner, Harold R. Finz

Proceedings Volume 2232, Signal Processing, Sensor Fusion, and Target Recognition III; (1994) https://doi.org/10.1117/12.177757
Event: SPIE's International Symposium on Optical Engineering and Photonics in Aerospace Sensing, 1994, Orlando, FL, United States

Abstract

We propose a timed neural net (TNN) approach to the problem of recognition of moving targets. We consider a synchronous timed Petri net (TPN) as a model for this timed neural net. In a TPN the transitions are enabled and fired by using a 'time' token. A group of place nodes and their corresponding transition nodes model a neuron in a TNN. In order to classify the type of motion that a moving target is executing, we look upon an image sequence as a single image evolving in time. The reachability set, R(t) at any instant of time represents a snapshot of the weight matrix of a static neural net recognizing the target. The motion classification is achieved by analyzing R(t). An example illustrating the approach is constructed.

Citation Download Citation

Dipak Basu, Stephen Lucci, Izidor Gertner, and Harold R. Finz "Timed neural nets for moving target recognition", Proc. SPIE 2232, Signal Processing, Sensor Fusion, and Target Recognition III, (10 June 1994); https://doi.org/10.1117/12.177757

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