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Proceedings Paper

Timed neural nets for moving target recognition
Author(s): Dipak Basu; Stephen Lucci; Izidor Gertner; Harold R. Finz
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Paper 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.

Paper Details

Date Published: 10 June 1994
PDF: 8 pages
Proc. SPIE 2232, Signal Processing, Sensor Fusion, and Target Recognition III, (10 June 1994); doi: 10.1117/12.177757
Show Author Affiliations
Dipak Basu, CUNY/City College (United States)
Stephen Lucci, CUNY/City College (United States)
Izidor Gertner, CUNY/City College (United States)
Harold R. Finz, CUNY/City College (United States)

Published in SPIE Proceedings Vol. 2232:
Signal Processing, Sensor Fusion, and Target Recognition III
Ivan Kadar; Vibeke Libby, Editor(s)

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