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Proceedings Paper

Multistage foveal target detection system
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Paper Abstract

The premise of foveal vision is that surveying a large area with low resolution to detect regions of interest, followed by their verification with localized high resolution, is a more efficient use of computational and communications throughput than resolving the area uniformly at high resolution. This paper presents target/clutter discrimination techniques that support the foveal multistage detection and verification of infrared-sensed ground targets in cluttered environments. The first technique uses a back-propagation neural network to classify narrow field-of-view high acuity image chips using their projection onto a set of principal components as input features. The second technique applies linear discriminant analysis on the same input features. Both techniques include refinements that address generalization and detected region of interest position errors. Experimental results using second generation forward looking infrared imagery are presented.

Paper Details

Date Published: 17 July 1998
PDF: 10 pages
Proc. SPIE 3374, Signal Processing, Sensor Fusion, and Target Recognition VII, (17 July 1998); doi: 10.1117/12.327097
Show Author Affiliations
Douglas C. McKee, Amherst Systems Inc. (United States)
Cesar Bandera, Amherst Systems Inc. (United States)


Published in SPIE Proceedings Vol. 3374:
Signal Processing, Sensor Fusion, and Target Recognition VII
Ivan Kadar, Editor(s)

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