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Proceedings Paper

Exploiting massive parallelism in algorithm understanding for automatic target recognition on SAR imagery
Author(s): Leslie Dias; John J. Santapietro
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Paper Abstract

We present a novel approach for implementing and optimizing an Automatic Target Recognition (ATR) algorithm for Synthetic Aperture Radar (SAR) imagery using the Princeton Engine (PE), a general purpose massively parallel single instruction multiple data (SIMD) machine. This approach was developed in the Algorithm Understanding Laboratory (AUL), a unique facility which is chartered to assist algorithm developers through high-speed implementation and near real-time visualization, and is located within the National Information Display Laboratory (NIDL). The PE architecture automatically provides a high speed-up directly proportional to the width of the image being processed, thereby reducing the train/test cycle times of ATR algorithms from days and hours down to minutes. Given this speed-up, the user can now train the system to classify a set of objects and then test it rapidly, thus tightening the train/test loop. With our approach, one can operate on the entire image, retaining useful image information until the very last stage in the algorithm.

Paper Details

Date Published: 20 October 1993
PDF: 6 pages
Proc. SPIE 1957, Architecture, Hardware, and Forward-Looking Infrared Issues in Automatic Target Recognition, (20 October 1993); doi: 10.1117/12.161445
Show Author Affiliations
Leslie Dias, David Sarnoff Research Ctr. (United States)
John J. Santapietro, David Sarnoff Research Ctr. (United States)

Published in SPIE Proceedings Vol. 1957:
Architecture, Hardware, and Forward-Looking Infrared Issues in Automatic Target Recognition
Lynn E. Garn; Lynda Ledford Graceffo, Editor(s)

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