20 October 1993 Exploiting massive parallelism in algorithm understanding for automatic target recognition on SAR imagery
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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.
© (1993) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Leslie Dias, Leslie Dias, John J. Santapietro, John J. Santapietro, "Exploiting massive parallelism in algorithm understanding for automatic target recognition on SAR imagery", Proc. SPIE 1957, Architecture, Hardware, and Forward-Looking Infrared Issues in Automatic Target Recognition, (20 October 1993); doi: 10.1117/12.161445; https://doi.org/10.1117/12.161445
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