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Proceedings Paper

GPU processing for parallel image processing and real-time object recognition
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Paper Abstract

In this paper, we present a method for reducing the computation time of Automated Target Recognition (ATR) algorithms through the utilization of the parallel computation on Graphics Processing Units (GPUs). A selected multistage ATR algorithm is refounded to encourage efficient execution on the GPU. Such refounding includes parallel reimplementations of optical correlation, Feature Extraction, Classification and Correlation using NVIDIA's CUDA programming model. This method is shown to significantly reduce computation time of the selected ATR algorithms allowing the potential for further complexity and real-time applications.

Paper Details

Date Published: 5 May 2014
PDF: 12 pages
Proc. SPIE 9094, Optical Pattern Recognition XXV, 909407 (5 May 2014); doi: 10.1117/12.2054353
Show Author Affiliations
Kevin Vincent, California State Univ. (United States)
Damien Nguyen, Saddleback College (United States)
Brian Walker, Georgia Institute of Technology (United States)
Thomas Lu, Jet Propulsion Lab. (United States)
Tien-Hsin Chao, Jet Propulsion Lab. (United States)


Published in SPIE Proceedings Vol. 9094:
Optical Pattern Recognition XXV
David Casasent; Tien-Hsin Chao, Editor(s)

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