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5 May 2014 GPU processing for parallel image processing and real-time object recognition
Kevin Vincent, Damien Nguyen, Brian Walker, Thomas Lu, Tien-Hsin Chao
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Proceedings Volume 9094, Optical Pattern Recognition XXV; 909407 (2014) https://doi.org/10.1117/12.2054353
Event: SPIE Defense + Security, 2014, Baltimore, MD, United States
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.
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Kevin Vincent, Damien Nguyen, Brian Walker, Thomas Lu, and Tien-Hsin Chao "GPU processing for parallel image processing and real-time object recognition", Proc. SPIE 9094, Optical Pattern Recognition XXV, 909407 (5 May 2014); https://doi.org/10.1117/12.2054353
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KEYWORDS
Automatic target recognition
Image processing
Principal component analysis
Computing systems
Detection and tracking algorithms
Image filtering
Computer programming
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