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Low-cost, high-speed computer vision using NVIDIA’s CUDA architecture

Seung In Park, Sean P. Ponce, Jing Huang, Yong Cao, Francis Quek
Center of Human Computer Interaction, Virginia Polytechnic Institute and University Blacksburg, VA 24060, USA
37th IEEE Applied Imagery Pattern Recognition Workshop, 2008

@article{park2008low,

   title={Low-cost, high-speed computer vision using NVIDIA’s CUDA architecture},

   author={Park, S.I. and Ponce, S.P. and Huang, J. and Cao, Y. and Quek, F.},

   year={2008},

   publisher={IEEE}

}

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In this paper, we introduce real time image processing techniques using modern programmable Graphic Processing Units (GPU). GPUs are SIMD (Single Instruction, Multiple Data) device that is inherently data-parallel. By utilizing NVIDIA’s new GPU programming framework, “Compute Unified Device Architecture” (CUDA) as a computational resource, we realize significant acceleration in image processing algorithm computations. We show that a range of computer vision algorithms map readily to CUDA with significant performance gains. Specifically, we demonstrate the efficiency of our approach by a parallelization and optimization of Canny’s edge detection algorithm, and applying it to a computation and data-intensive video motion tracking algorithm known as “Vector Coherence Mapping” (VCM). Our results show the promise of using such common low-cost processors for intensive computer vision tasks.
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