Low-cost, high-speed computer vision using NVIDIA’s CUDA architecture
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}
}
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.
January 13, 2011 by hgpu
