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2008
IEEE

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

9 years 11 months ago
Low-cost, high-speed computer vision using NVIDIA's CUDA architecture
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.
Seung In Park, Sean P. Ponce, Jing Huang, Yong Cao
Added 12 Oct 2010
Updated 12 Oct 2010
Type Conference
Year 2008
Where AIPR
Authors Seung In Park, Sean P. Ponce, Jing Huang, Yong Cao, Francis K. H. Quek
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