Low-cost, high-speed computer vision using NVIDIA's CUDA architecture
Seung In Park, Sean Ponce, Huang Jing, Yong Cao, Francis K. H. Quek · 2008
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, ldquocompute unified device architecturerdquo (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 ldquovector coherence mappingrdquo (VCM). Our results show the promise of using such common low-cost processors for intensive computer vision tasks.