Low-Cost, High-Speed Computer Vision Using NVIDIA's CUDA Architecture Conference Paper uri icon

abstract

  • 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.

name of conference

  • 2008 37th IEEE Applied Imagery Pattern Recognition Workshop

published proceedings

  • 2008 37th IEEE Applied Imagery Pattern Recognition Workshop

author list (cited authors)

  • Park, S. I., Ponce, S. P., Huang, J., Cao, Y., & Quek, F.

citation count

  • 25

complete list of authors

  • Park, Seung In||Ponce, Sean P||Huang, Jing||Cao, Yong||Quek, Francis

publication date

  • October 2008