Low-Cost, High-Speed Computer Vision Using NVIDIA's CUDA Architecture

In this paper, we introduce real time image processing techniques using modem 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.