high performance computing on graphics processing units: hgpu.org

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.