Computer vision signal processing on graphics processing units
James Fung, S. Mann · 2004
This paper shows speedups attained by using computer graphics hardware for implementation of computer vision algorithms by efficiently mapping mathematical operations of computer vision onto modem computer graphics architecture. As an example computer vision algorithm, we implement a real-time projective camera motion tracking routine on modern, GeForce FX class GPUs (graphics processing units). Algorithms are implemented using OpenGL and the nVIDIA Cg fragment shaders. Trade-offs between computer vision requirements and GPU resources are discussed. Algorithm implementation is examined closely, and hardware bottlenecks are addressed to examine the performance of GPU architecture for computer vision. It is shown that significant speedups can be achieved, while leaving the CPU free for other signal processing tasks. Applications of our work include wearable, computer mediated reality systems that use both computer vision and computer graphics, and require realtime processing with low-latency and high throughput provided by modem GPUs.