| Paper: | DISPS-P1.12 | ||
| Session: | VLSI Algorithms and Architectures for DSP | ||
| Time: | Friday, May 21, 09:30 - 11:30 | ||
| Presentation: | Poster | ||
| Topic: | Design and Implementation of Signal Processing Systems: Hardware, Software, and Algorithm Tradeoffs and Integration | ||
| Title: | COMPUTER VISION SIGNAL PROCESSING ON GRAPHICS PROCESSING UNITS | ||
| Authors: | James Fung; University of Toronto | ||
| Steve Mann; University of Toronto | |||
| Abstract: | In some sense, computer graphics and computer vision are inverses of one another. Special purpose computer vision hardware is rarely found in typical mass-produced personal computers, but graphics processing units (GPUs) found on most personal computers, often exceed (in number of transistors as well as in compute power) the capabilities of the Central Processing Unit (CPU). 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 modern 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. 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 modern GPUs. | ||
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