Computer vision signal processing on graphics processing units

Type :

Term papers

Pages :

5 pages

Format :

.pdf

Published date :

06/11/2009

Category :

Science & technology

Sub category :

Computer science

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Summary :

Abstract
Introduction
Relationship between graphics and vision
Background
Algorithm description
Techniques for applying graphics processing units for image analysis
1. Blurring and downsampling and derivatives
2. Projective texture lookups and light space conversion
3. Render to texture
4. Summations
Architectural performance
1. AGP and CPU effects
2. Fragment program length
Parallel computation on multiple GPUS
Conclusion
References

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 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. Keywords: Open graphics Library (OpenGL), graphics processing Unit (GPU)