Graphic processing unit(GPU) and computer vision signal processing

Graphic 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.

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