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Computer vision signal processing on graphics processing units

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  1. Abstract
  2. Introduction
  3. Relationship between graphics and vision
  4. Background
  5. Algorithm description
  6. 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
  7. Architectural performance
    1. AGP and CPU effects
    2. Fragment program length
  8. Parallel computation on multiple GPUS
  9. Conclusion
  10. References

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)

[...] James Fung, Felix Tang, and Steve Mann, ?Mediated reality using computer graphics hardware for computer vision,? in Proceedings of the International Symposium on Wearable Computing 2002 (ISWC2002), Seattle, Washington, USA, Oct pp. 83?89. Shingo Kagami, Takashi Komuro, Idaku Ishii, and Masatoshi Ishikawa, real?time visual processing system using a general purpose vision in Proceedings of the 2002 IEEE International Conference on Robotics and Automation, Washington, DC, USA, May 2002, pp. 1229?1234. Peter J. Burt, pyramid-based front-end processor for dynamic vision applications,? Proceedings of the IEEE, vol no pp. [...]

[...] The per?pixel functionality provided by the fragment shaders makes them a natural for image processing and computer vision Blurring and downsampling, and derivatives The following Cg code (unrolled by the Cg compiler) can be used to create a simple low?pass filtered image: float4 diff = { for( ; ) { for( ; ) { diff+= texRECT(texture,Coord+float2(i,j)); diff = diff/ 9.0 ; In the case that the down sampled image is desired, the image can be texture mapped onto a quadrilateral which fills a window which is one quarter the size of the original image. [...]

[...] BACKGROUND Some approaches to fast hardware solutions for computer vision have included focal plane processors dedicated architectures and FPGA solutions Computer graphics architectures have also been applied towards general purpose computing and matrix operations[6, and image processing as well as simulation. present frameworks for implementing matrix operations on graphics hardware, however, they did not have available at the time modern graphics architectures, and could only achieve limited accuracy. Computer graphics hardware has also been used to accelerate the calculations of morphological filters as used for in volume analysis for volume rendering applications However, full computer vision algorithms have not yet been implemented - rather only particular operations have been conducted on the graphics card ALGORITHM DESCRIPTION Video Orbits is an image registration algorithm which calculates a coordinate transformation between pairs of images of a static scene, taken with a camera that is free to pan, tilt, rotate about its optical axis, and zoom. [...]

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