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OpenVIDIA: parallel GPU computer vision

James Fung, Steve Mann
University of Toronto, Toronto, Ontario, Canada
In MULTIMEDIA ’05: Proceedings of the 13th annual ACM international conference on Multimedia (2005), pp. 849-852.

@conference{fung2005openvidia,

   title={OpenVIDIA: parallel GPU computer vision},

   author={Fung, J. and Mann, S.},

   booktitle={Proceedings of the 13th annual ACM international conference on Multimedia},

   pages={849–852},

   isbn={1595930442},

   year={2005},

   organization={ACM}

}

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Graphics and vision are approximate inverses of each other: ordinarily Graphics Processing Units (GPUs) are used to convert "numbers into pictures" (i.e. computer graphics). In this paper, we propose using GPUs in approximately the reverse way: to assist in "converting pictures into numbers" (i.e. computer vision). The OpenVIDIA project uses single or multiple graphics cards to accelerate image analysis and computer vision. It is a library and API aimed at providing a graphics hardware accelerated processing framework for image processing and computer vision. OpenVIDIA explores the creation of a parallel computer architecture consisting of multiple Graphics Processing Units (GPUs) built entirely from commodity hardware. OpenVIDIA uses multiple Graphic.Processing Units in parallel to operate as a general-purpose parallel computer architecture. It provides a simple API which implements some common computer vision algorithms. Many components can be used immediately and because the project is Open Source, the code is intended to serve as templates and examples for how similar algorithms are mapped onto graphics hardware. Implemented are image processing techniques (Canny edge detection, filtering), image feature handling (identifying and matching features) and image registration, to name a few.
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