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Acceleration of profile creation for three-dimensional vector video with GPGPU

https://doi.org/10.15514/ISPRAS-2015-27(3)-27

Abstract

In the report the optimization of image similarity metric computation method for three dimensional vector video with general-purpose computations on graphical processor unit (GPGPU) is discussed. The use of stream processors in graphics accelerators and Compute Unified Device Architecture (CUDA) platform allows significant performance gain in comparison to calculations on general-purpose processors, while solving problems of computer vision and image similarity determination. The performance of the GPGPU metric value computation is measured and researched.

About the Author

A. . Tsyganov
Samara State Technical University
Russian Federation


References

1. Thorsten Scheuermann, Justin Hensley. Efficient histogram Generation Using Scattering on GPUs. Proceedings of the 2007 symposium on Interactive 3D graphics and games, ACM New York, NY, USA, 2007, pp. 33-37. doi: 10.1145/1230100.1230105

2. N. Cornelis, L. Van Gool. Fast Scale Invariant Feature Detection and Matching on Programmable Graphics Hardware. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2008, pp. 1-8. doi: 10.1109/CVPRW.2008.4563087

3. N. K. Govindaraju, E. S. Larsen, J. Gray, D. Manocha. A memory model for scientific algorithms on graphics processors. Proceedings of the ACM/IEEE Conference on Supercomputing (SC’06), NY, USA: ACM Press, 2006, no. 89, pp. 6-15. doi: 10.1109/SC.2006.2

4. V. Podlozhnyuk. Histogram calculation in CUDA. Technical report. NVIDIA, 2007, http://developer.download.nvidia.com/compute/cuda/1.1-Beta/x86_website/projects/histogram64/doc/histogram.pdf

5. Ramtin Shams, R. A. Kennedy. Efficient Histogram Algorithms for NVIDIA CUDA Compatible Devices. Australia, Gold Coast, ICSPCS, 2007. pp. 418-422.

6. Cedric Nugteren, Gert-Jan van den Braak, Henk Corporaal, Bart Mesman. High Performance Predictable Histogramming on GPUs: Exploring and Evaluating Algorithm Trade-offs. Proceedings of the Fourth Workshop on General Purpose Processing on Graphics Processing Units, NY, USA: ACM New York, 2011. pp. 1-9. doi: 10.1145/1964179.1964181

7. O. Fluck, S. Aharon, D. Cremers, M. Rousson. GPU histogram computation. ACM SIGGRAPH 2006 Research posters, SIGGRAPH ’06. ACM, 2006, p. 53. doi: 10.1145/1179622.1179683

8. Adityo Mahardito, Adang Suhendra, Deni Tri Hasta. Optimizing Parallel Reduction In Cuda To Reach GPU Peak Performance. Proceedings of The Second International Workshop on Open source and Open Content WOSOC 2010, Indonesia, Depok.: Gunadarma University, 2010, pp. 48-57.

9. Herbert Bay, Andreas Ess, Tinne Tuytelaars, Luc Van Gool. Speeded-Up Robust Features (SURF). Computer Vision and Image Understanding, New York, USA, 2008, vol. 110, no. 3, pp. 346-359. doi: 10.1016/j.cviu.2007.09.014

10. Timothy B. Terriberry, Lindley M. French, John Helmsen. GPU Accelerating Speeded-Up Robust Features. Proceedings of the Fourth International Symposium on 3D Data Processing, Visualization and Transmission, Georgia Institute of Technology, Atlanta, GA, USA, 2008. pp. 355-362.


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For citations:


Tsyganov A. Acceleration of profile creation for three-dimensional vector video with GPGPU. Proceedings of the Institute for System Programming of the RAS (Proceedings of ISP RAS). 2015;27(3):379-390. (In Russ.) https://doi.org/10.15514/ISPRAS-2015-27(3)-27



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ISSN 2079-8156 (Print)
ISSN 2220-6426 (Online)