GPU based Parallel Image Processing Library for Embedded Systems
Mustafa Cavus, Hakkı Doganer Sumerkan, Osman Seckin Simsek, Hasan Hassan, Abdullah Giray Yaglikci, Oguz Ergin
2014
Abstract
Embedded image processing systems have many challenges, due to large computational requirements and other physical, power, and environmental constraints. However recent contemporary mobile devices include a graphical processing unit (GPU) in order to offer better use interface in terms of graphics. Some of these embedded GPUs also support OpenCL which allows the use of computation capacity of embedded GPUs for general purpose computing. Within this OpenCL support, challenges of image processing in embedded systems become easier to handle. In this paper, we present a new OpenCL-based image processing library, named TRABZ-10, which is specifically designed to run on an embedded platform. Our results show that the functions of TRABZ-10 show 7X speedup on embedded platform over the functions of OpenCV on average.
References
- Khronos Group. (2012). OpenGL Shading Language Specification. Available: http://www.opengl.org/ documentation/glsl. Last accessed 25th April 2013.
- Khronos Group. (2004). OpenGL ES 2.0 Specification. Available: http://www.khronos.org/opengles. Last accessed 25th April 2013.
- Munshi, A (2008). OpenGL ES 2.0 Programming Guide. Addison-Wesley Professional.
- R. Marroquim, A. Maximo, Introduction to GPU Programming with GLSL. Computer Graphics and Image Processing (SIBGRAPI TUTORIALS), 2009 Tutorials of the XXII Brazilian Symposium on, vol., no., pp.3,16, 11-14 Oct. 2009.
- Owens, J. (2007). A Survey of general-purpose computation on graphics hardware. Computer graphics forum. 26 (1), p80-113.
- Singhal, N. (2010). Implementation and optimization of image processing algorithms on handheld gpu. IEEE International Conference on Image Processing. p4481-4484.
- Farrugia, J-P. (2006). GPUCV: A framework for image processing acceleration with graphics processors. IEEE International Conference on Multimedia and Expo, 2006. p585-588.
- Babenko, P. (2008). MinGPU: a minimum GPU library for computer vision. Journal of Real-Time Image Processing. 3 (4), p255-268.
- Fung, J. (2005). OpenVIDIA: parallel GPU computer vision. Proceedings of the 13th annual ACM international conference on Multimedia. p849-852.
- Lindholm, E. (2008). NVIDIA Tesla: A unified graphics and computing architecture. Micro, IEEE. 28 (2), p39- 55.
- Yang, Z. (2008). Parallel image processing based on CUDA. IEEE International Conference on Computer Science and Software Engineering. 3 (1), p198-201.
- Kong, J. (2010). Accelerating MATLAB image processing toolbox functions on GPUs. ACM Proceedings of the 3rd Workshop on General-Purpose Computation on Graphics Processing Units. p75-85.
- Luo, Y. (2008). Canny edge detection on NVIDIA CUDA. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPRW'08. p1-8.
- Khronos OpenCL Working Group. (2008). OpenCL Specification. Available: http://www.khronos.org/ registry/cl. Last accessed 25th April 2013.
- Czajkowski, T. (2012). From OpenCL to highperformance hardware on FPGAs. IEEE 22nd International Conference on Field Programmable Logic and Applications (FPL). p531-534.
- Karimi, K. (2010). A performance comparison of CUDA and OpenCL. arXiv preprint arXiv:1005.2581.
- Fang, J. (2011). A comprehensive performance comparison of CUDA and OpenCL. IEEE International Conference on. Parallel Processing (ICPP). p216-225.
- Du, P. (2012). From CUDA to OpenCL: Towards a performance-portable solution for multi-platform GPU programming. Parallel Computing. 38 (8), p391-407.
- Munshi, A (2011). OpenCL programming guide. AddisonWesley Professional.
- Leskela, J. (2009). OpenCL embedded profile prototype in mobile device. IEEE Workshop on Signal Processing Systems, (SiPS). p279-284.
- Cheng, K. (2011). Using mobile GPU for general-purpose computing-a case study of face recognition on smartphones. IEEE International Symposium on VLSI Design, Automatin and Test (VLSI-DAT). p1-4.
- Lopez, M. (2011). Accelerating image recognition on mobile devices using GPGPU. International Society for Optics and Photonics IS&T/SPIE Electronic Imaging. 1 (1), p'720R-78720R-10.
Paper Citation
in Harvard Style
Cavus M., Sumerkan H., Simsek O., Hassan H., Yaglikci A. and Ergin O. (2014). GPU based Parallel Image Processing Library for Embedded Systems . In Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2014) ISBN 978-989-758-003-1, pages 234-241. DOI: 10.5220/0004859902340241
in Bibtex Style
@conference{visapp14,
author={Mustafa Cavus and Hakkı Doganer Sumerkan and Osman Seckin Simsek and Hasan Hassan and Abdullah Giray Yaglikci and Oguz Ergin},
title={GPU based Parallel Image Processing Library for Embedded Systems},
booktitle={Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2014)},
year={2014},
pages={234-241},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004859902340241},
isbn={978-989-758-003-1},
}
in EndNote Style
TY - CONF
JO - Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2014)
TI - GPU based Parallel Image Processing Library for Embedded Systems
SN - 978-989-758-003-1
AU - Cavus M.
AU - Sumerkan H.
AU - Simsek O.
AU - Hassan H.
AU - Yaglikci A.
AU - Ergin O.
PY - 2014
SP - 234
EP - 241
DO - 10.5220/0004859902340241