CFP last date
22 April 2024
Call for Paper
May Edition
IJCA solicits high quality original research papers for the upcoming May edition of the journal. The last date of research paper submission is 22 April 2024

Submit your paper
Know more
The week's pick
Responsive image
Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

Twinkle Patel Kiran Amin
Random Articles
Reseach Article

Towards Affordable Computing: SiftCU a Simple but Elegant GPU-based Implementation of SIFT

by Mahdi S. Mohammadi, Mehdi Rezaeian
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 90 - Number 7
Year of Publication: 2014
Authors: Mahdi S. Mohammadi, Mehdi Rezaeian
10.5120/15587-4329

Mahdi S. Mohammadi, Mehdi Rezaeian . Towards Affordable Computing: SiftCU a Simple but Elegant GPU-based Implementation of SIFT. International Journal of Computer Applications. 90, 7 ( March 2014), 30-37. DOI=10.5120/15587-4329

@article{ 10.5120/15587-4329,
author = { Mahdi S. Mohammadi, Mehdi Rezaeian },
title = { Towards Affordable Computing: SiftCU a Simple but Elegant GPU-based Implementation of SIFT },
journal = { International Journal of Computer Applications },
issue_date = { March 2014 },
volume = { 90 },
number = { 7 },
month = { March },
year = { 2014 },
issn = { 0975-8887 },
pages = { 30-37 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume90/number7/15587-4329/ },
doi = { 10.5120/15587-4329 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:10:27.037114+05:30
%A Mahdi S. Mohammadi
%A Mehdi Rezaeian
%T Towards Affordable Computing: SiftCU a Simple but Elegant GPU-based Implementation of SIFT
%J International Journal of Computer Applications
%@ 0975-8887
%V 90
%N 7
%P 30-37
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This article presents a fully functional GPU-based implementation of Scale Invariant Feature Transform (SIFT) algorithm. SIFT is a popular image feature extraction algorithm. Although it is a powerful algorithm for image matching but it is also computationally very expensive. This makes it difficult to use especially in real time applications. We purpose to expedite SIFT through GPU-based implementation. There has been some related works on this issue since SIFT was introduced. Our focus is solely on describing GPU-based implementation. We will discuss our implementation in detail. Our implementation is simpler and more efficient than previous works. Part of this paper's purpose is to discuss challenges and strategies related to implementing SIFT like image processing algorithms on GPU. In addition, we are going to present a full comparison between serial implementations of SIFT and our GPU-based implementation, namely siftCU, both in accuracy and time consumption.

References
  1. D. G. Lowe, "Distinctive Image Features from Scale-Invariant Keypoints," Journal of Computer Vision, vol. 60, no. 2, pp. 91-110, 2004.
  2. S. N. Sinha, J. -M. Frahm, M. Pollefeys and Y. Genc, "GPU-based Video Feature Tracking and Matching," in Workshop on Edge Computing Using New Commodity Architectures, Chapel Hill, North Carolina, 2006.
  3. S. Heymann, K. Muller, A. Smolic, B. Froehlich and T. Wiegand, "SIFT Implementation and Optimization for General-Purpose GPU," in International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision (WSCG), Plzen, Czech Republic, 2007.
  4. S. Warn, W. Emeneker, J. Cothren and A. Apon, "Accelerating SIFT on Parallel Architectures," in IEEE International Conference on Cluster Computing and Workshops, 2009. CLUSTER '09. , New Orleans, LA, 2009.
  5. Y. Huang, J. Liu, M. Tu, S. Li and J. Deng, "Research on CUDA-based SIFT Registration of SAR Image," in Fourth International Symposium on Parallel Architectures, Algorithms and Programming (PAAP), Tianjin, 2011.
  6. T. Yamazaki, T. Fujikawa and J. Katto, "Improving the performance of SIFT using Bilateral Filter and its application to generic object recognition," in 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Kyoto, 2012.
  7. Y. YANG and W. CHEN, "Parallel Algorithm for Moving Foreground Detection in Dynamic Background," in Fifth International Symposium on Computational Intelligence and Design, Hangzhou, 2012.
  8. M. Harris, "GPGPU," 2013. [Online]. Available: http://www. gpgpu. org.
  9. T. R. H. M. L. S. André R. Brodtkorba, "Graphics processing unit (GPU) programming strategies and trends in GPU computing," Jurnal of Parallel and Distributed Computing, vol. 73, no. 1, pp. 4-13, 2013.
  10. "openCL Home," KHRONOS GROUP, 2013. [Online]. Available: http://www. khronos. org/opencl/.
  11. "CUDA Home," Nvidia, 2013. [Online]. Available: http://www. nvidia. com/object/cuda_home_new. html.
  12. King, "siftCU Home," 2013. [Online]. Available: http://www. siftcu. eu5. org/.
  13. NVIDIA, "CUDA C Best Practices Guide," NVIDIA, 2013.
  14. R. Szeliski, Computer Vision: Algorithms and Applications, Springer, 2011.
  15. A. Vedaldi, "siftpp," 2006. [Online]. Available: http://www. vlfeat. org/~vedaldi/code/siftpp. html.
Index Terms

Computer Science
Information Sciences

Keywords

CUDA GPGPU GPU programming Image Retrieval SIFT

Powered by PhDFocusTM