DOI QR코드

DOI QR Code

Novel Parallel Approach for SIFT Algorithm Implementation

  • Le, Tran Su (School of Computer Engineering and Information Technology, University of Ulsan) ;
  • Lee, Jong-Soo (School of Computer Engineering and Information Technology, University of Ulsan)
  • Received : 2013.09.16
  • Accepted : 2013.10.30
  • Published : 2013.12.31

Abstract

The scale invariant feature transform (SIFT) is an effective algorithm used in object recognition, panorama stitching, and image matching. However, due to its complexity, real-time processing is difficult to achieve with current software approaches. The increasing availability of parallel computers makes parallelizing these tasks an attractive approach. This paper proposes a novel parallel approach for SIFT algorithm implementation using a block filtering technique in a Gaussian convolution process on the SIMD Pixel Processor. This implementation fully exposes the available parallelism of the SIFT algorithm process and exploits the processing and input/output capabilities of the processor, which results in a system that can perform real-time image and video compression. We apply this implementation to images and measure the effectiveness of such an approach. Experimental simulation results indicate that the proposed method is capable of real-time applications, and the result of our parallel approach is outstanding in terms of the processing performance.

Keywords

References

  1. S. N. Sinha, J. M. Frahm, M. Pollefeys, and Y. Genc, "Feature tracking and matching in video using programmable graphics hardware," Machine Vision and Applications, vol. 22, no. 1, pp. 207-217, 2011. https://doi.org/10.1007/s00138-007-0105-z
  2. Q. Zhang, Y. Chen, Y. Zhang, and Y. Xu, "SIFT implementation and optimization for multi-core systems," in Proceedings of the IEEE International Symposium on Parallel and Distributed Processing, Miami: FL, 2008.
  3. L. T. Su, P. J. Ghang, and J. S. Lee, "Integer Gaussian convolution with cache memory for real-time processing of the Scale Invariant Feature Transform algorithm," in Proceedings of the International Forum on Strategic Technology, Ulaanbaatar, Mongolia, pp. 298-301, 2007.
  4. S. Heymann, K. Muller, A. Smolic, B. Frohlich, and T. Wiegand, "SIFT implementation and optimization for general-purpose GPU," in Proceedings of the 15th International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, Plzen-Bory, Czech Republic, 2007.
  5. Y. Ke and R. Sukthankar, "PCA-SIFT: a more distinctive representation for local image descriptors," in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Washington: DC, pp. 506-513, 2004.
  6. K. Mikolajczyk and C. Schmid, "A performance evaluation of local descriptors," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 10, pp. 1615-1630, 2005. https://doi.org/10.1109/TPAMI.2005.188
  7. P. Azad, T. Asfour, and R. Dillmann, "Combining Harris interest points and the SIFT descriptor for fast scale-invariant object recognition," in Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, St. Louis: MO, pp. 4275-4280, 2009.
  8. M. Grabner, H. Grabner, and H. Bischof, "Fast approximated SIFT," in Proceedings of the 7th Asian Conference on Computer Vision, Hyderabad, India, pp. 918-927, 2006.
  9. D G. Lowe, "Object recognition from local scale-invariant features," in Proceedings of the International Conference on Computer Vision, Kerkyra, Greece, p. 1150, 1999.
  10. D. G. Lowe, "Distinctive image features from scale-invariant keypoints," International Journal of Computer Vision, vol. 60, no. 2, pp. 91-110, 2004. https://doi.org/10.1023/B:VISI.0000029664.99615.94
  11. A. Gentile, H. Cat, F. Kossentini, F. Sorbello, and D. S. Wills, "Real-time vector quantization-based image compression on the SIMPil low memory SIMD architecture," in Proceedings of the IEEE International Conference on Performance, Computing and Communications, Tempe: AZ, pp. 10-16, 1997.