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A Configurable Architecture for Fast Moments Computation

Abstract

In this paper, we present a single-chip architecture for generating a full set of geometric moments using digital filters. Other types of moments such as Zernike and Tchebichef moments can also be implemented. The architecture can be configured for any order of geometric moments and image spatial resolution at run time. The use of a single-scaler method and reusable hardware resources enables higher order moments to be computed. The incorporation of two-level pipelining and masking techniques further increases the throughput. Realized in a field-programmable gate array, the design is capable of processing sixty 512 × 512 8-bit-pixel images per second at 20 MHz, generating (59 + 59) orders of geometric moments (3,600 moments). The maximum round-off error is approximately 1 %.

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References

  1. Hu, M. K. (1962). Visual pattern recognition by moment invariants. IRE Transactions on Information Theory, 8(1), 179–187.

    MATH  Google Scholar 

  2. Dudani, S. A., Breeding, K. J., & McGhee, R. B. (1977). Aircraft identification by moment invariants. IEEE Transactions on Computers, C-26, 39–45.

    Article  Google Scholar 

  3. Teague, M. R. (1980). Image analysis via the general theory of moments. Journal of the Optical Society of America, 70, 920–930.

    Article  MathSciNet  Google Scholar 

  4. Reeves, A. P., & Wittner, B. S. (1983). Shape analysis of three dimensional objects using the method of moments. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 20–26, Anchorage.

  5. Kan, C., & Srinath, M. D. (2002). Invariant character recognition with Zernike and orthogonal Fourier-Mellin moments. Pattern Recognition, 35, 143–154.

    Article  MATH  Google Scholar 

  6. Shih, J.-L., & Chen, L.-H. (2001). A new system for trademark segmentation and retrieval. Image and Vision Computing, 19(13), 1011–1018.

    Article  Google Scholar 

  7. Zhu, H., Shu, H., Zhou, J., Luo, L., & Coatrieux, J. L. (2007). Image analysis by discrete orthogonal dual Hahn moments. Pattern Recognition Letters, 28(13), 1688–1704.

    Article  Google Scholar 

  8. Mukundan, R., Ong, S. H., & Lee, P. A. (2001). Image analysis by Tchebichef moments. IEEE Transactions on Image Processing, 10(9), 1357–1364.

    Article  MATH  MathSciNet  Google Scholar 

  9. Yap, P. T., Paramesran, R., & Ong, S. H. (2003). Image analysis by Krawtchouk moments. IEEE Transactions on Image Processing, 12(11), 1367–1377.

    Article  MathSciNet  Google Scholar 

  10. Wee, C. Y., Paramesran, R., & Mukundan, R. (2008). Fast computation of geometric moments using a symmetric kernel. Pattern Recognition, 41(7), 2369–2380.

    Article  MATH  Google Scholar 

  11. Wee, C. Y., Paramesran, R., & Takeda, F. (2004). New computational methods for full and subset Zernike moments. Information Sciences, 159(3–4), 203–220.

    Article  MATH  MathSciNet  Google Scholar 

  12. Chong, C. W., Raveendran, P., & Mukundan, R. (2003). A comparative analysis of algorithms for fast computation of Zernike moments. Pattern Recognition, 36(3), 731–742.

    Article  MATH  MathSciNet  Google Scholar 

  13. Kim, H. S., & Lee, H. (2003). Invariant image watermark using Zernike moments. IEEE Transactions on Circuits and Systems for Video Technology, 13(8), 766–775.

    Article  Google Scholar 

  14. Prismall, S. P., Nixon, M. S., & Carter, J. N. (2002). On moving object reconstruction by moments. In 13 th British Machine Vision Conference, pp. 73–82, Cardiff.

  15. Amayeh, G., Bebis, G., Erol, A., Nicolescu, M. (2006). Peg-free hand shape verification using high order Zernike moments. In Proc. Conference on Computer Vision and Pattern Recognition Workshop, pp. 40.

  16. Hatamian, M. (1986). A real-time two-dimensional moment generating algorithm and its single chip implementation. IEEE Transactions on Acoustics, Speech, and Signal Processing, ASSP-34(3), 546–553.

    Article  Google Scholar 

  17. Wong, W.-H., & Siu, W.-C. (1999). Improved digital filter structure for the fast moments computation. Proceedings of the IEE Vision, Image and Signal Processing, 146(2), 73–79.

    Article  Google Scholar 

  18. Kotoulas, L., & Andreadis, I. (2004). Efficient hardware architectures for computation of image moments. Real-Time Imaging, 10(6), 371–378.

    Article  Google Scholar 

  19. Kotoulas, L., & Andreadis, I. (2005). Real-time computation of Zernike moments. IEEE Transactions on Circuits and Systems for Video Technology, 15(6), 801–809.

    Article  Google Scholar 

  20. Kotoulas, L., & Andreadis, I. (2006). Fast computation of Chebyshev moments. IEEE Transactions on Circuits and Systems for Video Technology, 6(7), 884–888.

    Article  Google Scholar 

  21. Al-Rawi, M. (2008). Fast Zernike moments. Journal of Real-Time Image Processing, 3, 89–96.

    Article  Google Scholar 

  22. Chang, K.-H., Paramesran, R., Asli, B. H. S., & Lim, C.-L. (2012). Efficient hardware accelerators for the computation of Tchebichef moments. IEEE Transactions on Circuits and Systems for Video Technology, 22(3), 414–425.

    Article  Google Scholar 

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Correspondence to Kah-Hyong Chang.

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Chang, KH., Paramesran, R. A Configurable Architecture for Fast Moments Computation. J Sign Process Syst 78, 179–186 (2015). https://doi.org/10.1007/s11265-013-0857-9

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  • DOI: https://doi.org/10.1007/s11265-013-0857-9

Keywords

  • Image processing
  • Moments
  • Digital filters
  • Real-time
  • Configurable
  • High-order
  • Field-Programmable Gate Array (FPGA)