Skip to main content

Advertisement

Springer Nature Link
Log in

A fast algorithm for computing moments of gray images based on NAM and extended shading approach

  • Research Article
  • Published:
Frontiers of Computer Science in China Aims and scope Submit manuscript

Abstract

Computing moments on images is very important in the fields of image processing and pattern recognition. The non-symmetry and anti-packing model (NAM) is a general pattern representation model that has been developed to help design some efficient image representation methods. In this paper, inspired by the idea of computing moments based on the S-Tree coding (STC) representation and by using the NAM and extended shading (NAMES) approach, we propose a fast algorithm for computing lower order moments based on the NAMES representation, which takes O(N) time where N is the number of NAM blocks. By taking three idiomatic standard gray images ‘Lena’, ‘F16’, and ‘Peppers’ in the field of image processing as typical test objects, and by comparing our proposed algorithm with the conventional algorithm and the popular STC representation algorithm for computing the lower order moments, the theoretical and experimental results presented in this paper show that the average execution time improvement ratios of the proposed NAMES approach over the STC approach, and also the conventional approach are 26.63%, and 82.57% respectively while maintaining the image quality.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

Explore related subjects

Discover the latest articles and news from researchers in related subjects, suggested using machine learning.

References

  1. Tanaka Y, Ikehara M, Nguyen T Q. Multiresolution image representation using combined 2-D and 1-D directional filter banks. IEEE Transactions on Image Processing, 2009, 18(2): 269–280

    Article  MathSciNet  Google Scholar 

  2. Guo J M, Wu M F. Improved block truncation coding based on the void-and-cluster dithering approach. IEEE Transactions on Image Processing, 2009, 18(1): 211–213

    Article  MathSciNet  Google Scholar 

  3. Yang E H, Wang L. Joint optimization of run-length coding, Huffman coding, and quantization table with complete baseline JPEG decoder compatibility. IEEE Transactions on Image Processing, 2009, 18(1): 63–74

    Article  MathSciNet  Google Scholar 

  4. Distasi R, Nappi M, Vitulano S. Image compression by B-tree triangular coding. IEEE Transactions on Communications, 1997, 45(9): 1095–1100

    Article  Google Scholar 

  5. Dejonge W, Scheuermann P, Schijf A. S +-Trees: an efficient structure for the representation of large pictures. Computer Vision and Image Understanding, 1994, 59(3): 265–280

    Article  Google Scholar 

  6. Foley J D, Dam A V, Feiner S K, Hughes J F. Computer Graphics, Principle, and Practice. 2nd ed. Reading: Addision-Wesley, 1990

    Google Scholar 

  7. Chung K L, Wu J G. Improved image compression using S-tree and shading approach. IEEE Transactions on Communications, 2000, 48(5): 748–751

    Article  Google Scholar 

  8. Chen C B, Zheng Y P, Sarem M. A novel non-symmetry and antipacking model for image representation. Chinese Journal of Electronics, 2009, 18(1): 89–94

    MathSciNet  Google Scholar 

  9. Zheng Y, Chen C. Study on a new algorithm for gray image representation. Chinese Journal of Computers, 2010, 33(12): 2397–2406

    MathSciNet  Google Scholar 

  10. Qiao Y, Wang W, Minematsu N, Liu J, Takeda M, Tang X. A theory of phase singularities for image representation and its applications to object tracking and image matching. IEEE Transactions on Image Processing, 2009, 18(10): 2153–2166

    Article  Google Scholar 

  11. Chung K L, Liu Y W, Yan W M. A hybrid gray image representation using spatial- and DCT-based approach with application to moment computation. Journal of Visual Communication and Image Representation, 2006, 17(6): 1209–1226

    Article  Google Scholar 

  12. Chung K L, Yan W M, Liao Z H. Fast computation of moments on compressed grey images using block representation. Real-Time Imaging, 2002, 8(2): 137–144

    Article  MATH  Google Scholar 

  13. Papakostas G A, Boutalis Y S, Karras D A, Mertzios B G. Fast numerically stable computation of orthogonal Fourier-Mellin moments. IET Computer Vision, 2007, 1(1): 11–16

    Article  MathSciNet  Google Scholar 

  14. Kotoulas L, Andreadis I. Fast computation of Chebyshev moments. IEEE Transactions on Circuits and Systems for Video Technology, 2006, 16(7): 884–888

    Article  Google Scholar 

  15. Kotoulas L, Andreadis I. Accurate calculation of image moments. IEEE Transactions on Image Processing, 2007, 16(8): 2028–2037

    Article  MathSciNet  Google Scholar 

  16. Pei S C, Liou L G. Using moments to acquire the motion parameters of a deformable object without correspondences. Image and Vision Computing, 1994, 12(8): 475–485

    Article  Google Scholar 

  17. Tsai W H. Moment-preserving thresholding: a new approach. Computer Vision Graphics and Image Processing, 1985, 29(3): 377–393

    Article  Google Scholar 

  18. Pei S C, Horng J H. A moment-based approach for deskewing rotationally symmetric shapes. IEEE Transactions on Image Processing, 1999, 8(12): 1831–1834

    Article  Google Scholar 

  19. Lin H, Si J, Abousleman G P. Orthogonal rotation-invariant moments for digital image processing. IEEE Transactions on Image Processing, 2008, 17(3): 272–282

    Article  MathSciNet  Google Scholar 

  20. Chung K, Chen P. An efficient algorithm for computing moments on a block representation of a grey-scale image. Pattern Recognition, 2005, 38(12): 2578–2586

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, South China University of Technology, Guangzhou, 510006, China

    Yunping Zheng

  2. School of Software Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China

    Mudar Sarem

Authors
  1. Yunping Zheng
    View author publications

    Search author on:PubMed Google Scholar

  2. Mudar Sarem
    View author publications

    Search author on:PubMed Google Scholar

Corresponding author

Correspondence to Yunping Zheng.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Zheng, Y., Sarem, M. A fast algorithm for computing moments of gray images based on NAM and extended shading approach. Front. Comput. Sci. China 5, 57–65 (2011). https://doi.org/10.1007/s11704-010-0337-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11704-010-0337-3

Keywords