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Fast numerically stable computation of orthogonal Fourier–Mellin moments

Fast numerically stable computation of orthogonal Fourier–Mellin moments

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An efficient algorithm for the computation of the orthogonal Fourier–Mellin moments (OFMMs) is presented. The proposed method computes the fractional parts of the orthogonal polynomials, which consist of fractional terms, recursively, by eliminating the number of factorial calculations. The recursive computation of the fractional terms makes the overall computation of the OFMMs a very fast procedure in comparison with the conventional direct method. Actually, the computational complexity of the proposed method is linear O(p) in multiplications, with p being the moment order, while the corresponding complexity of the direct method is O(p2). Moreover, this recursive algorithm has better numerical behaviour, as it arrives at an overflow situation much later than the original one and does not introduce any finite precision errors. These are the two major advantages of the algorithm introduced in the current work, establishing the computation of the OFMMs to a very high order as a quite easy and achievable task. Appropriate simulations on images of different sizes justify the superiority of the proposed algorithm over the conventional algorithm currently used.

References

    1. 1)
      • Chong, C.-W., Mukundan, R., Raveendran, P.: `An efficient algorithm for fast computation of pseudo-Zernike moments', Proc. Int. Conf. Image and Vision Computing, 2001, New Zealand, p. 237–242.
    2. 2)
      • E.C. Kintner . On the mathematical properties of the Zernike polynomials. Opt. Acta , 8 , 679 - 680
    3. 3)
    4. 4)
    5. 5)
      • Papakostas, G.A., Boutalis, Y.S., Karras, D.A., Mertzios, B.G.: `On the reconstruction performance of compressed orthogonal moments', Proc. Int. Conf. Informatics in Control, Automation and Robotics, Setubal, Portugal, p. 468–474.
    6. 6)
      • G.A. Papakostas , D.A. Karras , B.G. Mertzios , Y.S. Boutalis . An efficient feature extraction methodology for computer vision applications using wavelet compressed Zernike moments. ICGST Int. J. Graphics Vis. Image Process. , 5 - 15
    7. 7)
      • Terrillon, J.C., McReynolds, D., Sadek, M., Sheng, Y., Akamatsu, S.: `Invariant neural-network based face detection with orthogonal Fourier–Mellin moments', Proc. Int. Conf. Pattern Recognition, 2000.
    8. 8)
    9. 9)
    10. 10)
      • A. Khotanzad , J.-H. Lu . Classification of invariant image representations using a neural network. IEEE Trans. Acoust. Signal Process. , 6 , 1028 - 1038
    11. 11)
      • M.R. Teague . Image analysis via the general theory of moments. J. Opt. Soc. Am. , 8 , 920 - 930
    12. 12)
      • Papakostas, G.A., Boutalis, Y.S., Mertzios, B.G.: `Evolutionary selection of Zernike moment sets in image processing', Proc. Int. Workshop Systems, Signals and Image Processing, September 2003, Prague, Czech Republic.
    13. 13)
      • S.O. Belkasim , M. Ahmadi , M. Shridhar . Efficient algorithm for fast computation of Zernike moments. J. Franklin Inst. , 4 , 577 - 581
    14. 14)
    15. 15)
      • R. Mukundan , K.R. Ramakrishnan . (1998) Moment functions in image analysis.
    16. 16)
    17. 17)
      • A. Prata , W.V.T. Rusch . Algorithm for computation of Zernike polynomials expansion coefficients. Appl. Opt. , 749 - 754
    18. 18)
      • C.-H. The , R.T. Chin . On image analysis by the methods of moments. IEEE Trans. Pattern Anal. Mach. Intell. , 4 , 496 - 513
    19. 19)
      • Y. Sheng , L. Shen . Orthogonal Fourier–Mellin moments for invariant pattern recognition. J. Opt. Soc. Am. , 1748 - 1757
    20. 20)
    21. 21)
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