Bidimensional Empirical Mode Decomposition Modified for Texture Analysis

  • J. C. Nunes
  • O. Niang
  • Y. Bouaoune
  • E. Delechelle
  • Ph. Bunel
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2749)

Abstract

This study introduces a new approach based on Bidimensional Empirical Mode Decomposition (BEMD) to extract texture features at multiple scales or spatial frequencies. Moreover, it can resolve the intrawave frequency modulation provided the frequency modulation. This decomposition, obtained by the bidimensional sifting process, plays an important role in the characterization of regions in textured images. The sifting process is realized using morphological operators to analyze the spatial frequencies and thanks to radial basis functions (RBF) for surface interpolation. We modified the original sifting algorithm to permit a pseudo bandpass decomposition of images by inserting scale criterion. Its effectiveness is demonstrated on synthetic and natural textures. In particular, we show that many different elements in textures can be extracted through the bidimensional empirical mode decomposition, which is fully unsupervised.

References

  1. [1]
    M. Tuceryan and A. K. Jain, “Texture analysis”, The Handbook of pattern Recognition and Computer Vision (2nd edition), by C. H. Chen, L. F. Pau, P. S. P. Wang (editors.), 207–248, World scientific Publishing Co., 1998.Google Scholar
  2. [2]
    R. Haralick, “Statistical and structural approaches to texture”, IEEE Proc., 67(5): 1979, 786–804.CrossRefGoogle Scholar
  3. [3]
    M. Tuceryan and A. K. Jain, “Texture Segmentation Using Voronoi Polygons,” IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. PAMI-12, pp. 211–216, February, 1990.CrossRefGoogle Scholar
  4. [4]
    C.C. Chen, J.S. Daponte, and M.D. Fox, “Fractal feature analysis and classification in medical imaging”, IEEE Transactions on Medical Imaging, 8, 133–142, 1989.CrossRefGoogle Scholar
  5. [5]
    B. S. Manjunath and R. Chellappa, “Unsupervised texture segmentation using Markov radom field models”, IEEE. Trans. Pattern Anal. Machine Intell., 13(5):478–482, May 1991.CrossRefGoogle Scholar
  6. [6]
    R. A. Peters II, “Morphological pseudo bandpass image decompositions”, Journal of Electronic Imaging, vol. 5, no 2, April 1996, 198–213.CrossRefGoogle Scholar
  7. [7]
    J. Krumm and S.A. Shafer, “Shape from Periodic Texture Using Spectrogram,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 284–289, 1992.Google Scholar
  8. [8]
    J.P. Havlicek, D.S. Harding, and A.C. Bovik, “Multidimensional quasieigenfunction approximations and multicomponent AM-FM models”, IEEE Trans. Image Proc., vol. 9, no. 2, pp. 227–242, February 2000.CrossRefGoogle Scholar
  9. [9]
    J. Hormigo and G. Cristóbal, “High Resolution Spectral Analysis of Images Using the Pseudo-Wigner Distribution”, IEEE Transactions on Signal Processing, vol. 46, no. 6, 1757–1763, June 1998.CrossRefGoogle Scholar
  10. [10]
    D. Dunn and W. E. Higgins, “Optimal Gabor filters for texture segmentation”, IEEE Trans. Image Proc., 4(7):947–964, july 1995CrossRefGoogle Scholar
  11. [11]
    M. Unser, “Texture classification and segmentation using wavelet frames”, IEEE Trans. Image Proc., 4(11):1549–1560, November 1995.CrossRefMathSciNetGoogle Scholar
  12. [12]
    T. Randen J.H. Husoy, “Filtering for texture classification: a comparative study”, IEEE Trans. Patt. Anal. Machine Intell., 21:291–310, 1999.CrossRefGoogle Scholar
  13. [13]
    N. E Huang and al., “The empirical mode decomposition and the Hilbert spectrum for non-linear and non-stationary time series analysis”, Proceedings of the Royal Society Lond. A, 454, 903–995, 1998.CrossRefGoogle Scholar
  14. [14]
    P.J. Oonincx; “Empirical mode decomposition: a new tool for S-wave detection”, CWI Reports of Probability, Networks and Algorithms (PNA) 2002, PNAR0203, ISSN 1386-3711Google Scholar
  15. [15]
    L. Vincent, “Morphological grayscale reconstruction in image analysis: applications and efficient algorithms”, IEEE Transactions on Image Processing, Vol. 2, No. 2, 176–201, April 1993.CrossRefGoogle Scholar
  16. [16]
    P. Soille, “Morphological Image Analysis: principles and applications”, Springer Verlag, 1999, 170–171.Google Scholar
  17. [17]
    J.C. Carr, W.R. Fright, and R.K. Beatson, “Surface interpolation with radial basis functions for medical imaging”, IEEE Trans. Med. Imag. vol. 16, pp. 96–107, janv. 1997.CrossRefGoogle Scholar
  18. [18]
    T. Blu, and M. Unser, “Wavelet, fractals and radial basis functions”, IEEE Trans. on Signal Processing. vol. 50(3), pp. 543–553, Mar. 2002.CrossRefMathSciNetGoogle Scholar
  19. [19]
    N. E Huang, Z. Shen and S. R. Long, “A new view of nonlinear water waves: the Hilbert spectrum”, Annu. Rev. Fluid. Mech., 1999, 31: 417–57.CrossRefMathSciNetGoogle Scholar
  20. [20]
    P. Brodatz, “Textures: a photographic album for artists and designers”, New York Dover publications, 1966.Google Scholar
  21. [21]
    J.C. Nunes, Y. Bouaoune, E. Deléchelle, S. Guyot, and Ph. Bunel. “Texture analysis based on the bidimensional empirical mode decomposition”. Journal of Machine Vision and Applications, (to appear), 2003.Google Scholar
  22. [22]
    J.C. Nunes, Y. Bouaoune, E. Deléchelle, O. Niang, and Ph. Bunel. “Image analysis by Bidimensional Empirical Mode Decomposition”. Image and Vision Computing Journal, (to appear), 2003.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • J. C. Nunes
    • 1
  • O. Niang
    • 1
  • Y. Bouaoune
    • 1
  • E. Delechelle
    • 1
  • Ph. Bunel
    • 1
  1. 1.LERISS Laboratoire d’Etude et de Recherche en Instrumentation, Signaux et SystèmesUniversité Paris XII-Val de MarneCreteil CedexFrance

Personalised recommendations