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.
Chapter PDF
References
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.
R. Haralick, “Statistical and structural approaches to texture”, IEEE Proc., 67(5): 1979, 786–804.
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.
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.
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.
R. A. Peters II, “Morphological pseudo bandpass image decompositions”, Journal of Electronic Imaging, vol. 5, no 2, April 1996, 198–213.
J. Krumm and S.A. Shafer, “Shape from Periodic Texture Using Spectrogram,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 284–289, 1992.
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.
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.
D. Dunn and W. E. Higgins, “Optimal Gabor filters for texture segmentation”, IEEE Trans. Image Proc., 4(7):947–964, july 1995
M. Unser, “Texture classification and segmentation using wavelet frames”, IEEE Trans. Image Proc., 4(11):1549–1560, November 1995.
T. Randen J.H. Husoy, “Filtering for texture classification: a comparative study”, IEEE Trans. Patt. Anal. Machine Intell., 21:291–310, 1999.
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.
P.J. Oonincx; “Empirical mode decomposition: a new tool for S-wave detection”, CWI Reports of Probability, Networks and Algorithms (PNA) 2002, PNAR0203, ISSN 1386-3711
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.
P. Soille, “Morphological Image Analysis: principles and applications”, Springer Verlag, 1999, 170–171.
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.
T. Blu, and M. Unser, “Wavelet, fractals and radial basis functions”, IEEE Trans. on Signal Processing. vol. 50(3), pp. 543–553, Mar. 2002.
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.
P. Brodatz, “Textures: a photographic album for artists and designers”, New York Dover publications, 1966.
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.
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.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Nunes, J.C., Niang, O., Bouaoune, Y., Delechelle, E., Bunel, P. (2003). Bidimensional Empirical Mode Decomposition Modified for Texture Analysis. In: Bigun, J., Gustavsson, T. (eds) Image Analysis. SCIA 2003. Lecture Notes in Computer Science, vol 2749. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45103-X_24
Download citation
DOI: https://doi.org/10.1007/3-540-45103-X_24
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-40601-3
Online ISBN: 978-3-540-45103-7
eBook Packages: Springer Book Archive