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Advances in Rotation-Invariant Texture Analysis

  • Alfonso Estudillo-Romero
  • Boris Escalante-Ramirez
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5856)

Abstract

Robust rotation invariance has been a matter of great interest in many applications which use low-level features such as textures. In this paper, we propose a method to analyze and capture visual patterns from textures regardless their orientation. In order to achieve rotation invariance, visual texture patterns are locally described as one-dimensional patterns by appropriately steering the Cartesian Hermite coefficients. Experiments with two datasets from the Brodatz album were performed to evaluate orientation invariance. High average precision and recall rates were achieved by the proposed method.

Keywords

Texture analysis steered Hermite transform image retrieval 

References

  1. 1.
    Davis, L.S.: Polarograms: A new tool for image texture analysis. Pattern Recognition 13(3), 219–223 (1981)CrossRefGoogle Scholar
  2. 2.
    Kashyap, R.L., Khotanzad, A.: A model-based method for rotation invariant texture classification. IEEE Trans. PAMI 8(4), 472–481 (1986)Google Scholar
  3. 3.
    Cohen, F., Fan, Z., Patel, M.: Classification of rotated and scaled textured images using gaussian markov random field models. IEEE Trans. PAMI 13(2), 192–202 (1991)Google Scholar
  4. 4.
    Pun, C.M., Lee, M.C.: Log-polar wavelet energy signatures for rotation and scale invariant texture classification. IEEE Trans. PAMI 25(5), 590–603 (2003)Google Scholar
  5. 5.
    Jafari-Khouzani, K., Soltanian-Zadeh, H.: Rotation-invariant multiresolution texture analysis using radon and wavelet transforms. IEEE Transactions on Image Processing 14(6), 783–795 (2005)CrossRefMathSciNetGoogle Scholar
  6. 6.
    Rallabandi, V.R., Rallabandi, V.S.: Rotation-invariant texture retrieval using wavelet-based hidden markov trees. Signal Processing 88(10), 2593–2598 (2008)zbMATHCrossRefGoogle Scholar
  7. 7.
    Montoya-Zegarra, J.A., Papa, J.P., Leite, N.J., da Silva Torres, R., Falco, A.X.: Learning how to extract rotation-invariant and scale-invariant features from texture images. EURASIP Journal on Advances in Signal Processing 691924 (2008)Google Scholar
  8. 8.
    Han, J., Ma, K.K.: Rotation-invariant and scale-invariant gabor features for texture image retrieval. Image and Vision Computing 25(9), 1474–1481 (2007)CrossRefMathSciNetGoogle Scholar
  9. 9.
    Martens, J.B.: The hermite transform-theory. IEEE Transactions on Acoustics, Speech and Signal Processing 38(9), 1595–1606 (1990)zbMATHCrossRefGoogle Scholar
  10. 10.
    van Dijk, A.M., Martens, J.B.: Image representation and compression with steered hermite transforms. Signal Processing 56(1), 1–16 (1997)zbMATHCrossRefGoogle Scholar
  11. 11.
    Silvan-Cardenas, J., Escalante-Ramirez, B.: The multiscale hermite transform for local orientation analysis. IEEE Transactions on Image Processing 15(5), 1236–1253 (2006)CrossRefGoogle Scholar
  12. 12.
    Freeman, W., Adelson, E.: The design and use of steerable filters. IEEE Trans. PAMI 13(9), 891–906 (1991)Google Scholar
  13. 13.
    Martens, J.B.: The Hermite transform: a survey. EURASIP Journal of Applied Signal Processing 26145 (2006)Google Scholar
  14. 14.
    Brodatz, P.: Texture: a photographic album for artists and designers. Dover, New York (1966)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Alfonso Estudillo-Romero
    • 1
  • Boris Escalante-Ramirez
    • 1
  1. 1.Fac. de Ingenieria, Edif. de Posgrado e Investigacion, Ciudad UniversitariaUniversidad Nacional Autonoma de MexicoMexicoMexico

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