Local Orientation Estimation in Corrupted Images

  • Franck Michelet
  • Jean-Pierre Da Costa
  • Pierre Baylou
  • Christian Germain
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4153)


IRON is a low level operator dedicated to the estimation of single and multiple local orientations in images. Previous works have shown that IRON is more accurate and more selective than Gabor and Steerable filters, for textures corrupted with Gaussian noise. In this paper, we propose two new features. The first one is dedicated to the estimation of orientation in images damaged by impulsive noise. The second one applies when images are corrupted with an amplitude modulation, such as an inhomogeneous lighting.


Image Processing Orientation estimation Anisotropy Impulse noise Amplitude modulation IRON 


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  1. 1.
    Bigün, J., Bigün, T., Nilsson, K.: Recognition by symmetry derivatives and the generalized structure tensor. IEEE Transactions on PAMI 26(12), 1590–1605 (2004)Google Scholar
  2. 2.
    Bigün, J., du Buf, J.H.: N-folded symmetries by complex moments in Gabor space and their application to unsupervised texture segmentation. IEEE Trans. on PAMI 16(1), 80–87 (1994)Google Scholar
  3. 3.
    Chen, J., Sato, Y., Tamura, S.: Orientation Space Filtering for Multiple Line Segmentation. In: Proc. of IEEE Conference on Computer Vision and Pattern Recognition, California (1998)Google Scholar
  4. 4.
    Chetverikov, D., Hanbury, A.: Finding defects in texture using regularity and local orientation. Pattern Recognition 35, 2165–2180 (2002)zbMATHCrossRefGoogle Scholar
  5. 5.
    Deriche, R.: Fast Algorithms for Low-Level Vision. IEEE Transactions on PAMI 12(1), 78–81 (1990)Google Scholar
  6. 6.
    Freeman, W.T., Adelson, E.H.: The design and use of steerable filters. IEEE Trans. on PAMI 13(9), 891–906 (1991)Google Scholar
  7. 7.
    Knutsson, H.: Representing Local Structure Using Tensors. In: Proceedings of Scandinavian Conference on Image Analysis, Oulu, Finland (1989)Google Scholar
  8. 8.
    Le Pouliquen, F., Da Costa, J.P., Germain, C., Baylou, P.: A new adaptive framework for unbiased orientation estimation. Pattern Recognition 38, 2032–2046 (2005)CrossRefGoogle Scholar
  9. 9.
    Michelet, F., Germain, C., Baylou, P.: Local Multiple Orientation Estimation: Isotropic and Recursive Oriented Network. In: Proc. of ICPR 2004, Cambridge, UK (2004)Google Scholar
  10. 10.
    Perona, P.: Deformable kernels for early vision. IEEE Transactions on PAMI 17(5), 488–499 (1995)Google Scholar
  11. 11.
    Perona, P., Malik, J.: Scale-space and edge detection using anisotropic diffusion. PAMI 12(7), 629–639 (1990)Google Scholar
  12. 12.
    Rao, A.R.: A Taxonomy for Texture Description and Identification. Springer, Heidelberg (1990)zbMATHGoogle Scholar
  13. 13.
    Terebes, R., Lavialle, O., Baylou, P., Borda, M.: Orientation driven diffusion. Acta Technica Napocensis-Electronics and Telecommunications, Cluj-Napoca 42(2), 20–24 (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Franck Michelet
    • 1
  • Jean-Pierre Da Costa
    • 1
  • Pierre Baylou
    • 1
  • Christian Germain
    • 1
  1. 1.LAPS – Signal & Image Team, UMR N°5131 CNRSBordeaux I University – ENSEIRB – ENITABTalenceFrance

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