An Evaluation of Scale and Noise Sensitivity of Fibre Orientation Estimation in Volume Images

  • Maria Axelsson
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5716)


Fibre orientation influences many important properties of fibre-based materials, for example, strength and stiffness. Fibre orientation and the orientation anisotropy in paper and other wood fibre-based materials have previously been estimated using two-dimensional images. Recently, we presented a method for estimating the three-dimensional fibre orientation in volume images based on local orientation estimates. Here, we present an evaluation of the method with respect to scale and noise sensitivity. The evaluation is performed for both tubular and solid fibres. We also present a new method for automatic scale selection for solid fibres. The method is based on a segmentation of the fibres that also provides an estimate of the fibre dimension distribution in an image. The results show that the fibre orientation estimation performs well both in noisy images and at different scales. The presented results can be used as a guide to select appropriate parameters for the method when it is applied to real data. The applicability of the fibre orientation estimation to fibre-based materials with solid fibres is demonstrated for a volume image of a press felt acquired with X-ray microtomography.


Volume Image Structure Tensor Orientation Estimation Parameter Setup Noise Sensitivity 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Erkkilä, A.-L., Pakarinen, P., Odell, M.: Sheet forming studies using layered orientation analysis. Pulp and Paper Canada 99(1), 81–85 (1998)Google Scholar
  2. 2.
    Hirn, U., Bauer, W.: Evaluating an improved method to determine layered fibre orientation by sheet splitting. In: 61st Appita Annual Conference and Exhibition, Gold Coast, Australia, May 6–9, pp. 71–79 (2007)Google Scholar
  3. 3.
    Samuelsen, E., Gregersen, Ø., Houen, P.J., Helle, T., Raven, C., Snigirev, A.: Three-dimensional imaging of paper by use of synchroton X-Ray microtomography. Journal of Pulp and Paper Science 27(2), 50–53 (2001)Google Scholar
  4. 4.
    Robb, K., Wirjandi, O., Schladitz, K.: Fiber orientation estimation from 3D image data: Practical algorithms, visualization, and interpretation. In: Proceedings of 7th International Conference on Hybrid Intelligent Systems, September 2007, pp. 320–325 (2007)Google Scholar
  5. 5.
    Axelsson, M.: Estimating 3D Fibre Orientation in Volume Images. In: Proceedings of 19th International Conference on Pattern Recognition (2008)Google Scholar
  6. 6.
    Granlund, G.H., Knutsson, H.: Signal Processing for Computer Vision. Kluwer Academic Publishers, Dordrecht (1995)CrossRefGoogle Scholar
  7. 7.
    Tomasi, C., Manduchi, R.: Bilateral filtering for gray and color images. In: IEEE International Conference on Computer Vision 1998, pp. 836–846 (1998)Google Scholar
  8. 8.
    Canny, J.F.: A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 8(6), 679–698 (1986)CrossRefGoogle Scholar
  9. 9.
    Vincent, L., Soille, P.: Watersheds in digital spaces: An efficient algorithm based on immersion simulations. IEEE Transactions on Pattern Analysis and Machine Intelligence 13(6), 583–598 (1991)CrossRefGoogle Scholar
  10. 10.
    Borgefors, G.: On Digital Distance Transforms in Three Dimensions. Computer Vision and Image Understanding 64(3), 368–376 (1996)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Maria Axelsson
    • 1
  1. 1.Centre for Image AnalysisSwedish University of Agricultural SciencesUppsalaSweden

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