Applicability of Motion Estimation Algorithms for an Automatic Detection of Spiral Grain in CT Cross-Section Images of Logs

  • Karl Entacher
  • Christian Lenz
  • Martin Seidel
  • Andreas Uhl
  • Rudolf Weiglmaier
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4673)


Techniques for an automatic detection of spiral grain in cross-section CT images of logs are proposed and evaluated on sets of natural and artificial cross-section images. Explicit analysis of global rotation, block matching, and optical flow techniques are compared. Experimental results seem to indicate that spiral grain in fact cannot be modeled by a circular motion of luminance values in gray scale images.


Mean Square Error Motion Vector Automatic Detection Block Match Motion Estimation Algorithm 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Forest Products Laboratory. Wood handbook - Wood as an engineering material. Gen.Tech.Rep.FPL-GTR-113. Madison, WI: U.S. Department of Agriculture, Forest Service (March 2007) (1999), Online Version
  2. 2.
    Harris, J.M.: Spiral grain and wave phenomena in wood Formation. Springer, Heidelberg (1989)Google Scholar
  3. 3.
    Nyström, J.: Automatic measurement of compression wood and spiral grain for the predection of distortion in sawn wood products. PhD thesis, LuleåUniversity of Technology (2002), available from:
  4. 4.
    Grönlund, A., Oja, J., Grundberg, S., Nyström, J., Ekevad M.: Process control based on measurement of spiral grain and heartwood content. Draft to be presented at The 18th International Wood Machining Seminar, Vancouver, Canada (2007)Google Scholar
  5. 5.
    Gindl, W., Teischinger, A.: The potential of VIS- and NIR-spectroscopy for the nondestructive evaluation of grain-angle in wood. Wood and Fiber Science 34, 651–656 (2002)Google Scholar
  6. 6.
    Sepúlveda, P., Oja, J., Grönlund: Predicting spiral grain by computed tomography of norway spruce. Journal of Wood Science 48, 479–483 (2002)CrossRefGoogle Scholar
  7. 7.
    Ekevad, M.: Method to compute fiber directions in wood from computed tomography images. Journal of Wood Science 50, 41–46 (2004)CrossRefGoogle Scholar
  8. 8.
    Teischinger, A., Patzelt, M.: XXL-Wod. Berichte aus Energie- und Umweltforschung 27/2006. BMVIT, Vienna, Austria. (March 2007) (2006), Online Version:
  9. 9.
    Teischinger, A., Buksnowitz, C., Müller, U.: Wood properties of old growth spruce and their technological potential. In: Kurjatko, S., Kudela, J., Lagaňa, R. (eds.) Proceedings of the 5th Symposium Wood Strucöture and Properties 2006, pp. 413–416. Arbora Publishers, Zvolen, Slovakia (2006)Google Scholar
  10. 10.
    Furht, B.: Motion Estimation Algorithms for Video Compression. Kluwer Academic Publishers, Boston, MA (1997)Google Scholar
  11. 11.
    Horn, B.K.P., Schunck, B.G.: Determining optical flow. Artificial Intelligence 17, 185–203 (1981)CrossRefGoogle Scholar
  12. 12.
    Lucas, B.D., Kanade, T.: An iterative image registration technique with an application to stereo vision. In: Proceedings of Imaging understanding workshop, pp. 121–130 (1981)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Karl Entacher
    • 1
  • Christian Lenz
    • 2
  • Martin Seidel
    • 2
  • Andreas Uhl
    • 2
  • Rudolf Weiglmaier
    • 2
  1. 1.Salzburg University of Applied SciencesAustria
  2. 2.Department of Computer Sciences, Salzburg UniversityAustria

Personalised recommendations