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Modeling Knot Geometry in Norway Spruce from Industrial CT Images

  • Jean-Philippe Andreu
  • Alfred Rinnhofer
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2749)

Abstract

For spruce (Picea abies (L.) Karst.), as with most other species, the value of a log without major defects like decay or compression wood is to great extent determined by its knot structure. That is the reason why sawyers, aiming at the optimal utilization of a log, were naturally interested in the knots, their number, position, diameter and length. X-ray computer tomography (CT) scanning is able to detect internal density variations like knots. To use this information for a computer based optimization process, it is necessary to model different defects by geometric descriptions which are easy to handle. The purpose of this study is to show how to model knots in spruce logs from non-destructive measurements using industrial CT and automatic image analysis processes. A comparison between the results of these methods and the results of a destructive method gives an experimental evaluation of our approach.

Keywords

Computer Tomography False Alarm Rate Symmetry Plane Annual Ring Compression Wood 
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.

References

  1. 1.
    Andreu, J.Ph. and Rinnhofer, A.: “Enhancement of annual rings on industrial CT images of logs”, Proc. ICPR2002, Vol. III (2002), 261–264.Google Scholar
  2. 2.
    Aune, J.: “An X-ray Log-Scanner for Sawmills”, Proc. of the 2nd Int. Workshop on Scanning Technology and Image Processing on Wood (1995), 51–65.Google Scholar
  3. 3.
    Brdicko, J., Orbay, L. and Tong, Q.: “Using External and Internal Log Characteristics for Log Breakdown Optimization”, Proc. of the 5th Int. Conference on Image Processing and Scanning of Wood (2003), 115–124.Google Scholar
  4. 4.
    Chang, J.-H., Fan K.-C. and Chang Y.-L.: “Multi-modal gray-level Histogram Modeling and Decomposition”, Image and Vision Computing, 20 (2002) 203–216.CrossRefGoogle Scholar
  5. 5.
    Chang, S. J.: “Hardwood Sawing Optimization based on CT Scanning of Internal Defects”, Proc. of the 5th Int. Conference on Image Processing and Scanning of Wood (2003), 125–130.Google Scholar
  6. 6.
    Grundberg, S.: “Scanning for internal defects in logs”, Ph.D. thesis, Lulea Univ. of Tech., (1994) ISSN 0280-8242.Google Scholar
  7. 7.
    Hodges, D.G., Anderson, W.C. and Mac Millin, C.W.: “The economic potential of CT scanners for hardwood sawmills”, Forest Products Journal 40(3), (1990) 65–69.Google Scholar
  8. 8.
    Oja, J.: “X-ray Measurement of Properties of Saw Logs”, Ph.D. thesis, Lulea Univ. of Tech., (1999) ISSN 1402-1544.Google Scholar
  9. 9.
    Samson, M.: “Modeling of knots in logs”, Wood Science and Tech., 27 (1993), 429–437.Google Scholar
  10. 10.
    Schmoldt D.L., Scheinman E., Rinnhofer A. and Occeña L.G., “Internal log scanning: Research to reality”, Proc. of the Hardwood Research Council Meeting, (2000).Google Scholar
  11. 11.
    Skatter, S.: “Non destructive determination of the external shape and the internal structure of logs-Possible technologies for use in the sawmills”, Ph.D. thesis, Agricultural Univ. of Norway, ISBN 82-575-0352-5 (1998).Google Scholar
  12. 12.
    VRML97, The Virtual Reality Modeling Language, http://www.web3d.org/Specifications/Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Jean-Philippe Andreu
    • 1
  • Alfred Rinnhofer
    • 1
  1. 1.JOANNEUM RESEARCHInstitute of Digital Image ProcessingGrazAustria

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