On 3D Scanning, Reconstruction, Enhancement, and Segmentation of Logs

  • Katarina Flood
  • Per-Erik Danielsson
  • Maria Magnusson Seger
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2749)

Abstract

This paper presents novel results from an ongoing feasibility study of fully 3D X-ray scanning of Pinus Sylvestris (Scots Pine) logs. Logs are assumed to be translated through tw o iden tical and static cone beam systems with the beams rotated 90° relative to eachother, providing a dual set of 2D-projections. For reasons of both cost and speed, each 2D-detector in these tw o systems consists of a limited number of line detectors. The quality of the reconstructed images is far from perfect, due to sparse detector data and missing projection angles. In spite of this we show that by employing a shape- and direction discriminative technique based on second derivativ es, w e are able to enhance knot-like features in these data. In the enhanced images it is then possible to detect and localize the pith for each whorl of knots, and subsequently also to perform a full segmentation of the knots in the heartwood.

Keywords

Derivative Operator Line Detector Streak Artifact Radial Projection Scanning Geometry 
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.
    L.G. Johansson and Å. Liljeblad, Some applications withing the project “Quality simulation of sawn logs”, Trätek, Sweden, 1988, I 880605Google Scholar
  2. 2.
    Stig Grundberg. An X-ray log-scanner-a tool for control of the sawmill process. PhD-thesis, Luleå University, Sweden, 1999.Google Scholar
  3. 3.
    Maria Magnusson-Seger, Per-Erik Danielsson. Linear Cone-Beam Tomography for Logs, In the 4th International Conference on Image Processing and Scanning of Wood, 2000.Google Scholar
  4. 4.
    Per-Erik Danielsson, Qingfen Lin, Qin-Zhong Ye. “Efficient detection of second degree variations in 2D and 3D images”. Journal of Visual Computations and Visual Representation, 2001.Google Scholar
  5. 5.
    A. C. Kak and M. Slaney Principles of Computerized Tomographic Imaging IEEE Press, New York, 1988.MATHGoogle Scholar
  6. 6.
    T. Koller From Data to Information: Segmentation, Description, and Analysis of the Cerebral Vascularity PhD-thesis, Swiss Federal Inst. of Techn., Zurich, 1994.Google Scholar
  7. 7.
    K. Flood and P.E. Danielsson Detection of knots in 3D log scanning, Proc. of 2001 Symposium in Image Analysis, 2001.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Katarina Flood
    • 1
  • Per-Erik Danielsson
    • 1
  • Maria Magnusson Seger
    • 1
  1. 1.Computer Vision LaboratoryLinköping UniversityLinköpingSweden

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