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3D Reconstruction of Volume Defects from Few X-Ray Images

  • C. Lehr
  • C. -E. Liedtke
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1689)

Abstract

In nondestructive testing for quality control of industrial objects the standard X-ray analysis produces a 2D projection of the 3D objects. Defects can be detected but cannot be localized in 3D position, size and shape. Tomographic testing equipment turns frequently out to be too costly and time consuming for many applications. Here a new approach for 3D reconstruction is suggested using standard X-ray equipment without costly positioning equipment. The new approach requires only a small number of X-ray views from different directions in order to reduce the image acquisition time.

The geometric and photometric imaging properties of the system are calibrated using different calibration patterns. The parameters of a CAHV camera model are obtained for each view permitting the exact registration of the acquired images. The efficiency of the 3D reconstruction algorithm has been increased by limiting the reconstruction to regions of interest around the defects. This requires an automated segmentation. The 3D reconstruction of the defects is performed with an iterative procedure. Regularization of the reconstruction problem is achieved on the basis of the maximum entropy principle. The reliability and robustness of the method has been tested on simulated and real data.

Keywords

X-ray tomography calibration 

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References

  1. 1.
    Herman, G.T.: Image Reconstruction from Projections. The fundamentals of Computerized Tomography. Academic Press, 1980.Google Scholar
  2. 2.
    Kak, A. C.; Slaney, M.: Principles of Computerized Tomographic Imaging. New York, IEEE Press, 1988.zbMATHGoogle Scholar
  3. 3.
    Robert, N.; Peyrin, F.; Yaffe, M.J.: Blodd vessel reconstruction from a limited number of cone-beam projections: Application to cerebral blood vessel projections and to an excise animal heart. Annual SPIE Conference on Machine Vision Applications in Industrial Inspection, San Jose, Cal., 1995, SPIE proc. series 2423.Google Scholar
  4. 4.
    Stegemann, D.: Zerströungsfreie Prüfverfahren: Radiographie und Radioskopie. B.G. Teubner, Stutgart, 1995.Google Scholar
  5. 5.
    Djafari, A.M.: Shape Reconstruction in X-Ray Tomography. Image Reconstruction and Restoration 2, San Diego, California, 1997, SPIE Vol. 3170.Google Scholar
  6. 6.
    Milanfar, P.; Karl, W.C.; Willesky, A.S.: Reconstructing Binary Polygonal Objects from Projections: A Statistical View. Graphical Models and Image Processing, Vol 56,5, 1994.Google Scholar
  7. 7.
    Retraint, F.; Peyrin, F.; Dinten, J.M.: Three-Dimensional Regularized Binary Image Reconstruction From Two-Dimensional Projections Using a Randomized ICM Algorithmus. Int. J. of Imag. Systems Technology, pp. 135–146, Vol 9, 1998.CrossRefGoogle Scholar
  8. 8.
    Yakimovski, Y.; Cunningham, R.: A System for Extraction Three-Dimensional Measurement from a Stereo Pair of TV Cameras. Intern. Journal on Computer Graphics and Image Processing. Vol 7, S. 195–210, 1978.CrossRefGoogle Scholar
  9. 9.
    Lehr, C.; Feiste, K.; Stegeman, D.; Liedtke, C.-E.: Three dimensional defect analysis using stereoradioscopy based on camera modelling. 7. ECNDT Conference, Copenhagen, 1998.Google Scholar
  10. 10.
    Tsai, R.Y.: A versatile camera calibration technique for high-accuracy 3D machine vision meterology using off-the-shell tv cameras and lenses. IEEE J.Robotics Automation, Vol. RA-3(4), August 1988, S. 323–344.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1999

Authors and Affiliations

  • C. Lehr
    • 1
  • C. -E. Liedtke
    • 1
  1. 1.Institut für Theoretische Nachrichtentechnik und Informationsverarbeitung Division: Automatic Image InterpretationUniversität HannoverHannoverGermany

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