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3D Reconstruction from Sparse Radiographic Data

  • James SachsJr.
  • Ken Sauer
Part of the Applied and Numerical Harmonic Analysis book series (ANHA)

Abstract

Nondestructive evaluation of materials through X-ray and -y-ray radiography has long been achieved by inferring three-dimensional structure from exposed films. Multiple views with varying positions of radioactive sources and the film have the potential for direct three-dimensional tomographic reconstruction for more detailed diagnosis of material flaws. The data are sufficiently sparse, however, to leave the reconstruction badly under-specified, requiring regularization and/ or constraints to achieve meaningful results. In this chapter we discuss and illustrate the application of Bayesian binary 3D tomographic reconstruction to radiographs, including the several non-idealities frequently encountered in the field.

Keywords

Photon Count Markov Random Field Posteriori Probability Tomographic Reconstruction Center Slice 
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.

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Copyright information

© Springer Science+Business Media New York 1999

Authors and Affiliations

  • James SachsJr.
    • 1
  • Ken Sauer
    • 2
  1. 1.Ford Motor Corporation, Product Development CenterPDC MD-331DearbornUSA
  2. 2.Department of Electrical EngineeringUniversity of Notre DameNotre DameUSA

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