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Proceedings Article

Automatic classification of 3D segmented CT data using data fusion and support vector machine

[+] Author Affiliations
Ahmad Osman, Ulf Hassler

Fraunhofer Institute for Integrated Circuits (Germany)

Valérie Kaftandjian

National Institute of Applied Sciences (France)

Proc. SPIE 8000, Tenth International Conference on Quality Control by Artificial Vision, 80000F (July 12, 2011); doi:10.1117/12.890038
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From Conference Volume 8000

  • Tenth International Conference on Quality Control by Artificial Vision
  • Jean-Charles Pinoli; Johan Debayle; Yann Gavet; Frédéric Gruy; Claude Lambert
  • Saint-Etienne, France | June 28, 2011

abstract

The three dimensional X-ray computed tomography (3D-CT) has proved its successful usage as inspection method in non destructive testing. The generated 3D volume using high efficiency reconstruction algorithms contains all the inner structures of the inspected part. Segmentation of this volume reveals suspicious regions which need to be classified into defects or false alarms. This paper deals with the classification step using data fusion theory and support vector machine. Results achieved are very promising and prove the effectiveness of the data fusion theory as a method to build stronger classifier.

© (2011) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Citation

Ahmad Osman ; Valérie Kaftandjian and Ulf Hassler
"Automatic classification of 3D segmented CT data using data fusion and support vector machine", Proc. SPIE 8000, Tenth International Conference on Quality Control by Artificial Vision, 80000F (July 12, 2011); doi:10.1117/12.890038; http://dx.doi.org/10.1117/12.890038


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