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Proceedings Paper

Automatic classification of 3D segmented CT data using data fusion and support vector machine
Author(s): Ahmad Osman; Valérie Kaftandjian; Ulf Hassler
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Paper 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.

Paper Details

Date Published: 12 July 2011
PDF: 10 pages
Proc. SPIE 8000, Tenth International Conference on Quality Control by Artificial Vision, 80000F (12 July 2011); doi: 10.1117/12.890038
Show Author Affiliations
Ahmad Osman, Fraunhofer Institute for Integrated Circuits (Germany)
Valérie Kaftandjian, National Institute of Applied Sciences (France)
Ulf Hassler, Fraunhofer Institute for Integrated Circuits (Germany)


Published in SPIE Proceedings Vol. 8000:
Tenth International Conference on Quality Control by Artificial Vision
Jean-Charles Pinoli; Johan Debayle; Yann Gavet; Frédéric Gruy; Claude Lambert, Editor(s)

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