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Communication Dans Un Congrès Année : 2011

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

Résumé

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.
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Dates et versions

hal-00904876 , version 1 (15-11-2013)

Identifiants

  • HAL Id : hal-00904876 , version 1

Citer

Ahmad Osman, Valerie Kaftandjian, Ulf Hassler. Automatic Classification of 3D segmented CT data using data fusion and support vector machine. Quality Control and Artificial Vision QCAV'2011, Jun 2011, saint etienne, France. pp.00. ⟨hal-00904876⟩
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