Improvement of X-ray castings inspection reliability by using Dempster-Shafer data fusion theory

@article{Osman2011ImprovementOX,
  title={Improvement of X-ray castings inspection reliability by using Dempster-Shafer data fusion theory},
  author={Ahmad Osman and Val{\'e}rie Kaftandjian and Ulf Hassler},
  journal={Pattern Recognition Letters},
  year={2011},
  volume={32},
  pages={168-180}
}
The aim of this work is to improve the classification of defects in X-ray inspection by developing a new method based on Dempster-Shafer data fusion theory where measured features on the detected objects are considered as information sources. From the histogram of features values on a learning database of manually classified objects, an automatic procedure is proposed to define a set of mass functions for each feature. The spatial repartition of features is divided into regions of confidence… CONTINUE READING

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  • The best results were obtained employing the PNN and MLP networks with performances of 90 % and 81 % respectively on the test vectors, and 85 % and 94.5 % for the validation vectors.

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