Journal of Engineering Science and Technology Review (Jan 2009)

Morphological feature selection and neural classification

  • G. Tsirigotis,
  • D. Lefkaditis

Journal volume & issue
Vol. 2, no. 1
pp. 151 – 156

Abstract

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This paper presents the development procedure of the feature extraction and classification module of an intelligent sortingsystem for electronic components. This system was designed as a prototype to recognise six types of electronic componentsfor the needs of the educational electronics laboratories of the Kavala Institute of Technology. A list of features that describethe morphology of the outline of the components was extracted from the images. Two feature selection strategies were examinedfor the production of a powerful yet concise feature vector. These were correlation analysis and an implementationof support vector machines. Moreover, two types of neural classifiers were considered. The multilayer perceptron trainedwith the back-propagation algorithm and the radial basis function network trained with the K-means method. The best resultswere obtained with the combination of SVMs with MLPs, which successfully recognised 92.3% of the cases.

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