Morphological feature selection and neural classification

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.