Object recognition in industrial environments using support vector machines and artificial neural networks

This paper presents a comparison between Artificial Neural Networks and Support Vector Machines in the application of classifying automotive wheels in an industrial environment. Performance of these two approaches over a range of classifier parameters on a dataset pre-processed in multiple ways has been evaluated and the results analysed. Results indicate that the best performance is obtained using a Support Vector Machine approach incorporating a linear kernel.