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Title

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

Author(s)

Barry, T.;  Nagarajah, C. Romesh

Abstract

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.

Publication type

Journal article

Research centre

Swinburne University of Technology. Faculty of Engineering and Industrial Sciences

Source

International Journal of Advanced Manufacturing Technology, Vol. 48, no. 5-8 (May 2010), pp. 815-821

Publication year

2010

Keyword(s)

Artificial neural networks;  Data processing;  Feature extraction;  Support vector machines;  Wheel identification

Publisher

Springer

ISSN

0268-3768

Publisher URL

http://dx.doi.org/10.1007/s00170-009-2313-3

Copyright

Copyright © Springer-Verlag London Limited 2009. The accepted manuscript of the paper is reproduced here in accordance with the copyright policy of the publisher. The definitive version of the publication is available from http://www.springer.com.

Additional information

The authors would like to thank the Ford Motor Company of Australia and the Australian Research Council for their support for this project through the linkage grant scheme.

Full text

Peer reviewed