Comparison of neural and minimum distance classifiers in wood veneer defect identification

摘要

The minimum distance classifier (MDC) is an example of a commonly used 'conventional' classifier. Whilst there has been a focus on using neural networks for the advantages that they offer, few researchers report direct comparison with conventional classifiers which typically have the advantage of being simpler. This paper provides such a comparison. The results show that the MDC does not perform as well as a neural network when applied to an industrial problem, namely that of identifying defects on wood veneer.