Finding the best classifier for evaluating cork quality in an industrial environment

Abstract

A classification problem existing in the cork industry is the cork stopper/disk classification according to their quality using a visual inspection system. Cork is a natural and heterogeneous material, therefore, its automatic classification (usually, seven different quality classes exist) is very difficult. In this work, we study the use of different classifiers to solve this problem. The classifiers, which we present here, work with several quality discriminators (features), that we think could influence cork quality. These discriminators (features) have been checked and evaluated before being used by the different classifiers that will be exposed here. In this chapter we attempt to evaluate the performance of a total of 4 different cork quality-based classifiers in order to conclude which of them is the most appropriate for this industry, and therefore, obtains the best cork classification results. In conclusion, our experiments show that the Euclidean classifier is the one which obtains the best results in this application field. © 2008 Springer-Verlag Berlin Heidelberg.