A fuzzy clustering approach on the classification of non uniform cosmetic defects

In this paper a fuzzy clustering approach for the classification of cosmetic defects is presented. The paper investigates the solution of this classification problem with the Gustafson-Kessel (GK), and Geth-Geva (GG) with Abonyi-Szeifert (AS) fuzzy algorithms. The clustering process is achieved on multidimensional feature vectors that represent the cosmetic defects. The performance of the GK algorithm may be considered similar to a human inspector which is between 85% and 90% approximately. However, the fuzzy clustering technique has the advantage to be very consistent, contrary to a human inspector that can change her/his mind due to subjective influences. The paper also presents the comparison between the fuzzy approach and the artificial neural network approach. The problem faced in this work also helped to compare the performance of FC algorithms with ANN in real world applications.