An efficient system for automatic sorting of the ceramic tiles

The ceramic tile manufacturing process is completely automated with the exception of visual inspection of the product (sorting stage). There are a number of methods for the automatic detection of multifarious range of ceramic tile defects and automatic sorting of them. In these methods it is necessary a tradeoff between sorting accuracy and the rate of computation. In this paper we propose a system that uses machine-vision techniques for sorting the ceramic tiles. We apply the co-occurrence matrix to extract the features of ceramic tiles such as ASM, IDF, Contrast and Entropy. As the classifier, we use a multi-layer perceptron neural network. We investigate the performance of the proposed system with different number of layers and different training algorithms of neural networks to classify ceramic tiles into four groups. We compare their speed and accuracy.