Ceramic Tile Inspection Using a Hybrid Model of Discrete Wavelet Transform and Gray Level Co-Occurrence Matrix and Artificial Neural Networks

Visual inspection of industrial products is used to determine the control quality for different products. This paper deals with the problem of visual inspection of ceramic tiles industry using a hybrid model of 2-D Discrete Wavelet Transform (DWT) with Haar as a mother wavelet, Gray-Level Co-occurrence Matrices (GLCM) and Artificial Neural Networks (ANN). The proposed algorithm consists of two main phases, the first phase is feature extraction and it is carried out using the first level coefficients of two dimensions DWT and GLCM. The second phase is classification which is carried out using two different supervised ANN classifiers; Multilayer Perceptron (MLP) and Radial Basis Function (RBF) networks. The experimental results of the proposed algorithm give good results in defect detection especially with RBF (average 94 %) rather than MLP (average 91 %)