Improved defect detection using support vector machines and wavelet feature extraction based on vector quantization and SVD techniques

This paper aims at investigating a novel solution to the problem of defect detection from images using the Support Vector Machines (SVM) classification approach, that can find applications in the design of robust quality control systems for the production of furniture, textile, integrated circuits, etc. The suggested solution focuses on detecting defects for manufacturing applications from their wavelet transformation and vector quantization related properties of the associated wavelet coefficients. More specifically, a novel methodology is investigated for discriminating defects by applying a supervised neural classification technique, namely SVM, to innovative multidimensional wavelet based feature vectors. These vectors are extracted from the K-Level 2-D DWT (Discrete Wavelet Transform) transformed original image using Vector Quantization techniques and a Singular Value Decomposition (SVD) Analysis applied to these wavelet domain quantization vectors. The results of the proposed methodology are illustrated in defective textile images where the defective areas are recognized with higher accuracy than the one obtained by applying two rival defect detection methodologies. The first one of them uses all the wavelet coefficients derived from the k-Level 2-D DWT and involves SVM again in the classification stage, while the second one uses again all the wavelet coefficients derived from the k-Level 2-D DWT but involves a Multilayer Perceptron (MLP) neural network in the classification stage. The promising results herein shown outline the importance of judicious selection and processing of 2-D DWT wavelet coefficients for industrial pattern recognition applications as well as the generalization performance benefits obtained by involving SVM neural networks instead of other ANN models.