Applying wavelets transform and support vector machine for copper clad laminate defects classification

In this paper, we present a multi-resolution approach for the inspection local defects embedded in homogeneous copper clad laminate (CCL) surfaces. The proposed method does not rely on the extraction of local textural features in a spatial basis. It is based mainly on the wavelet transform and inverse wavelet transform on the smooth subimage and detail subimages by properly selecting the adequate decomposition levels. The restored image will remove regular, repetitive texture patterns and enhance only local anomalies. Based on these local anomalies, feature extraction methods can then be used to discriminate between the defective regions and homogeneous regions in the restored image. Real samples with five classes of defects have been classified using this novel multi-classifier, namely, support vector machine. The experimental results show the efficacy of the proposed method.