Defect segmentation of texture images with wavelet transform and a co-occurrence matrix

Defect segmentation of complicated textures is a challenging problem in automatic inspection. In this paper, we use wavelet transform (wr) and a co-occurrence matrix (cm) to extract features of texture images, then use those features to locate defects on textile fabrics. From the experimental results, we obtain a 92% accuracy rate when determining if the inspected image is with or without defects and an 84% accuracy rate when locating the defect position in an image with defects. We also find that the method's performance is invariant under geometric transformation. This method can be extensively applied to automatic surface defect inspection of other materials such as wood and metal.