Fabric Defect Classification Based on LBP and GLCM (Journal of Fiber Bioengineering and Informatics, 2015)
Inevitably there will be various types of fabric defect exists in textile production line. In order to\rdistinguish and classify the types of defects more efficiently and accurately, an algorithm which combines\rLocal Binary Patterns (LBP) and Gray-level Co-occurrence Matrix (GLCM) is proposed in this paper\rfor fabric defect classification. The most pivotal step of the algorithm is to extract the local and global\rfeature values of defect images. Firstly the local feature information of the image is extracted by adopting\rLBP algorithm. And then the overall texture information of the image is described via GLCM algorithm.\rIn this way, the fabric image can be fully described from global and local. Finally, the two-part feature\rinformation are structured as a whole as the input of BP Neural Network. Thus the trained BP Neural\rNetwork can be used to classify the different types of defects. Experimental results show that the\ralgorithm has higher classification accuracy.
Keywords: LBP; Gray-level Co-occurrence Matrix (GLCM); BP Neural Network; Defect Classification