Fabric Defect Detection and Classification Using Gabor Filters and Gaussian Mixture Model

This work investigates the problem of automatic and robust fabric defect detection and classification which are more essential and important in assuring the fabric quality. Two characteristics of this work are: first, a new scheme combining Gabor filters and Gaussian in model (GMM) is proposed for fabric defect detection and classification. In detection, the foreground mask and texture features are extracted using Gabor filters. In classification, a GMM based classifier is trained and assigns each foreground pixel to known classes. The second characteristic of this work is the test data is actually collected from Qinfeng textile factory, China, including nine different fabric defects with more than 1000 samples. All the evaluation of our method is based on these actual fabric images and the experimental results show the proposed algorithm achieved satisfied performance.