Fabric defect inspection based on isotropic lattice segmentation

Automated visual inspection of fabric defects is a challenge due to the diversity of the fabric patterns and defects. Although there are many automated inspection methods of identifying fabric defects, most methods process images containing the fabric patterns classified as the crystallographic group p1 and implicitly assume the fabric patterns are arranged in fixed directions. This paper proposes an automated defect inspection method which calibrates the fabric image and then segments the image into none-overlapped sub-images which are called lattices. Thus, the image is represented by hundreds of lattices sharing some common features instead of millions of unrelated pixels. The defect inspection problem is transformed to comparing the lattice similarity based on the shared features and identifying the defective lattices as the outliers in the feature space. The performance of the proposed method ILS (Isotropic Lattice Segmentation) is evaluated on the databases of images containing fabric patterns arranged orthogonally and arbitrarily. By comparing the resultant images with ground-truth images, an overall detection rate of 0.955 is achieved, which is comparable with the state-of-the-art methods.