Fabric defect inspection based on lattice segmentation and lattice templates

Automated fabric inspection is a challenging task due to the unpredictable visual forms of the fabric defects and their scarcity compared with the tremendous amount of defect-free fabric products. This paper proposes a novel method based on lattice segmentation and lattice templates which automatically identifies the defects of fabric images. With the proposed method, a fabric image is segmented to lattices by inferring the placement rule of the texture primitives categorized to distinct texture classes. Each texture class is modeled by multiple templates inferred from the defect-free samples based on some metrics determined a priori according to their inspection efficiencies. For a lattice segmented from a given image, the most similar template is identified through a template matching process which compensates the local deformations around the lattice, and the distances between the lattice and the identified template are estimated based on the selected metrics. The lattices of distances exceeding the learnt distance range are identified as defective. The performance of the proposed method is evaluated based on two databases respectively providing pixel-level and image-level evaluations. For both databases, the receiver operating characteristic curves are plotted and the average areas under curves are 0.86 and 0.95, respectively, for pixel-level and image-level databases. The proposed method is further tested on the blurred and noisy version of images from pixel-level database and the resulting area is 0.81 on average. The proposed method outperforms the state-of-the-art methods by comparing corresponding areas.