Patterned Fabric Inspection and Visualization by the Method of Image Decomposition

This paper analyzes the cartoon and texture structures to inspect and visualize defective objects in a patterned fabric image. It presents a method of an image decomposition (ID) and solves it by a convex optimization algorithm. Our experimental results on benchmark fabric images are superior to those by other methods. Note to Practitioners-This paper is motivated by an ID method to examine how to novelly represent defective objects and repeated patterns in fabric images. We decompose a fabric image into two components: cartoon structure as defective objects and texture structure as repeated patterns. The ID is optimized by the largest correlation between a given defect-free fabric image and the texture structure of a testing image. Its merit is requiring only one defect-free image to optimize the inspection. The resulting cartoon structure is identified for inspection and visualization. An intensive performance evaluation is conducted on dot-, star-, and box-patterned fabric images and the detection accuracies range from 94.9%similar to 99.6%. This research is beneficial to the practitioners for quality control in textile, ceramics, tile, wallpaper, printed circuit board, and aircraft window industries.