Fabric defect segmentation by bidimensional empirical mode decomposition

Fabric defect detection has attracted increasing attention in the fields of computer vision and textile engineering because it is essential to quality assurance of textile manufacturing. In this paper, we propose a novel defect detection scheme for fabric inspection based on bidimensional empirical mode decomposition. The stopping criterion for sifting and the intrinsic mode functions (IMFs) are adapted for this specific application. Appropriate IMFs are selected to eliminate influences of fabric textures and lighting in defect segmentation. The experiment results on sample images from our laboratory and from TILDA’s Textile Texture Database demonstrate that the proposed method is a robust and accurate approach for fabric defect inspection.