Defect detection on patterned fabrics using texture periodicity and the coordinated clusters representation

Abstract

Patterned fabrics may be regarded as periodic textures, which are defined as the regular tessellation of a primitive unit. A patterned fabric is considered as defective when a primitive unit is different from the others. In this paper, we propose a one-class classifier that uses Reduced Coordinated Cluster Representation (RCCR) as features. In the training step, the size of the primitive unit of defect-free fabrics is automatically estimated using a texture periodicity algorithm. After that, the fabrics are split into samples of one unit and their local structure is learnt with the RCCR features in a one-class classifier. During the test step, defective and non-defective fabrics are also split into samples and are analyzed unit by unit. If the features of a given unit do not satisfy the classification criterion, it is considered to be a defect. Among the advantages of the RCCR is that it represents structural information of textures in a low-dimensional feature space with high discrimination performance. Results from experiments on an extensive database of real fabric images show that our method yields accurate detections, outperforming other state-of-the-art algorithms.