Sparse Dictionary Reconstruction for Textile Defect Detection

Inspired by the image de-noising techniques using learned dictionaries and sparse representation, we present a fabric defect detection scheme via sparse dictionary reconstruction. Fabric defects can be regarded as local anomalies against the relatively homogeneous texture background. Following from the flexibility of sparse representation, normal fabric samples can be efficiently represented using a linear combination of a few elements of a learned dictionary. When modeling new samples with a learned dictionary, tuned to the input data containing normal fabric structural features, abnormal or defective samples are likely to have larger dissimilarity than normal samples. We evaluate the proposed methods using ten different fabric types. Experimental results show that our method has many advantages in defect detection, especially in adapting variation of fabric textures.