  In this paper, we propose a machine vision algorithm for automatically
detecting defects in textures belonging to 16 out of 17 wallpaper groups using
texture-periodicity and a family of Gabor wavelets. Input defective images are
subjected to Gabor wavelet transformation in multi-scales and
multi-orientations and a resultant image is obtained in L2 norm. The resultant
image is split into several periodic blocks and energy of each block is used as
a feature space to automatically identify defective and defect-free blocks
using Ward's hierarchical clustering. Experiments on defective fabric images of
three major wallpaper groups, namely, pmm, p2 and p4m, show that the proposed
method is robust in finding fabric defects without human intervention and can
be used for automatic defect detection in fabric industries.
