  In order to accurately detect defects in patterned fabric images, a novel
detection algorithm based on Gabor-HOG (GHOG) and low-rank decomposition is
proposed in this paper. Defect-free pattern fabric images have the specified
direction, while defects damage their regularity of direction. Therefore, a
direction-aware descriptor is designed, denoted as GHOG, a combination of Gabor
and HOG, which is extremely valuable for localizing the defect region. Upon
devising a powerful directional descriptor, an efficient low-rank decomposition
model is constructed to divide the matrix generated by the directional feature
extracted from image blocks into a low-rank matrix (background information) and
a sparse matrix (defect information). A nonconvex log det(.) as a smooth
surrogate function for the rank instead of the nuclear norm is also exploited
to improve the efficiency of the low-rank model. Moreover, the computational
efficiency is further improved by utilizing the alternative direction method of
multipliers (ADMM). Thereafter, the saliency map generated by the sparse matrix
is segmented via the optimal threshold algorithm to locate the defect regions.
Experimental results show that the proposed method can effectively detect
patterned fabric defects and outperform the state-of-the-art methods.
