TY - JOUR
T1 - Textile fabric defect detection based on low-rank representation
AU - Li, Peng
AU - Liang, Junli
AU - Shen, Xubang
AU - Zhao, Minghua
AU - Sui, Liansheng
N1 - Publisher Copyright:
© 2017, Springer Science+Business Media, LLC.
PY - 2019/1/1
Y1 - 2019/1/1
N2 - In this paper, we propose a novel and robust fabric defect detection method based on the low-rank representation (LRR) technique. Due to the repeated texture structure we model a defects-free fabric image as a low-rank structure. In addition, because defects, if exist, change only the texture of fabric locally, we model them with a sparse structure. Based on the above idea, we represent a fabric image into the sum of a low-rank matrix which expresses fabric texture and a sparse matrix which expresses defects. Then, the LRR method is applied to obtain the corresponding decomposition. Especially, in order to make better use of low-rank structure characteristics we propose LRREB (low-rank representation based on eigenvalue decomposition and blocked matrix) method to improve LRR. LRREB is implemented by dividing a image into some corresponding blocked matrices to reduce dimensions and applying eigen-value decomposition (EVD) on blocked matrix instead of singular value decomposition (SVD) on original fabric image, which improves the accuracy and efficiency. No training samples are required in our methods. Experimental results show that the proposed fabric defect detection method is feasible, effective, and simple to be employed.
AB - In this paper, we propose a novel and robust fabric defect detection method based on the low-rank representation (LRR) technique. Due to the repeated texture structure we model a defects-free fabric image as a low-rank structure. In addition, because defects, if exist, change only the texture of fabric locally, we model them with a sparse structure. Based on the above idea, we represent a fabric image into the sum of a low-rank matrix which expresses fabric texture and a sparse matrix which expresses defects. Then, the LRR method is applied to obtain the corresponding decomposition. Especially, in order to make better use of low-rank structure characteristics we propose LRREB (low-rank representation based on eigenvalue decomposition and blocked matrix) method to improve LRR. LRREB is implemented by dividing a image into some corresponding blocked matrices to reduce dimensions and applying eigen-value decomposition (EVD) on blocked matrix instead of singular value decomposition (SVD) on original fabric image, which improves the accuracy and efficiency. No training samples are required in our methods. Experimental results show that the proposed fabric defect detection method is feasible, effective, and simple to be employed.
KW - Eigen-value decomposition (EVD)
KW - Fabric defect detection
KW - Low-Rank Representation (LRR)
KW - Singular value decomposition (SVD), low-rank representation based on eigenvalue decomposition and blocked matrix (LRREB)
KW - Sparse matrix
UR - http://www.scopus.com/inward/record.url?scp=85032817377&partnerID=8YFLogxK
U2 - 10.1007/s11042-017-5263-z
DO - 10.1007/s11042-017-5263-z
M3 - 文章
AN - SCOPUS:85032817377
SN - 1380-7501
VL - 78
SP - 99
EP - 124
JO - Multimedia Tools and Applications
JF - Multimedia Tools and Applications
IS - 1
ER -