[1]DI Lan,ZHAO Shuzhi,HE Ruibo.Fabric defect inspection based on illumination preprocessing and feature extraction[J].CAAI Transactions on Intelligent Systems,2019,14(4):716-724.[doi:10.11992/tis.201805023]
Copy

Fabric defect inspection based on illumination preprocessing and feature extraction

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 14 Number of periods: 2019 4 Page number: 716-724 Column: 学术论文—机器感知与模式识别 Public date: 2019-07-02
Title:
Fabric defect inspection based on illumination preprocessing and feature extraction
Author(s):
DI Lan;  ZHAO Shuzhi;  HE Ruibo
School of Digital Media, Jiangnan University, Wuxi 214000, China
Keywords:
illumination preprocessing; local contrast enhancement; CLBP (complete local binary pattern); lattice segmentation; Kullbac–Leibler divergence; feature extraction; fabric defect inspection
CLC:
TS131.9
DOI:
10.11992/tis.201805023
Abstract:
In this study, a modified complete local binary pattern (CLBP) feature extraction method based on the local contrast enhancement is proposed to address the influence of illumination on the feature extraction of fabrics and the limitations of the traditional CLBP algorithms. In this method, a local contrast enhancement algorithm is used to preprocess the fabric images that are affected by illumination, and the modified CLBP algorithm is used to calculate the feature value of every lattice in every image after lattice segmentation and to subsequently calculate the relative divergence KLD between the feature of every lattice and the standard feature value. Subsequently, the calculated relative divergence will be compared with the threshold, and the lattice that exhibits a value larger than the threshold is considered to be a defect area. The experimental results obtained using the standard star pattern and the box pattern databases denote that the proposed method is better than the remaining preprocessing methods, and the recall rate of the majority of the detection results obtained using this method can reach approximately 0.99.
References:
[1] NGAN H Y T, PANG G K H, YUNG S P, et al. Wavelet based methods on patterned fabric defect detection[J]. Pattern recognition, 2005, 38(4):559-576.
[2] NGAN H Y T, PANG G K H. Novel method for patterned fabric inspection using bollinger bands[J]. Optical engineering, 2006, 45(8):087202.
[3] NGAN H Y T, PANG G K H. Regularity analysis for patterned texture inspection[J]. IEEE transactions on automation science and engineering, 2009, 6(1):131-144.
[4] TSANG C S C, NGAN H Y T, PANG G K H. Fabric inspection based on the Elo rating method[J]. Pattern recognition, 2016, 51:378-394.
[5] JIA Liang, CHEN Chen, LIANG Jiuzhen, et al. Fabric defect inspection based on lattice segmentation and Gabor filtering[J]. Neurocomputing, 2017, 238:84-102.
[6] BELHUMEUR P N, KRIEGMAN D J. What is the set of images of an object under all possible illumination conditions[J]. International journal of computer vision, 1998, 28(3):245-260.
[7] 王霞玲, 吕岳, 文颖. 复杂背景和非均匀光照环境下的条码自动定位和识别[J]. 智能系统学报, 2010, 5(1):35-40 WANG Xialing, Lü Yue, WEN Ying. Automatic location and recognition of barcodes on objects with complex background and non-uniform lighting[J]. CAAI transactions on intelligent systems, 2010, 5(1):35-40
[8] 黄腾, 施鹏飞. 非均匀光照下的车牌定位方法[J]. 智能系统学报, 2009, 4(5):421-426 HUANG Teng, SHI Pengfei. Locating vehicle license plates in a non-uniformly illuminated environment[J]. CAAI transactions on intelligent systems, 2009, 4(5):421-426
[9] CANDES E J, DONOHO D L. Curvelets-a surprisingly effective nonadaptive representation for objects with edges[M]. Nashville, TN:Vanderbilt University Press, 1999:1-10.
[10] WANG Haitao, LI S Z. WANG Yangsheng, et al. Self quotient image for face recognition[C]//Proceedings of 2004 International Conference on Image Processing. Singapore, Singapore, 2004:1397-1400.
[11] LAND E H. Recent advances in Retinex theory[J]. Vision research, 1986, 26(1):7-21.
[12] LANGE C, POLTHIER K. Anisotropic smoothing of point sets[J]. Computer aided geometric design, 2005, 22(7):680-692.
[13] KAO Wenchung, HSU M C, YANG Y Y. Local contrast enhancement and adaptive feature extraction for illumination-invariant face recognition[J]. Pattern recognition, 2010, 43(5):1736-1747.
[14] 王美, 梁久祯. 自适应特征提取的光照鲁棒性人脸识别[J]. 计算机工程与应用, 2012, 48(11):164-169 WANG Mei, LIANG Jiuzhen. Adaptive feature extraction for illumination invariant face recognition[J]. Computer Engineering and Applications, 2012, 48(11):164-169
[15] GUO Zhenhua, ZHANG Lei, ZHANG D. A completed modeling of local binary pattern operator for texture classification[J]. IEEE transactions on image processing, 2010, 19(6):1657-1663.
[16] WEN Jia, ZHAO Junsuo, WANG Cailing. Improved morphological component analysis for interference hyperspectral image decomposition[J]. Computers & electrical engineering, 2015, 46:394-402.
[17] 周宇旋, 吴秦, 梁久祯. 判别性完全局部二值模式人脸表情识别[J]. 计算机工程与应用, 2017, 53(4):163-169 ZHOU Yuxuan, WU Qin, LIANG Jiuzhen. Facial expression recognition based on discriminative CLBP[J]. Computer engineering and applications, 2017, 53(4):163-169
[18] DO M N, VETTERLI M. The contourlet transform:an efficient directional multiresolution image representation[J]. IEEE transactions on image processing, 2005, 14(12):2091-2106.
[19] 赵树志, 狄岚, 何锐波. 基于改进判别性完整局部二值模式与格分割的织物瑕疵检测方法[J]. 纺织学报, 2018, 39(9):57-64 ZHAO Shuzhi, DI Lan, HE Ruibo. Fabric defect detection based on modified discriminant complete local binary pattern and lattice segmentation[J]. Journal of textile research, 2018, 39(9):57-64
Similar References:

Memo

-

Last Update: 2019-08-25

Copyright © CAAI Transactions on Intelligent Systems