American Institute of Mathematical Sciences

Advanced
  • Previous Article
    Robust portfolio selection with a combined WCVaR and factor model
  • JIMO Home
  • This Issue
  • Next Article
    Minimizing equilibrium expected sojourn time via performance-based mixed threshold demand allocation in a multiple-server queueing environment
April  2012, 8(2): 325-341. doi: 10.3934/jimo.2012.8.325

Fabric defect detection using multi-level tuned-matched Gabor filters

Kai-Ling Mak 1, , Pai Peng 1, and  Ka-Fai Cedric Yiu 2,

1. 

Department of Industrial and Manufacturing Systems Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong, China, China
2. 

Department of Applied Mathematics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China

Received  November 2010 Revised  August 2011 Published  April 2012

This paper proposes a new defect detection scheme for woven fabrics. The proposed scheme is divided into two parts, namely the training part and the defect detection part. In the training part, a non-defective fabric image is used as a template image, and a finite set of multi-level Gabor wavelets are tuned to match the texture information of the image. In the defect detection part, filtered images from different levels are fused together and the constructed detection scheme is used to detect defects in fabric sample images with the same texture background as that of the template image. A filter selection method is also developed to select optimal filters to facilitate defect detection. The novelty of the method comes from the observation that a Gabor filter with finer resolutions than the fabric defects yarn can contribute very little for defect segmentation but need additional computational time. The proposed scheme is tested by using 78 homogeneous textile fabric images. The results exhibit accurate defect detections with lower false alarms than using the standard Gabor wavelets. Analysis of the computational complexity of the proposed detection scheme is derived, which shows that the scheme can be implemented in real time easily.
Keywords: industrial inspection., defect detection, woven fabrics, Multi-level Gabor wavelet.
Mathematics Subject Classification: 68U10, 65D18, 68T0.
Citation: Kai-Ling Mak, Pai Peng, Ka-Fai Cedric Yiu. Fabric defect detection using multi-level tuned-matched Gabor filters. Journal of Industrial and Management Optimization, 2012, 8 (2) : 325-341. doi: 10.3934/jimo.2012.8.325
References:
[1]

A. Bodnarova, M. Bennamoun and S. Latham, Optimal Gabor filters for textile flaw detection, Pattern Recognition, 35 (2002), 2973-2991. doi: 10.1016/S0031-3203(02)00017-1.

[2]

J. G. Campell, C. Fraley, F. Murtagh and A. E. Raftery, Linear flaw detection in woven textiles using model-based clustering, Pattern Recognition Letters, 18 (1997), 1539-1548. doi: 10.1016/S0167-8655(97)00148-7.

[3]

D. Casasent and J. S. Smokelin, Neural net design of macro Gabor wavelet filters for distortion-invariant object detection in clutter, Opt. Eng., 33 (1994), 2264-2271. doi: 10.1117/12.172408.

[4]

D. Chetverikov and A. Hanbury, Finding defects in texture using regularity and local orientation, Pattern Recognition, 35 (2002), 2165-2180. doi: 10.1016/S0031-3203(01)00188-1.

[5]

F. S. Cohen, Z. Fan and S. Attali, Automated inspection of textile fabrics using textural models, IEEE Trans. Pattern Anal. Machine Intell., 13 (1991), 803-808. doi: 10.1109/34.85670.

[6]

J. G. Daugman, Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters, J. Optical Soc. Amer., 2 (1985), 1160-1169. doi: 10.1364/JOSAA.2.001160.

[7]

J. Escofet, R. Navarro, M. S. Millan and J. Pladelloreans, Detection of local defects in textiles webs using Gabor filters, Opt. Eng., 37 (1998), 2297-2307. doi: 10.1117/1.601751.

[8]

D. E. Goldberg, "Genetic Algorithm in Search, Optimization and Machine Learning," Addison-Wesley, New York, 1989.

[9]

Graniteville Company, "Manual of Standard Fabric Defects in the Textile Industry," South Carolina, 1975.

[10]

A. K. Jain and F. Farrokhnia, Unsupervised texture segmentation using Gabor filters, Pattern Recognition, 24 (1991), 1167-1186. doi: 10.1016/0031-3203(91)90143-S.

[11]

K. L. Mak and P. Peng, Detecting defects in textile fabrics with optimal Gabor filters, International Journal of Computer Science, 1 (2006), 1306-4428.

[12]

R. Malhotra, K. R. Namuduru and N. Ranganathan, Gabor filter-based edge detection, Pattern Recognition, 25 (1992), 1479-1494. doi: 10.1016/0031-3203(92)90121-X.

[13]

J. Malik and P. Perona, Prettentive texture discrimination with early vision mechanisms, J. Opt. Soc. Am., A7 (1994), 923-932. doi: 10.1364/JOSAA.7.000923.

[14]

S. G. Mallat, Multifrequency channel decomposition of images and wavelet models, IEEE Trans. Acoust. Speech, Signal Processing, 37 (1989), 2091-2110. doi: 10.1109/29.45554.

[15]

T. L. Mason, C. Emelle, J. van Berkel, A. M. Bagirov, F. Kampas and J. D. Pintér, Integrated production system optimization using global optimization techniques, Journal of Industrial and Management Optimization, 3 (2007), 257-277. doi: 10.3934/jimo.2007.3.257.

[16]

S. Ozdemir and A. Ercil, Markov random fields and Karhumen-loeve transforms for defect inspection of textile products, Proc. IEEE Conf. Emerging Technologies and Factory Automation, 2 (1996), 697-703.

[17]

O. Pichler, A. Teuner and B. J. Hosticka, An unsupervised texture segmentation algorithm with feature space reduction and knowledge feedback, IEEE Trans. Image Processing, 7 (1998), 53-61. doi: 10.1109/83.650850.

[18]

D. A. Pollen and S. F. Ronner, Visual cortical neurons as localized spatial frequency filters, IEEE Transactions on Systems, Man and Cybernetics, 13 (1983), 907-916.

[19]

T. Ray and R. Sarker, EA for solving combined machine layout and job assignment problems, Journal of Industrial and Management Optimization, 4 (2008), 631-646. doi: 10.3934/jimo.2008.4.631.

[20]

R. Sablatnig, Increasing flexibility for automatic visual inspection: The general analysis graph, Machine Vision and Applications, 12 (2000), 158-169. doi: 10.1007/s001380050135.

[21]

H. Sari-Sarraf and J. S. Goddard, Vision system for on-loom fabric inspection, IEEE Trans. Ind. Appl., 35 (1999), 1252-1259. doi: 10.1109/28.806035.

[22]

K. Srinivasan, P. H. Dastoor, P. Radhakrishnaiah and S. Jayaraman, FDAS: A knowledge-based framework for analysis of defects in woven textile structures, J. Textile Inst., 83 (1992), 431-448. doi: 10.1080/00405009208631217.

[23]

A. Teuner, O. Pichler and B. J. Hosticka, Unsupervised texture segmentation of images using tuned matched Gabor filters, IEEE Trans. Image Processing, 4 (1995), 863-870. doi: 10.1109/83.388091.

[24]

D. M. Tsai and C.-Y. Heish, Automated surface inspection for directional textures, Image Vis. Comput., 18 (1999), 49-62. doi: 10.1016/S0262-8856(99)00009-8.

[25]

J. Wang, R. A. Campbell and R. J. Harwood, Automated inspection of carpets, Proc. SPIE, 2345 (1995), 180-191. doi: 10.1117/12.198873.

[26]

M. A. Webster and R. L. De Valois, Relationship between spatial frequency and orientation tuning of striate cortex cells, J. Optical Soc. Amer., A2 (1985), 1124-1132. doi: 10.1364/JOSAA.2.001124.

[27]

C. Z. Wu and K. L. Teo, Global impulsive optimal control computation, Journal of Industrial and Management Optimization, 2 (2006), 435-450. doi: 10.3934/jimo.2006.2.435.

[28]

K. F. C Yiu, Y. Liu and K. L. Teo, A hybrid descent method for global optimization, Journal of Global Optimization, 28 (2004), 229-238. doi: 10.1023/B:JOGO.0000015313.93974.b0.

[29]

Y. F. Zhang and R. R. Bresee, Fabric defect detection and classification using image analysis, Text. Res. J., 65 (1995), 1-9. doi: 10.1177/004051759506500101.

show all references

References:
[1]

A. Bodnarova, M. Bennamoun and S. Latham, Optimal Gabor filters for textile flaw detection, Pattern Recognition, 35 (2002), 2973-2991. doi: 10.1016/S0031-3203(02)00017-1.

[2]

J. G. Campell, C. Fraley, F. Murtagh and A. E. Raftery, Linear flaw detection in woven textiles using model-based clustering, Pattern Recognition Letters, 18 (1997), 1539-1548. doi: 10.1016/S0167-8655(97)00148-7.

[3]

D. Casasent and J. S. Smokelin, Neural net design of macro Gabor wavelet filters for distortion-invariant object detection in clutter, Opt. Eng., 33 (1994), 2264-2271. doi: 10.1117/12.172408.

[4]

D. Chetverikov and A. Hanbury, Finding defects in texture using regularity and local orientation, Pattern Recognition, 35 (2002), 2165-2180. doi: 10.1016/S0031-3203(01)00188-1.

[5]

F. S. Cohen, Z. Fan and S. Attali, Automated inspection of textile fabrics using textural models, IEEE Trans. Pattern Anal. Machine Intell., 13 (1991), 803-808. doi: 10.1109/34.85670.

[6]

J. G. Daugman, Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters, J. Optical Soc. Amer., 2 (1985), 1160-1169. doi: 10.1364/JOSAA.2.001160.

[7]

J. Escofet, R. Navarro, M. S. Millan and J. Pladelloreans, Detection of local defects in textiles webs using Gabor filters, Opt. Eng., 37 (1998), 2297-2307. doi: 10.1117/1.601751.

[8]

D. E. Goldberg, "Genetic Algorithm in Search, Optimization and Machine Learning," Addison-Wesley, New York, 1989.

[9]

Graniteville Company, "Manual of Standard Fabric Defects in the Textile Industry," South Carolina, 1975.

[10]

A. K. Jain and F. Farrokhnia, Unsupervised texture segmentation using Gabor filters, Pattern Recognition, 24 (1991), 1167-1186. doi: 10.1016/0031-3203(91)90143-S.

[11]

K. L. Mak and P. Peng, Detecting defects in textile fabrics with optimal Gabor filters, International Journal of Computer Science, 1 (2006), 1306-4428.

[12]

R. Malhotra, K. R. Namuduru and N. Ranganathan, Gabor filter-based edge detection, Pattern Recognition, 25 (1992), 1479-1494. doi: 10.1016/0031-3203(92)90121-X.

[13]

J. Malik and P. Perona, Prettentive texture discrimination with early vision mechanisms, J. Opt. Soc. Am., A7 (1994), 923-932. doi: 10.1364/JOSAA.7.000923.

[14]

S. G. Mallat, Multifrequency channel decomposition of images and wavelet models, IEEE Trans. Acoust. Speech, Signal Processing, 37 (1989), 2091-2110. doi: 10.1109/29.45554.

[15]

T. L. Mason, C. Emelle, J. van Berkel, A. M. Bagirov, F. Kampas and J. D. Pintér, Integrated production system optimization using global optimization techniques, Journal of Industrial and Management Optimization, 3 (2007), 257-277. doi: 10.3934/jimo.2007.3.257.

[16]

S. Ozdemir and A. Ercil, Markov random fields and Karhumen-loeve transforms for defect inspection of textile products, Proc. IEEE Conf. Emerging Technologies and Factory Automation, 2 (1996), 697-703.

[17]

O. Pichler, A. Teuner and B. J. Hosticka, An unsupervised texture segmentation algorithm with feature space reduction and knowledge feedback, IEEE Trans. Image Processing, 7 (1998), 53-61. doi: 10.1109/83.650850.

[18]

D. A. Pollen and S. F. Ronner, Visual cortical neurons as localized spatial frequency filters, IEEE Transactions on Systems, Man and Cybernetics, 13 (1983), 907-916.

[19]

T. Ray and R. Sarker, EA for solving combined machine layout and job assignment problems, Journal of Industrial and Management Optimization, 4 (2008), 631-646. doi: 10.3934/jimo.2008.4.631.

[20]

R. Sablatnig, Increasing flexibility for automatic visual inspection: The general analysis graph, Machine Vision and Applications, 12 (2000), 158-169. doi: 10.1007/s001380050135.

[21]

H. Sari-Sarraf and J. S. Goddard, Vision system for on-loom fabric inspection, IEEE Trans. Ind. Appl., 35 (1999), 1252-1259. doi: 10.1109/28.806035.

[22]

K. Srinivasan, P. H. Dastoor, P. Radhakrishnaiah and S. Jayaraman, FDAS: A knowledge-based framework for analysis of defects in woven textile structures, J. Textile Inst., 83 (1992), 431-448. doi: 10.1080/00405009208631217.

[23]

A. Teuner, O. Pichler and B. J. Hosticka, Unsupervised texture segmentation of images using tuned matched Gabor filters, IEEE Trans. Image Processing, 4 (1995), 863-870. doi: 10.1109/83.388091.

[24]

D. M. Tsai and C.-Y. Heish, Automated surface inspection for directional textures, Image Vis. Comput., 18 (1999), 49-62. doi: 10.1016/S0262-8856(99)00009-8.

[25]

J. Wang, R. A. Campbell and R. J. Harwood, Automated inspection of carpets, Proc. SPIE, 2345 (1995), 180-191. doi: 10.1117/12.198873.

[26]

M. A. Webster and R. L. De Valois, Relationship between spatial frequency and orientation tuning of striate cortex cells, J. Optical Soc. Amer., A2 (1985), 1124-1132. doi: 10.1364/JOSAA.2.001124.

[27]

C. Z. Wu and K. L. Teo, Global impulsive optimal control computation, Journal of Industrial and Management Optimization, 2 (2006), 435-450. doi: 10.3934/jimo.2006.2.435.

[28]

K. F. C Yiu, Y. Liu and K. L. Teo, A hybrid descent method for global optimization, Journal of Global Optimization, 28 (2004), 229-238. doi: 10.1023/B:JOGO.0000015313.93974.b0.

[29]

Y. F. Zhang and R. R. Bresee, Fabric defect detection and classification using image analysis, Text. Res. J., 65 (1995), 1-9. doi: 10.1177/004051759506500101.

[1]

Roberta Ghezzi, Benedetto Piccoli. Optimal control of a multi-level dynamic model for biofuel production. Mathematical Control and Related Fields, 2017, 7 (2) : 235-257. doi: 10.3934/mcrf.2017008
[2]

Jean-Philippe Cointet, David Chavalarias. Multi-level science mapping with asymmetrical paradigmatic proximity. Networks and Heterogeneous Media, 2008, 3 (2) : 267-276. doi: 10.3934/nhm.2008.3.267
[3]

Ankang Yu, Yajuan Yang, Baode Li. A new Carleson measure adapted to multi-level ellipsoid covers. Communications on Pure and Applied Analysis, 2021, 20 (10) : 3481-3497. doi: 10.3934/cpaa.2021115
[4]

Andrew J. Majda, Yuan Yuan. Fundamental limitations of Ad hoc linear and quadratic multi-level regression models for physical systems. Discrete and Continuous Dynamical Systems - B, 2012, 17 (4) : 1333-1363. doi: 10.3934/dcdsb.2012.17.1333
[5]

Binbin Cao, Zhongdong Xiao, Xiaojun Li. Joint decision on pricing and waste emission level in industrial symbiosis chain. Journal of Industrial and Management Optimization, 2018, 14 (1) : 135-164. doi: 10.3934/jimo.2017040
[6]

Yongkun Wang, Fengshou He, Xiaobo Deng. Multi-aircraft cooperative path planning for maneuvering target detection. Journal of Industrial and Management Optimization, 2022, 18 (3) : 1935-1948. doi: 10.3934/jimo.2021050
[7]

Shuai Ren, Tao Zhang, Fangxia Shi. Characteristic analysis of carrier based on the filtering and a multi-wavelet method for the information hiding. Discrete and Continuous Dynamical Systems - S, 2015, 8 (6) : 1291-1299. doi: 10.3934/dcdss.2015.8.1291
[8]

Ibrahim Agyemang, H. I. Freedman. A mathematical model of an Agricultural-Industrial-Ecospheric system with industrial competition. Communications on Pure and Applied Analysis, 2009, 8 (5) : 1689-1707. doi: 10.3934/cpaa.2009.8.1689
[9]

Sergio Andrés De Raco, Viktoriya Semeshenko. Labor mobility and industrial space in Argentina. Journal of Dynamics and Games, 2019, 6 (2) : 107-118. doi: 10.3934/jdg.2019008
[10]

Paolo Maria Mariano. Line defect evolution in finite-dimensional manifolds. Discrete and Continuous Dynamical Systems - B, 2012, 17 (2) : 575-596. doi: 10.3934/dcdsb.2012.17.575
[11]

Mario Ahues, Filomena D. d'Almeida, Alain Largillier, Paulo B. Vasconcelos. Defect correction for spectral computations for a singular integral operator. Communications on Pure and Applied Analysis, 2006, 5 (2) : 241-250. doi: 10.3934/cpaa.2006.5.241
[12]

Tudor Barbu. Deep learning-based multiple moving vehicle detection and tracking using a nonlinear fourth-order reaction-diffusion based multi-scale video object analysis. Discrete and Continuous Dynamical Systems - S, 2022  doi: 10.3934/dcdss.2022083
[13]

P. Cerejeiras, M. Ferreira, U. Kähler, F. Sommen. Continuous wavelet transform and wavelet frames on the sphere using Clifford analysis. Communications on Pure and Applied Analysis, 2007, 6 (3) : 619-641. doi: 10.3934/cpaa.2007.6.619
[14]

Xiaoqun Zhang, Tony F. Chan. Wavelet inpainting by nonlocal total variation. Inverse Problems and Imaging, 2010, 4 (1) : 191-210. doi: 10.3934/ipi.2010.4.191
[15]

Fang Qin, Ying Jiang, Ping Gu. Three-dimensional computer simulation of twill woven fabric by using polynomial mathematical model. Discrete and Continuous Dynamical Systems - S, 2019, 12 (4&5) : 1167-1178. doi: 10.3934/dcdss.2019080
[16]

Monika Muszkieta. A variational approach to edge detection. Inverse Problems and Imaging, 2016, 10 (2) : 499-517. doi: 10.3934/ipi.2016009
[17]

Michael Dellnitz, O. Junge, B Thiere. The numerical detection of connecting orbits. Discrete and Continuous Dynamical Systems - B, 2001, 1 (1) : 125-135. doi: 10.3934/dcdsb.2001.1.125
[18]

Wided Kechiche. Regularity of the global attractor for a nonlinear Schrödinger equation with a point defect. Communications on Pure and Applied Analysis, 2017, 16 (4) : 1233-1252. doi: 10.3934/cpaa.2017060
[19]

Biswajit Sarkar, Bimal Kumar Sett, Sumon Sarkar. Optimal production run time and inspection errors in an imperfect production system with warranty. Journal of Industrial and Management Optimization, 2018, 14 (1) : 267-282. doi: 10.3934/jimo.2017046
[20]

Ming-Jong Yao, Shih-Chieh Chen, Yu-Jen Chang. A common cycle approach for solving the economic lot and inspection scheduling problem. Journal of Industrial and Management Optimization, 2012, 8 (1) : 141-162. doi: 10.3934/jimo.2012.8.141

2020 Impact Factor: 1.801

Tools

Metrics

  • PDF downloads (344)
  • HTML views (0)
  • Cited by (9)

Other articles
by authors

[Back to Top]

Copyright © 2022 American Institute of Mathematical Sciences | Privacy Policy