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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 & 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.  doi: 10.1016/S0031-3203(02)00017-1.  Google Scholar

[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.  doi: 10.1016/S0167-8655(97)00148-7.  Google Scholar

[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.  doi: 10.1117/12.172408.  Google Scholar

[4]

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

[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.  doi: 10.1109/34.85670.  Google Scholar

[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.  doi: 10.1364/JOSAA.2.001160.  Google Scholar

[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.  doi: 10.1117/1.601751.  Google Scholar

[8]

D. E. Goldberg, "Genetic Algorithm in Search, Optimization and Machine Learning,", Addison-Wesley, (1989).   Google Scholar

[9]

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

[10]

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

[11]

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

[12]

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

[13]

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

[14]

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

[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.  doi: 10.3934/jimo.2007.3.257.  Google Scholar

[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.   Google Scholar

[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.  doi: 10.1109/83.650850.  Google Scholar

[18]

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

[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.  doi: 10.3934/jimo.2008.4.631.  Google Scholar

[20]

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

[21]

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

[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.  doi: 10.1080/00405009208631217.  Google Scholar

[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.  doi: 10.1109/83.388091.  Google Scholar

[24]

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

[25]

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

[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.  doi: 10.1364/JOSAA.2.001124.  Google Scholar

[27]

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

[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.  doi: 10.1023/B:JOGO.0000015313.93974.b0.  Google Scholar

[29]

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

show all references

References:
[1]

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

[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.  doi: 10.1016/S0167-8655(97)00148-7.  Google Scholar

[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.  doi: 10.1117/12.172408.  Google Scholar

[4]

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

[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.  doi: 10.1109/34.85670.  Google Scholar

[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.  doi: 10.1364/JOSAA.2.001160.  Google Scholar

[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.  doi: 10.1117/1.601751.  Google Scholar

[8]

D. E. Goldberg, "Genetic Algorithm in Search, Optimization and Machine Learning,", Addison-Wesley, (1989).   Google Scholar

[9]

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

[10]

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

[11]

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

[12]

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

[13]

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

[14]

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

[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.  doi: 10.3934/jimo.2007.3.257.  Google Scholar

[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.   Google Scholar

[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.  doi: 10.1109/83.650850.  Google Scholar

[18]

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

[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.  doi: 10.3934/jimo.2008.4.631.  Google Scholar

[20]

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

[21]

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

[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.  doi: 10.1080/00405009208631217.  Google Scholar

[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.  doi: 10.1109/83.388091.  Google Scholar

[24]

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

[25]

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

[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.  doi: 10.1364/JOSAA.2.001124.  Google Scholar

[27]

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

[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.  doi: 10.1023/B:JOGO.0000015313.93974.b0.  Google Scholar

[29]

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

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