Abstract

We propose a new defect detection algorithm for scale-covered steel wire rods. The algorithm incorporates an adaptive wavelet filter that is designed on the basis of lattice parameterization of orthogonal wavelet bases. This approach offers the opportunity to design orthogonal wavelet filters via optimization methods. To improve the performance and the flexibility of wavelet design, we propose the use of the undecimated discrete wavelet transform, and separate design of column and row wavelet filters but with a common cost function. The coefficients of the wavelet filters are optimized by the so-called univariate dynamic encoding algorithm for searches (uDEAS), which searches the minimum value of a cost function designed to maximize the energy difference between defects and background noise. Moreover, for improved detection accuracy, we propose an enhanced double-threshold method. Experimental results for steel wire rod surface images obtained from actual steel production lines show that the proposed algorithm is effective.

© 2012 Optical Society of America

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2011 (2)

V. H. C. D. Albuquerque, C. C. Silva, T. I. D. S. Menezes, J. P. Farias, and J. M. R. S. Tavares, “Automatic evaluatin of nickel alloy secondary phases from SEM images,” Microsc. Res. Tech. 74, 36–46 (2011).
[CrossRef]

D. C. Choi, Y. J. Jeon, J. P. Yun, and S. W. Kim, “Pinhole detection in steel slab images using Gabor filter and morphological features,” Appl. Opt. 50, 5122–5129 (2011).
[CrossRef]

2010 (3)

P. P. R. Filho, T. D. S. Cavalcante, V. H. C. D. Albuquerque, and J. M. R. S. Tavares, “Brinell and Vickers hardness measurement using image processing and analysis techniques,” J. Test. Eval. 38, 88–94 (2010).

V. H. C. D. Albuquerque, J. M. R. S. Tavares, and L. M. P. Dur, “Evaluation of delamination damage on composite plates using an artificial neural network for the radiographic image analysis,” J. Compos. Mater. 44, 1139–1159 (2010).
[CrossRef]

E. Kim, M. S. Kim, J. Y. Choi, S. W. Kim, and J. W. Kim, “Trajectory generation schemes for bipedal ascending and descending stairs using univariate dynamic encoding algorithm for searches (uDEAS),“ Int. J. Control Autom. Syst. 8, 1061–1071 (2010).

2009 (2)

J. P. Yun, S. H. Choi, and S. W. Kim, “Vision-based defect detection of scale-covered steel billet surfaces,” Opt. Eng. 48, 037205 (2009).
[CrossRef]

M. Rallo, M. S. Millan, and J. Escofet, “Unsupervised novelty detection using Gabor filters for defect segmentation in textures,” J. Opt. Soc. Am. A 26, 1967–1976 (2009).
[CrossRef]

2008 (4)

J. H. Oh, B. J. Yun, S. Y. Kim, and K. H. Park, “A development of the TFT-LCD image defect inspection method based on human visual system,” IEICE Trans. Fundamentals E91-A, 1400–1407 (2008).
[CrossRef]

X. Peng, T. Chen, W. Yu, Z. Zhou, and G. Sun, “An online defects inspection method for float glass fabrication based on machine vision,” Int. J. Adv. Manuf. Technol. 39, 1180–1189 (2008).

J. P. Yun, S. H. Choi, B. Y. Seo, and S. W. Kim, “Real-time vision-based defect inspection for high-speed steel products,” Opt. Eng. 47, 077204 (2008).
[CrossRef]

J. W. Kim, T. Kim, Y. Park, and S. W. Kim, “On load motor parameter identification using dynamic encoding algorithm for searches (uDEAS),” IEEE Trans. Energy Conv. 23, 804–813 (2008).
[CrossRef]

2007 (3)

Y. Han and P. Shi, “An adaptive level-selecting wavelet transform for texture defect detection,” Image Vision Comput. 25, 1239–1248 (2007).
[CrossRef]

J. W. Kim and S. W. Kim, “A fast computational optimization method: univariate dynamic encoding algorithm for searches (uDEAS),” IEICE Trans. Fundamentals E-90A, 1679–1689 (2007).
[CrossRef]

Y. J. Jang and S. W. Kim, “An estimation of a billet temperature during reheating furnace operation,” Int. J. Control Autom. Syst. 5, 43–60 (2007).

2006 (4)

Alaknanda, R. S. Anand, and P. Kumar, “Flaw detection in radiographic weld images using morphological approach,” NDT and E Int. 39, 29–33 (2006).

X. Li, S. K. Tso, X. P. Guan, and Q. Huang, “Improving automatic detection of defects in castings by applying wavelet technique,” IEEE Trans. Ind. Electron. 53, 1927–1934 (2006).
[CrossRef]

S. C. Kim and T. J. Kang, “Automated defect detection system using wavelet packet frame and Gaussian mixture model,” J. Opt. Soc. Am. A 23, 2690–2691 (2006).
[CrossRef]

X. Li, S. K. Tso, X. P. Guan, and Q. Huang, “Improving automatic detection of defects in castings by applying wavelet technique,” IEEE Trans. Ind. Electron. 53, 1927–1934 (2006).
[CrossRef]

2005 (4)

C. S. Cho, B. M. Chung, and M. J. Park, “Development of real-time vision-based fabric inspection system,” IEEE Trans. Ind. Electron. 52, 1073–1079 (2005).
[CrossRef]

J. Ge, Z. Zhu, D. He, and L. Chen, “A vision-based algorithm for seam detection in a PAW process for large-diameter stainless steel pipes,” Int. J. Adv. Manuf. Technol. 26, 1006–1011 (2005).

W. J. Jasper, J. Joines, and J. Brenzovich, “Fabric defect detection using genetic algorithm tuned wavelet filter,” J. Text. Inst. 96, 43–54 (2005).
[CrossRef]

X. Z. Yang, G. K. H. Pang, and N. H. C. Yung,” Robust fabric defect detection and classification using multiple adaptive wavelets,” IEE Proc. Vis. Image Signal Process. 152, 715–723 (2005).

2004 (2)

J. W. Kim and S. W. Kim, “Numerical method for global optimisation: dynamic encoding algorithm for searches,” IEE Proc. Control Theory Applic. 151, 661–668 (2004).

F. Pernkopf, “Detection of surface defects on raw steel blocks using Bayesian network classifiers,” Pattern Anal. Applic. 7, 333–342 (2004).

2003 (1)

A. Kumar, “Neural network based detection of local textile defects,” Pattern Recogn. 36, 1645–1659 (2003).
[CrossRef]

2002 (3)

X. Z. Yang, G. K. H. Pang, and N. H. C. Yung, “Discriminative fabric defect detection using adaptive wavelets,” Opt. Eng. 41, 3116–3126 (2002).
[CrossRef]

A. Kumar and G. K. H. Pang, “Defect detection in textured materials using Gabor filters,” IEEE Trans. Ind. Appl. 38, 425–440 (2002).
[CrossRef]

B. Aiazzi, L. Alparone, S. Baronti, and A. Garzelli, “Context-driven fusion of high spatial and spectral resolution images based on oversampled multiresolution analysis,” IEEE Trans. Geosci. Remote Sens. 40, 2300–2312 (2002).
[CrossRef]

2000 (1)

1999 (2)

T. H. Kim, T. H. Cho, Y. H. Moon, and S. H. Park, “Visual inspection system for the classification of solder joints,” Pattern Recogn. 32565–575 (1999).
[CrossRef]

H. Sari-Sarraf and J. S. Goddard, “Vision system for on-loom fabric inspection,” IEEE Trans. Industrial Appl. 35, 1252–1259 (1999).

1998 (3)

P. Rieder, J. Gotze, and J. A. Nossek, “Parameterization of orthogonal wavelet transform and their implementation,” IEEE. Trans. Circuits Syst. II 45, 217–226 (1998) .

Z. Wen and Y. Tao, “Brightness-invariant image segmentation for on-line fruit defect detection,” Opt. Eng. 37, 2948–2952 (1998).
[CrossRef]

J. S. Goodchild and F. Fueten, “Edge detection in petrographic images using the rotating polarizer stage,” Comput. Geosci. 24, 745–751 (1998).
[CrossRef]

1996 (1)

W. J. Jasper, S. J. Garnier, and H. Potlapalli, “Texture characterization and defect detection using adaptive wavelets,” Opt. Eng. 35, 3140–3149 (1996).
[CrossRef]

Aiazzi, B.

B. Aiazzi, L. Alparone, S. Baronti, and A. Garzelli, “Context-driven fusion of high spatial and spectral resolution images based on oversampled multiresolution analysis,” IEEE Trans. Geosci. Remote Sens. 40, 2300–2312 (2002).
[CrossRef]

Alaknanda,

Alaknanda, R. S. Anand, and P. Kumar, “Flaw detection in radiographic weld images using morphological approach,” NDT and E Int. 39, 29–33 (2006).

Albuquerque, V. H. C. D.

V. H. C. D. Albuquerque, C. C. Silva, T. I. D. S. Menezes, J. P. Farias, and J. M. R. S. Tavares, “Automatic evaluatin of nickel alloy secondary phases from SEM images,” Microsc. Res. Tech. 74, 36–46 (2011).
[CrossRef]

V. H. C. D. Albuquerque, J. M. R. S. Tavares, and L. M. P. Dur, “Evaluation of delamination damage on composite plates using an artificial neural network for the radiographic image analysis,” J. Compos. Mater. 44, 1139–1159 (2010).
[CrossRef]

P. P. R. Filho, T. D. S. Cavalcante, V. H. C. D. Albuquerque, and J. M. R. S. Tavares, “Brinell and Vickers hardness measurement using image processing and analysis techniques,” J. Test. Eval. 38, 88–94 (2010).

Alparone, L.

B. Aiazzi, L. Alparone, S. Baronti, and A. Garzelli, “Context-driven fusion of high spatial and spectral resolution images based on oversampled multiresolution analysis,” IEEE Trans. Geosci. Remote Sens. 40, 2300–2312 (2002).
[CrossRef]

Anand, R. S.

Alaknanda, R. S. Anand, and P. Kumar, “Flaw detection in radiographic weld images using morphological approach,” NDT and E Int. 39, 29–33 (2006).

Baronti, S.

B. Aiazzi, L. Alparone, S. Baronti, and A. Garzelli, “Context-driven fusion of high spatial and spectral resolution images based on oversampled multiresolution analysis,” IEEE Trans. Geosci. Remote Sens. 40, 2300–2312 (2002).
[CrossRef]

Bernabeu, E.

Brenzovich, J.

W. J. Jasper, J. Joines, and J. Brenzovich, “Fabric defect detection using genetic algorithm tuned wavelet filter,” J. Text. Inst. 96, 43–54 (2005).
[CrossRef]

Cavalcante, T. D. S.

P. P. R. Filho, T. D. S. Cavalcante, V. H. C. D. Albuquerque, and J. M. R. S. Tavares, “Brinell and Vickers hardness measurement using image processing and analysis techniques,” J. Test. Eval. 38, 88–94 (2010).

Chang, T. S.

H. Jia, Y. L. Murphey, J. Shi, and T. S. Chang, “An intelligent real-time vision system for surface defect detection,” in Proceedings of the IEEE International Conference on Pattern Recognition, Vol. 3 (2004), pp. 239–242.

Chen, L.

J. Ge, Z. Zhu, D. He, and L. Chen, “A vision-based algorithm for seam detection in a PAW process for large-diameter stainless steel pipes,” Int. J. Adv. Manuf. Technol. 26, 1006–1011 (2005).

Chen, T.

X. Peng, T. Chen, W. Yu, Z. Zhou, and G. Sun, “An online defects inspection method for float glass fabrication based on machine vision,” Int. J. Adv. Manuf. Technol. 39, 1180–1189 (2008).

Cho, C. S.

C. S. Cho, B. M. Chung, and M. J. Park, “Development of real-time vision-based fabric inspection system,” IEEE Trans. Ind. Electron. 52, 1073–1079 (2005).
[CrossRef]

Cho, T. H.

T. H. Kim, T. H. Cho, Y. H. Moon, and S. H. Park, “Visual inspection system for the classification of solder joints,” Pattern Recogn. 32565–575 (1999).
[CrossRef]

Choi, D. C.

Choi, J. Y.

E. Kim, M. S. Kim, J. Y. Choi, S. W. Kim, and J. W. Kim, “Trajectory generation schemes for bipedal ascending and descending stairs using univariate dynamic encoding algorithm for searches (uDEAS),“ Int. J. Control Autom. Syst. 8, 1061–1071 (2010).

Choi, S. H.

J. P. Yun, S. H. Choi, and S. W. Kim, “Vision-based defect detection of scale-covered steel billet surfaces,” Opt. Eng. 48, 037205 (2009).
[CrossRef]

J. P. Yun, S. H. Choi, B. Y. Seo, and S. W. Kim, “Real-time vision-based defect inspection for high-speed steel products,” Opt. Eng. 47, 077204 (2008).
[CrossRef]

Chung, B. M.

C. S. Cho, B. M. Chung, and M. J. Park, “Development of real-time vision-based fabric inspection system,” IEEE Trans. Ind. Electron. 52, 1073–1079 (2005).
[CrossRef]

Dur, L. M. P.

V. H. C. D. Albuquerque, J. M. R. S. Tavares, and L. M. P. Dur, “Evaluation of delamination damage on composite plates using an artificial neural network for the radiographic image analysis,” J. Compos. Mater. 44, 1139–1159 (2010).
[CrossRef]

Escofet, J.

Farias, J. P.

V. H. C. D. Albuquerque, C. C. Silva, T. I. D. S. Menezes, J. P. Farias, and J. M. R. S. Tavares, “Automatic evaluatin of nickel alloy secondary phases from SEM images,” Microsc. Res. Tech. 74, 36–46 (2011).
[CrossRef]

Filho, P. P. R.

P. P. R. Filho, T. D. S. Cavalcante, V. H. C. D. Albuquerque, and J. M. R. S. Tavares, “Brinell and Vickers hardness measurement using image processing and analysis techniques,” J. Test. Eval. 38, 88–94 (2010).

Fueten, F.

J. S. Goodchild and F. Fueten, “Edge detection in petrographic images using the rotating polarizer stage,” Comput. Geosci. 24, 745–751 (1998).
[CrossRef]

Garnier, S. J.

W. J. Jasper, S. J. Garnier, and H. Potlapalli, “Texture characterization and defect detection using adaptive wavelets,” Opt. Eng. 35, 3140–3149 (1996).
[CrossRef]

Garzelli, A.

B. Aiazzi, L. Alparone, S. Baronti, and A. Garzelli, “Context-driven fusion of high spatial and spectral resolution images based on oversampled multiresolution analysis,” IEEE Trans. Geosci. Remote Sens. 40, 2300–2312 (2002).
[CrossRef]

Ge, J.

J. Ge, Z. Zhu, D. He, and L. Chen, “A vision-based algorithm for seam detection in a PAW process for large-diameter stainless steel pipes,” Int. J. Adv. Manuf. Technol. 26, 1006–1011 (2005).

Goddard, J. S.

H. Sari-Sarraf and J. S. Goddard, “Vision system for on-loom fabric inspection,” IEEE Trans. Industrial Appl. 35, 1252–1259 (1999).

Goodchild, J. S.

J. S. Goodchild and F. Fueten, “Edge detection in petrographic images using the rotating polarizer stage,” Comput. Geosci. 24, 745–751 (1998).
[CrossRef]

Gotze, J.

P. Rieder, J. Gotze, and J. A. Nossek, “Parameterization of orthogonal wavelet transform and their implementation,” IEEE. Trans. Circuits Syst. II 45, 217–226 (1998) .

Guan, X. P.

X. Li, S. K. Tso, X. P. Guan, and Q. Huang, “Improving automatic detection of defects in castings by applying wavelet technique,” IEEE Trans. Ind. Electron. 53, 1927–1934 (2006).
[CrossRef]

X. Li, S. K. Tso, X. P. Guan, and Q. Huang, “Improving automatic detection of defects in castings by applying wavelet technique,” IEEE Trans. Ind. Electron. 53, 1927–1934 (2006).
[CrossRef]

Han, Y.

Y. Han and P. Shi, “An adaptive level-selecting wavelet transform for texture defect detection,” Image Vision Comput. 25, 1239–1248 (2007).
[CrossRef]

He, D.

J. Ge, Z. Zhu, D. He, and L. Chen, “A vision-based algorithm for seam detection in a PAW process for large-diameter stainless steel pipes,” Int. J. Adv. Manuf. Technol. 26, 1006–1011 (2005).

Huang, Q.

X. Li, S. K. Tso, X. P. Guan, and Q. Huang, “Improving automatic detection of defects in castings by applying wavelet technique,” IEEE Trans. Ind. Electron. 53, 1927–1934 (2006).
[CrossRef]

X. Li, S. K. Tso, X. P. Guan, and Q. Huang, “Improving automatic detection of defects in castings by applying wavelet technique,” IEEE Trans. Ind. Electron. 53, 1927–1934 (2006).
[CrossRef]

Jang, Y. J.

Y. J. Jang and S. W. Kim, “An estimation of a billet temperature during reheating furnace operation,” Int. J. Control Autom. Syst. 5, 43–60 (2007).

Jasper, W. J.

W. J. Jasper, J. Joines, and J. Brenzovich, “Fabric defect detection using genetic algorithm tuned wavelet filter,” J. Text. Inst. 96, 43–54 (2005).
[CrossRef]

W. J. Jasper, S. J. Garnier, and H. Potlapalli, “Texture characterization and defect detection using adaptive wavelets,” Opt. Eng. 35, 3140–3149 (1996).
[CrossRef]

Jeon, Y. J.

Jia, H.

H. Jia, Y. L. Murphey, J. Shi, and T. S. Chang, “An intelligent real-time vision system for surface defect detection,” in Proceedings of the IEEE International Conference on Pattern Recognition, Vol. 3 (2004), pp. 239–242.

Joines, J.

W. J. Jasper, J. Joines, and J. Brenzovich, “Fabric defect detection using genetic algorithm tuned wavelet filter,” J. Text. Inst. 96, 43–54 (2005).
[CrossRef]

Kang, T. J.

Kim, E.

E. Kim, M. S. Kim, J. Y. Choi, S. W. Kim, and J. W. Kim, “Trajectory generation schemes for bipedal ascending and descending stairs using univariate dynamic encoding algorithm for searches (uDEAS),“ Int. J. Control Autom. Syst. 8, 1061–1071 (2010).

Kim, J. W.

E. Kim, M. S. Kim, J. Y. Choi, S. W. Kim, and J. W. Kim, “Trajectory generation schemes for bipedal ascending and descending stairs using univariate dynamic encoding algorithm for searches (uDEAS),“ Int. J. Control Autom. Syst. 8, 1061–1071 (2010).

J. W. Kim, T. Kim, Y. Park, and S. W. Kim, “On load motor parameter identification using dynamic encoding algorithm for searches (uDEAS),” IEEE Trans. Energy Conv. 23, 804–813 (2008).
[CrossRef]

J. W. Kim and S. W. Kim, “A fast computational optimization method: univariate dynamic encoding algorithm for searches (uDEAS),” IEICE Trans. Fundamentals E-90A, 1679–1689 (2007).
[CrossRef]

J. W. Kim and S. W. Kim, “Numerical method for global optimisation: dynamic encoding algorithm for searches,” IEE Proc. Control Theory Applic. 151, 661–668 (2004).

Kim, M. S.

E. Kim, M. S. Kim, J. Y. Choi, S. W. Kim, and J. W. Kim, “Trajectory generation schemes for bipedal ascending and descending stairs using univariate dynamic encoding algorithm for searches (uDEAS),“ Int. J. Control Autom. Syst. 8, 1061–1071 (2010).

Kim, S. C.

Kim, S. W.

D. C. Choi, Y. J. Jeon, J. P. Yun, and S. W. Kim, “Pinhole detection in steel slab images using Gabor filter and morphological features,” Appl. Opt. 50, 5122–5129 (2011).
[CrossRef]

E. Kim, M. S. Kim, J. Y. Choi, S. W. Kim, and J. W. Kim, “Trajectory generation schemes for bipedal ascending and descending stairs using univariate dynamic encoding algorithm for searches (uDEAS),“ Int. J. Control Autom. Syst. 8, 1061–1071 (2010).

J. P. Yun, S. H. Choi, and S. W. Kim, “Vision-based defect detection of scale-covered steel billet surfaces,” Opt. Eng. 48, 037205 (2009).
[CrossRef]

J. P. Yun, S. H. Choi, B. Y. Seo, and S. W. Kim, “Real-time vision-based defect inspection for high-speed steel products,” Opt. Eng. 47, 077204 (2008).
[CrossRef]

J. W. Kim, T. Kim, Y. Park, and S. W. Kim, “On load motor parameter identification using dynamic encoding algorithm for searches (uDEAS),” IEEE Trans. Energy Conv. 23, 804–813 (2008).
[CrossRef]

J. W. Kim and S. W. Kim, “A fast computational optimization method: univariate dynamic encoding algorithm for searches (uDEAS),” IEICE Trans. Fundamentals E-90A, 1679–1689 (2007).
[CrossRef]

Y. J. Jang and S. W. Kim, “An estimation of a billet temperature during reheating furnace operation,” Int. J. Control Autom. Syst. 5, 43–60 (2007).

J. W. Kim and S. W. Kim, “Numerical method for global optimisation: dynamic encoding algorithm for searches,” IEE Proc. Control Theory Applic. 151, 661–668 (2004).

Kim, S. Y.

J. H. Oh, B. J. Yun, S. Y. Kim, and K. H. Park, “A development of the TFT-LCD image defect inspection method based on human visual system,” IEICE Trans. Fundamentals E91-A, 1400–1407 (2008).
[CrossRef]

Kim, T.

J. W. Kim, T. Kim, Y. Park, and S. W. Kim, “On load motor parameter identification using dynamic encoding algorithm for searches (uDEAS),” IEEE Trans. Energy Conv. 23, 804–813 (2008).
[CrossRef]

Kim, T. H.

T. H. Kim, T. H. Cho, Y. H. Moon, and S. H. Park, “Visual inspection system for the classification of solder joints,” Pattern Recogn. 32565–575 (1999).
[CrossRef]

Kumar, A.

A. Kumar, “Neural network based detection of local textile defects,” Pattern Recogn. 36, 1645–1659 (2003).
[CrossRef]

A. Kumar and G. K. H. Pang, “Defect detection in textured materials using Gabor filters,” IEEE Trans. Ind. Appl. 38, 425–440 (2002).
[CrossRef]

Kumar, P.

Alaknanda, R. S. Anand, and P. Kumar, “Flaw detection in radiographic weld images using morphological approach,” NDT and E Int. 39, 29–33 (2006).

Lau, H. T. K.

K. L. Mak, P. Peng, and H. T. K. Lau, “Optimal morphological filter design for fabric defect detection,” in Proceedings of IEEE International Conference on Industrial Technology (2005), pp. 799–804.

Li, X.

X. Li, S. K. Tso, X. P. Guan, and Q. Huang, “Improving automatic detection of defects in castings by applying wavelet technique,” IEEE Trans. Ind. Electron. 53, 1927–1934 (2006).
[CrossRef]

X. Li, S. K. Tso, X. P. Guan, and Q. Huang, “Improving automatic detection of defects in castings by applying wavelet technique,” IEEE Trans. Ind. Electron. 53, 1927–1934 (2006).
[CrossRef]

Liu, W.

W. Liu and Y. Zhao, “Image fusion based on PCA and undecimated discrete wavelet transform,” in Lecture Notes in Computer Science, 2006 (LNCS)4233, (Springer-Verlag, 2006), pp. 481–488.
[CrossRef]

Mak, K. L.

K. L. Mak, P. Peng, and H. T. K. Lau, “Optimal morphological filter design for fabric defect detection,” in Proceedings of IEEE International Conference on Industrial Technology (2005), pp. 799–804.

Menezes, T. I. D. S.

V. H. C. D. Albuquerque, C. C. Silva, T. I. D. S. Menezes, J. P. Farias, and J. M. R. S. Tavares, “Automatic evaluatin of nickel alloy secondary phases from SEM images,” Microsc. Res. Tech. 74, 36–46 (2011).
[CrossRef]

Millan, M. S.

Moon, Y. H.

T. H. Kim, T. H. Cho, Y. H. Moon, and S. H. Park, “Visual inspection system for the classification of solder joints,” Pattern Recogn. 32565–575 (1999).
[CrossRef]

Murphey, Y. L.

H. Jia, Y. L. Murphey, J. Shi, and T. S. Chang, “An intelligent real-time vision system for surface defect detection,” in Proceedings of the IEEE International Conference on Pattern Recognition, Vol. 3 (2004), pp. 239–242.

Nason, G. P.

G. P. Nason and W. Silverman, “The stationary wavelet transform and some statistical application,” in Wavelets and Statistics, A. Antoniadis and G. Oppenheim, eds. (Springer-Verlag, 1995), pp. 281–299.

Nguyen, T.

G. Strang and T. Nguyen, Wavelets and Filter Banks (Wellesley-Cambridge Press, 1996).

Nossek, J. A.

P. Rieder, J. Gotze, and J. A. Nossek, “Parameterization of orthogonal wavelet transform and their implementation,” IEEE. Trans. Circuits Syst. II 45, 217–226 (1998) .

Oh, J. H.

J. H. Oh, B. J. Yun, S. Y. Kim, and K. H. Park, “A development of the TFT-LCD image defect inspection method based on human visual system,” IEICE Trans. Fundamentals E91-A, 1400–1407 (2008).
[CrossRef]

Pang, G. K. H.

X. Z. Yang, G. K. H. Pang, and N. H. C. Yung,” Robust fabric defect detection and classification using multiple adaptive wavelets,” IEE Proc. Vis. Image Signal Process. 152, 715–723 (2005).

X. Z. Yang, G. K. H. Pang, and N. H. C. Yung, “Discriminative fabric defect detection using adaptive wavelets,” Opt. Eng. 41, 3116–3126 (2002).
[CrossRef]

A. Kumar and G. K. H. Pang, “Defect detection in textured materials using Gabor filters,” IEEE Trans. Ind. Appl. 38, 425–440 (2002).
[CrossRef]

Park, K. H.

J. H. Oh, B. J. Yun, S. Y. Kim, and K. H. Park, “A development of the TFT-LCD image defect inspection method based on human visual system,” IEICE Trans. Fundamentals E91-A, 1400–1407 (2008).
[CrossRef]

Park, M. J.

C. S. Cho, B. M. Chung, and M. J. Park, “Development of real-time vision-based fabric inspection system,” IEEE Trans. Ind. Electron. 52, 1073–1079 (2005).
[CrossRef]

Park, S. H.

T. H. Kim, T. H. Cho, Y. H. Moon, and S. H. Park, “Visual inspection system for the classification of solder joints,” Pattern Recogn. 32565–575 (1999).
[CrossRef]

Park, Y.

J. W. Kim, T. Kim, Y. Park, and S. W. Kim, “On load motor parameter identification using dynamic encoding algorithm for searches (uDEAS),” IEEE Trans. Energy Conv. 23, 804–813 (2008).
[CrossRef]

Peng, P.

K. L. Mak, P. Peng, and H. T. K. Lau, “Optimal morphological filter design for fabric defect detection,” in Proceedings of IEEE International Conference on Industrial Technology (2005), pp. 799–804.

Peng, X.

X. Peng, T. Chen, W. Yu, Z. Zhou, and G. Sun, “An online defects inspection method for float glass fabrication based on machine vision,” Int. J. Adv. Manuf. Technol. 39, 1180–1189 (2008).

Pernkopf, F.

F. Pernkopf, “Detection of surface defects on raw steel blocks using Bayesian network classifiers,” Pattern Anal. Applic. 7, 333–342 (2004).

Potlapalli, H.

W. J. Jasper, S. J. Garnier, and H. Potlapalli, “Texture characterization and defect detection using adaptive wavelets,” Opt. Eng. 35, 3140–3149 (1996).
[CrossRef]

Rallo, M.

Rebollo, M. A.

Rieder, P.

P. Rieder, J. Gotze, and J. A. Nossek, “Parameterization of orthogonal wavelet transform and their implementation,” IEEE. Trans. Circuits Syst. II 45, 217–226 (1998) .

Sanchez-Brea, L. M.

Sari-Sarraf, H.

H. Sari-Sarraf and J. S. Goddard, “Vision system for on-loom fabric inspection,” IEEE Trans. Industrial Appl. 35, 1252–1259 (1999).

Seo, B. Y.

J. P. Yun, S. H. Choi, B. Y. Seo, and S. W. Kim, “Real-time vision-based defect inspection for high-speed steel products,” Opt. Eng. 47, 077204 (2008).
[CrossRef]

Shi, J.

H. Jia, Y. L. Murphey, J. Shi, and T. S. Chang, “An intelligent real-time vision system for surface defect detection,” in Proceedings of the IEEE International Conference on Pattern Recognition, Vol. 3 (2004), pp. 239–242.

Shi, P.

Y. Han and P. Shi, “An adaptive level-selecting wavelet transform for texture defect detection,” Image Vision Comput. 25, 1239–1248 (2007).
[CrossRef]

Siegmann, P.

Silva, C. C.

V. H. C. D. Albuquerque, C. C. Silva, T. I. D. S. Menezes, J. P. Farias, and J. M. R. S. Tavares, “Automatic evaluatin of nickel alloy secondary phases from SEM images,” Microsc. Res. Tech. 74, 36–46 (2011).
[CrossRef]

Silverman, W.

G. P. Nason and W. Silverman, “The stationary wavelet transform and some statistical application,” in Wavelets and Statistics, A. Antoniadis and G. Oppenheim, eds. (Springer-Verlag, 1995), pp. 281–299.

Smith, S. W.

S. W. Smith, The Scientist and Engineer’s Guide to Digital Signal Processing (California Technical, 1997).

Sobral, J. L.

J. L. Sobral, “Optimized filters for texture defect detection,” in IEEE International Conference on Image Processing 2005 (IEEE, 2005), pp. 565–568.

Strang, G.

G. Strang and T. Nguyen, Wavelets and Filter Banks (Wellesley-Cambridge Press, 1996).

Sun, G.

X. Peng, T. Chen, W. Yu, Z. Zhou, and G. Sun, “An online defects inspection method for float glass fabrication based on machine vision,” Int. J. Adv. Manuf. Technol. 39, 1180–1189 (2008).

Tao, Y.

Z. Wen and Y. Tao, “Brightness-invariant image segmentation for on-line fruit defect detection,” Opt. Eng. 37, 2948–2952 (1998).
[CrossRef]

Tavares, J. M. R. S.

V. H. C. D. Albuquerque, C. C. Silva, T. I. D. S. Menezes, J. P. Farias, and J. M. R. S. Tavares, “Automatic evaluatin of nickel alloy secondary phases from SEM images,” Microsc. Res. Tech. 74, 36–46 (2011).
[CrossRef]

V. H. C. D. Albuquerque, J. M. R. S. Tavares, and L. M. P. Dur, “Evaluation of delamination damage on composite plates using an artificial neural network for the radiographic image analysis,” J. Compos. Mater. 44, 1139–1159 (2010).
[CrossRef]

P. P. R. Filho, T. D. S. Cavalcante, V. H. C. D. Albuquerque, and J. M. R. S. Tavares, “Brinell and Vickers hardness measurement using image processing and analysis techniques,” J. Test. Eval. 38, 88–94 (2010).

Tso, S. K.

X. Li, S. K. Tso, X. P. Guan, and Q. Huang, “Improving automatic detection of defects in castings by applying wavelet technique,” IEEE Trans. Ind. Electron. 53, 1927–1934 (2006).
[CrossRef]

X. Li, S. K. Tso, X. P. Guan, and Q. Huang, “Improving automatic detection of defects in castings by applying wavelet technique,” IEEE Trans. Ind. Electron. 53, 1927–1934 (2006).
[CrossRef]

Vaidyanathan, P. P.

P. P. Vaidyanathan, Multirate Systems and Filter Banks(Prentice Hall, 1993).

Wen, Z.

Z. Wen and Y. Tao, “Brightness-invariant image segmentation for on-line fruit defect detection,” Opt. Eng. 37, 2948–2952 (1998).
[CrossRef]

Yang, X. Z.

X. Z. Yang, G. K. H. Pang, and N. H. C. Yung,” Robust fabric defect detection and classification using multiple adaptive wavelets,” IEE Proc. Vis. Image Signal Process. 152, 715–723 (2005).

X. Z. Yang, G. K. H. Pang, and N. H. C. Yung, “Discriminative fabric defect detection using adaptive wavelets,” Opt. Eng. 41, 3116–3126 (2002).
[CrossRef]

Yu, W.

X. Peng, T. Chen, W. Yu, Z. Zhou, and G. Sun, “An online defects inspection method for float glass fabrication based on machine vision,” Int. J. Adv. Manuf. Technol. 39, 1180–1189 (2008).

Yun, B. J.

J. H. Oh, B. J. Yun, S. Y. Kim, and K. H. Park, “A development of the TFT-LCD image defect inspection method based on human visual system,” IEICE Trans. Fundamentals E91-A, 1400–1407 (2008).
[CrossRef]

Yun, J. P.

D. C. Choi, Y. J. Jeon, J. P. Yun, and S. W. Kim, “Pinhole detection in steel slab images using Gabor filter and morphological features,” Appl. Opt. 50, 5122–5129 (2011).
[CrossRef]

J. P. Yun, S. H. Choi, and S. W. Kim, “Vision-based defect detection of scale-covered steel billet surfaces,” Opt. Eng. 48, 037205 (2009).
[CrossRef]

J. P. Yun, S. H. Choi, B. Y. Seo, and S. W. Kim, “Real-time vision-based defect inspection for high-speed steel products,” Opt. Eng. 47, 077204 (2008).
[CrossRef]

Yung, N. H. C.

X. Z. Yang, G. K. H. Pang, and N. H. C. Yung,” Robust fabric defect detection and classification using multiple adaptive wavelets,” IEE Proc. Vis. Image Signal Process. 152, 715–723 (2005).

X. Z. Yang, G. K. H. Pang, and N. H. C. Yung, “Discriminative fabric defect detection using adaptive wavelets,” Opt. Eng. 41, 3116–3126 (2002).
[CrossRef]

Zhao, Y.

W. Liu and Y. Zhao, “Image fusion based on PCA and undecimated discrete wavelet transform,” in Lecture Notes in Computer Science, 2006 (LNCS)4233, (Springer-Verlag, 2006), pp. 481–488.
[CrossRef]

Zhou, Z.

X. Peng, T. Chen, W. Yu, Z. Zhou, and G. Sun, “An online defects inspection method for float glass fabrication based on machine vision,” Int. J. Adv. Manuf. Technol. 39, 1180–1189 (2008).

Zhu, Z.

J. Ge, Z. Zhu, D. He, and L. Chen, “A vision-based algorithm for seam detection in a PAW process for large-diameter stainless steel pipes,” Int. J. Adv. Manuf. Technol. 26, 1006–1011 (2005).

Appl. Opt. (2)

Comput. Geosci. (1)

J. S. Goodchild and F. Fueten, “Edge detection in petrographic images using the rotating polarizer stage,” Comput. Geosci. 24, 745–751 (1998).
[CrossRef]

IEE Proc. Control Theory Applic. (1)

J. W. Kim and S. W. Kim, “Numerical method for global optimisation: dynamic encoding algorithm for searches,” IEE Proc. Control Theory Applic. 151, 661–668 (2004).

IEE Proc. Vis. Image Signal Process. (1)

X. Z. Yang, G. K. H. Pang, and N. H. C. Yung,” Robust fabric defect detection and classification using multiple adaptive wavelets,” IEE Proc. Vis. Image Signal Process. 152, 715–723 (2005).

IEEE Trans. Energy Conv. (1)

J. W. Kim, T. Kim, Y. Park, and S. W. Kim, “On load motor parameter identification using dynamic encoding algorithm for searches (uDEAS),” IEEE Trans. Energy Conv. 23, 804–813 (2008).
[CrossRef]

IEEE Trans. Geosci. Remote Sens. (1)

B. Aiazzi, L. Alparone, S. Baronti, and A. Garzelli, “Context-driven fusion of high spatial and spectral resolution images based on oversampled multiresolution analysis,” IEEE Trans. Geosci. Remote Sens. 40, 2300–2312 (2002).
[CrossRef]

IEEE Trans. Ind. Appl. (1)

A. Kumar and G. K. H. Pang, “Defect detection in textured materials using Gabor filters,” IEEE Trans. Ind. Appl. 38, 425–440 (2002).
[CrossRef]

IEEE Trans. Ind. Electron. (3)

X. Li, S. K. Tso, X. P. Guan, and Q. Huang, “Improving automatic detection of defects in castings by applying wavelet technique,” IEEE Trans. Ind. Electron. 53, 1927–1934 (2006).
[CrossRef]

X. Li, S. K. Tso, X. P. Guan, and Q. Huang, “Improving automatic detection of defects in castings by applying wavelet technique,” IEEE Trans. Ind. Electron. 53, 1927–1934 (2006).
[CrossRef]

C. S. Cho, B. M. Chung, and M. J. Park, “Development of real-time vision-based fabric inspection system,” IEEE Trans. Ind. Electron. 52, 1073–1079 (2005).
[CrossRef]

IEEE Trans. Industrial Appl. (1)

H. Sari-Sarraf and J. S. Goddard, “Vision system for on-loom fabric inspection,” IEEE Trans. Industrial Appl. 35, 1252–1259 (1999).

IEEE. Trans. Circuits Syst. II (1)

P. Rieder, J. Gotze, and J. A. Nossek, “Parameterization of orthogonal wavelet transform and their implementation,” IEEE. Trans. Circuits Syst. II 45, 217–226 (1998) .

IEICE Trans. Fundamentals (2)

J. W. Kim and S. W. Kim, “A fast computational optimization method: univariate dynamic encoding algorithm for searches (uDEAS),” IEICE Trans. Fundamentals E-90A, 1679–1689 (2007).
[CrossRef]

J. H. Oh, B. J. Yun, S. Y. Kim, and K. H. Park, “A development of the TFT-LCD image defect inspection method based on human visual system,” IEICE Trans. Fundamentals E91-A, 1400–1407 (2008).
[CrossRef]

Image Vision Comput. (1)

Y. Han and P. Shi, “An adaptive level-selecting wavelet transform for texture defect detection,” Image Vision Comput. 25, 1239–1248 (2007).
[CrossRef]

Int. J. Adv. Manuf. Technol. (2)

X. Peng, T. Chen, W. Yu, Z. Zhou, and G. Sun, “An online defects inspection method for float glass fabrication based on machine vision,” Int. J. Adv. Manuf. Technol. 39, 1180–1189 (2008).

J. Ge, Z. Zhu, D. He, and L. Chen, “A vision-based algorithm for seam detection in a PAW process for large-diameter stainless steel pipes,” Int. J. Adv. Manuf. Technol. 26, 1006–1011 (2005).

Int. J. Control Autom. Syst. (2)

E. Kim, M. S. Kim, J. Y. Choi, S. W. Kim, and J. W. Kim, “Trajectory generation schemes for bipedal ascending and descending stairs using univariate dynamic encoding algorithm for searches (uDEAS),“ Int. J. Control Autom. Syst. 8, 1061–1071 (2010).

Y. J. Jang and S. W. Kim, “An estimation of a billet temperature during reheating furnace operation,” Int. J. Control Autom. Syst. 5, 43–60 (2007).

J. Compos. Mater. (1)

V. H. C. D. Albuquerque, J. M. R. S. Tavares, and L. M. P. Dur, “Evaluation of delamination damage on composite plates using an artificial neural network for the radiographic image analysis,” J. Compos. Mater. 44, 1139–1159 (2010).
[CrossRef]

J. Opt. Soc. Am. A (2)

J. Test. Eval. (1)

P. P. R. Filho, T. D. S. Cavalcante, V. H. C. D. Albuquerque, and J. M. R. S. Tavares, “Brinell and Vickers hardness measurement using image processing and analysis techniques,” J. Test. Eval. 38, 88–94 (2010).

J. Text. Inst. (1)

W. J. Jasper, J. Joines, and J. Brenzovich, “Fabric defect detection using genetic algorithm tuned wavelet filter,” J. Text. Inst. 96, 43–54 (2005).
[CrossRef]

Microsc. Res. Tech. (1)

V. H. C. D. Albuquerque, C. C. Silva, T. I. D. S. Menezes, J. P. Farias, and J. M. R. S. Tavares, “Automatic evaluatin of nickel alloy secondary phases from SEM images,” Microsc. Res. Tech. 74, 36–46 (2011).
[CrossRef]

NDT and E Int. (1)

Alaknanda, R. S. Anand, and P. Kumar, “Flaw detection in radiographic weld images using morphological approach,” NDT and E Int. 39, 29–33 (2006).

Opt. Eng. (5)

J. P. Yun, S. H. Choi, B. Y. Seo, and S. W. Kim, “Real-time vision-based defect inspection for high-speed steel products,” Opt. Eng. 47, 077204 (2008).
[CrossRef]

Z. Wen and Y. Tao, “Brightness-invariant image segmentation for on-line fruit defect detection,” Opt. Eng. 37, 2948–2952 (1998).
[CrossRef]

J. P. Yun, S. H. Choi, and S. W. Kim, “Vision-based defect detection of scale-covered steel billet surfaces,” Opt. Eng. 48, 037205 (2009).
[CrossRef]

W. J. Jasper, S. J. Garnier, and H. Potlapalli, “Texture characterization and defect detection using adaptive wavelets,” Opt. Eng. 35, 3140–3149 (1996).
[CrossRef]

X. Z. Yang, G. K. H. Pang, and N. H. C. Yung, “Discriminative fabric defect detection using adaptive wavelets,” Opt. Eng. 41, 3116–3126 (2002).
[CrossRef]

Pattern Anal. Applic. (1)

F. Pernkopf, “Detection of surface defects on raw steel blocks using Bayesian network classifiers,” Pattern Anal. Applic. 7, 333–342 (2004).

Pattern Recogn. (2)

T. H. Kim, T. H. Cho, Y. H. Moon, and S. H. Park, “Visual inspection system for the classification of solder joints,” Pattern Recogn. 32565–575 (1999).
[CrossRef]

A. Kumar, “Neural network based detection of local textile defects,” Pattern Recogn. 36, 1645–1659 (2003).
[CrossRef]

Other (9)

S. W. Smith, The Scientist and Engineer’s Guide to Digital Signal Processing (California Technical, 1997).

K. L. Mak, P. Peng, and H. T. K. Lau, “Optimal morphological filter design for fabric defect detection,” in Proceedings of IEEE International Conference on Industrial Technology (2005), pp. 799–804.

H. Jia, Y. L. Murphey, J. Shi, and T. S. Chang, “An intelligent real-time vision system for surface defect detection,” in Proceedings of the IEEE International Conference on Pattern Recognition, Vol. 3 (2004), pp. 239–242.

J. L. Sobral, “Optimized filters for texture defect detection,” in IEEE International Conference on Image Processing 2005 (IEEE, 2005), pp. 565–568.

G. P. Nason and W. Silverman, “The stationary wavelet transform and some statistical application,” in Wavelets and Statistics, A. Antoniadis and G. Oppenheim, eds. (Springer-Verlag, 1995), pp. 281–299.

W. Liu and Y. Zhao, “Image fusion based on PCA and undecimated discrete wavelet transform,” in Lecture Notes in Computer Science, 2006 (LNCS)4233, (Springer-Verlag, 2006), pp. 481–488.
[CrossRef]

G. Strang and T. Nguyen, Wavelets and Filter Banks (Wellesley-Cambridge Press, 1996).

I. W. Selesnick, “Maple and the parameterization of orthogonal wavelet bases,” http://taco.poly.edu/selesi/theta2h (1997).

P. P. Vaidyanathan, Multirate Systems and Filter Banks(Prentice Hall, 1993).

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Figures (14)

Fig. 1.
Fig. 1.

Steel wire rod images containing defect region T2=Td and defect-free region T1=Tf: (a) roll mark, (b) scratch.

Fig. 2.
Fig. 2.

Analysis filters divided into HR,0, HC,0, HR,1, and HC,1.

Fig. 3.
Fig. 3.

The results of uDEAS for roll marks: (a) cost value during execution of uDEAS and (b) optimal parameters.

Fig. 4.
Fig. 4.

The energy of vertical, horizontal, and diagonal subimages of Fig. 1(a) using proposed wavelets: (a) vertical details, (b) horizontal details, (c) diagonal details, respectively.

Fig. 5.
Fig. 5.

The energy of vertical, horizontal, and diagonal subimages Fig. 1(a) using conventional Haar wavelets: (a) vertical details, (b) horizontal details, (c) diagonal details, respectively.

Fig. 6.
Fig. 6.

The comparison of energy profile: (a) horizontal details using proposed wavelets, (b) horizontal details using conventional Haar wavelets, (c) energy profiles of A and Á lines in defect region, (d) energy profiles of B and B´ lines in defect-free region.

Fig. 7.
Fig. 7.

The results of uDEAS for scratches: (a) cost value during execution of uDEAS and (b) optimal parameters.

Fig. 8.
Fig. 8.

The energy of vertical, horizontal, and diagonal subimages [Fig. 1(b)] using proposed wavelets: (a) vertical details, (b) horizontal details, (c) diagonal details, respectively.

Fig. 9.
Fig. 9.

The energy of vertical, horizontal, and diagonal subimages [Fig. 1(b)] using conventional Haar wavelets: (a) vertical details, (b) horizontal details, (c) diagonal details, respectively.

Fig. 10.
Fig. 10.

The comparison of the energy profile: (a) horizontal details using proposed wavelets, (b) horizontal details using conventional Haar wavelets, (c) energy profile of the B and B´ lines in the defect region, (d) energy profiles of A and Á lines in defect-free region.

Fig. 11.
Fig. 11.

Steel wire rod images containing defect (a) and (b) roll marks and (c) and (d) scratches, respectively.

Fig. 12.
Fig. 12.

Surface inspection system for steel wire rods: (a) blue LED lighting system and (b) inspection system installed at actual steel production line.

Fig. 13.
Fig. 13.

Steel wire rod image containing roll mark at each step: (a) image to be segmented, (b) the output of optimal wavelet transform, (c) absolute value of (b), (d) binary image with low threshold, (e) binary image with high threshold, (f) binary image with enhanced double threshold, (g) result of defect detection.

Fig. 14.
Fig. 14.

Steel wire rod image containing scratch at each step: (a) image to be segmented, (b) the output of optimal wavelet transform, (c) absolute value of (b), (d) binary image with low threshold, (e) binary image with high threshold, (f) binary image with enhanced double threshold, (g) result of defect detection.

Tables (4)

Tables Icon

Table 1. Minimum Cost Value and Processing Time According to the Variation in the Size of Filter

Tables Icon

Table 2. Effect of the Threshold Values on the Performance of the Defect Detection

Tables Icon

Table 3. Measured Processing Time of Roll-Mark Defect Detection for Each Step

Tables Icon

Table 4. Measured Processing Time of Scratch Defect Detection for Each Step

Equations (25)

Equations on this page are rendered with MathJax. Learn more.

H1(z)H1(z1)+H0(z)H0(z1)=2,
H1(1)=0,H0(1)=0,
Hp(z)=Rm(θ)Λ(z)Rm1(θ)Λ(z)R1(θ)Λ(z)R0,
R(θ)=[cos(θ)sin(θ)sin(θ)cos(θ)]andΛ(z)=[100z1].
Hp(z)=[H00(z)H01(z)H10(z)H11(z)].
H0=H00(z2)+z1H01(z2).
H1=H10(z2)+z1H11(z2).
k=0mθk=π4.
h0,k[j]=h0,k2j={h0,k/2j,k=2jm,ifmZ0,else,h1,k[j]=h1,k2j={h1,k/2j,k=2jm,ifmZ0,else,
Aj+1(m,n)=klh0,k[j]h0,l[j]Aj(m+k,n+l),Wj+1LH(m,n)=klh1,k[j]h0,l[j]Aj(m+k,n+l),Wj+1HL(m,n)=klh0,k[j]h1,l[j]Aj(m+k,n+l),Wj+1HH(m,n)=klh1,k[j]h1,l[j]Aj(m+k,n+l),
E(x,y)=[Wjd(x,y)]2,
μ=1M×Ny=1Nx=1ME(x,y),
J=μ1μ2,
P(θ)=[θC,0θC,1θC,m1columnθR,0θR,1θR,m1row],
θC,m=π4k=0m1θC,k,
θR,m=π4k=0m1θR,k.
fd(bmbm1b1b0)=12m+112m+111bj2j,
fd(scl)fd(sp)fd(scr),
J=μfμd,
IfThigh<R(x,y),thenBhigh(x,y)=1elseBhigh(x,y)=0,IfTlow<R(x,y),thenBlow(x,y)=1elseBlow(x,y)=0.
IfLhigh(n)Llow(m),thenLdouble(n)=Llow(m)elseLdouble(n)=0.
Dilation:D(A,B)=bB(A+b),
Erosion:E(A,B)=bB(A+b).
Thigh=mean{R(x,y)}+αhigh×std{R(x,y)},
Tlow=mean{R(x,y)}+αlow×std{R(x,y)},

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