Detecting Defects in Textile Fabrics with Optimal Gabor Filters
This paper investigates the problem of automated defect
detection for textile fabrics and proposes a new optimal filter design
method to solve this problem. Gabor Wavelet Network (GWN) is
chosen as the major technique to extract the texture features from
textile fabrics. Based on the features extracted, an optimal Gabor filter
can be designed. In view of this optimal filter, a new semi-supervised
defect detection scheme is proposed, which consists of one real-valued
Gabor filter and one smoothing filter. The performance of the scheme
is evaluated by using an offline test database with 78 homogeneous
textile images. The test results exhibit accurate defect detection with
low false alarm, thus showing the effectiveness and robustness of the
proposed scheme. To evaluate the detection scheme comprehensively,
a prototyped detection system is developed to conduct a real time test.
The experiment results obtained confirm the efficiency and
effectiveness of the proposed detection scheme.
[1] 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., pt. 1, vol. 83, no. 3, pp. 431-448,
1992.
[2] H. Sari-Sarraf and J. S. Goddard, "Vision systems for on-loom fabric
inspection", IEEE Trans. Ind. Appl., vol. 35, pp. 1252-1259, Nov-Dec,
1999.
[3] J. Wang, R.A. Campbell, and R.J. Harwood, "Automated inspection of
carpets", in Proc. SPIE, vol. 2345, 1995, pp. 180-191.
[4] J. Escofet, R. Navarro, M. S. Millan, and J. Pladelloreans, "Detection of
local defects in textiles webs using Gabor filters", Opt. Eng., vol. 37, pp.
2297-2307, Aug. 1998.
[5] A. Kumar and G. Pang, "Fabric defect segmentation using multichannel
blob defectors", Opt. Eng., vol. 39, no.12, pp. 3176-3190, 2000.
[6] A. Kumar and G.K.H. Pang, "Defect detection in textured materials
using Gabor filters", IEEE Trans. Ind. Appl., vol. 38, no.2, pp. 425-440,
2002.
[7] A. Bodnarova, M. Bennamoun and S. Latham, "Optimal Gabor filters
for textile flaw detection", Pattern Recognition, vol. 35, pp. 2973-2991,
2002.
[8] P. Vautrot, N. Bonnet and M. Herbin, "Comparative study of different
spatial/spatial-frequency methods (Gabor filters, wavelets, wavelets
packets) for texture segmentation/classification", in Proceedings of the
1996 IEEE Inter. Conf. Image Processing, ICIP-96, vol. 3, 1996, pp.
145-148.
[9] A. Teuner, O. Pichler and B.J. Hosticka, "Unsupervised texture
segmentation of images using tuned matched Gabor fitlers", IEEE
Trans. Image Processing, vol. 4, no. 6, pp. 863-870, 1995.
[10] D. Casasent and J.S. Smokelin, "Neural net design of macro Gabor
wavelet filters for distortion-invariant object detection in clutter", Opt.
Eng., vol. 33, no. 7, pp. 2264-2271, 1994.
[11] R. Mehrotra, K.R. Namuduri, and N. Ranganathan, "Gabor Filter-Based
Edge Detection", Pattern Recognition, vol. 25, no. 12, pp. 1479-1494,
1992.
[12] Qinghua Zhang and Albert Benveniste, "Wavelet networks", IEEE
transactions on Neural Networks, vol. 3, no. 6, pp. 889-898, November
1992.
[13] V. Krueger and G. Sommer, "Gabor wavelet networks for object
representation", DAGM Symposium, Germany, September 2000, pp.
13-15.
[14] Volker Kruger and Gerald Sommer, "Gabor wavelet networks for
efficient heard pose estimation", Image and vision computing, vol. 20,
pp. 665-672, 2002.
[15] V. Krueger, "Gabor wavelet networks for object representation", Ph.D
thesis, Christian Albrechts University, Germany, 2001.
[16] Rogerio S. Feris and Roberto M. Cesar Junior, "Tracking facial features
using Gabor wavelet networks", Computer graphics and image
proceeding, proceedings XII Brazalian Symposium on, 2000, pp. 22-27.
[17] K. L. Mak and P. Peng, "Defect Detection in Textile Fabrics Using
Gabor Wavelet Networks", 18th International Conference on Computer
Applications in Industry and Engineering, Hawaii, USA, November
9-11, 2005, pp. 226-231.
[18] H. Sari-Sarraf, J.S. Goddard, "Vision systems for on-loom fabric
inspection", IEEE Trans. Ind. Appl., vol. 35, pp. 1252-1259, 1999.
[19] A.K. Jain and K. Karu, "Learning texture discrimination masks", IEEE
Trans. Pattern Anal. Mach. Intell., vol. 18, pp. 195-205, 1996.
[20] A.K. Jain and F. Furrokhnia, "Unsupervised texture segmentation using
Gabor filters", Pattern Recognition, vol. 23, pp. 1167-1186, 1991.
[1] 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., pt. 1, vol. 83, no. 3, pp. 431-448,
1992.
[2] H. Sari-Sarraf and J. S. Goddard, "Vision systems for on-loom fabric
inspection", IEEE Trans. Ind. Appl., vol. 35, pp. 1252-1259, Nov-Dec,
1999.
[3] J. Wang, R.A. Campbell, and R.J. Harwood, "Automated inspection of
carpets", in Proc. SPIE, vol. 2345, 1995, pp. 180-191.
[4] J. Escofet, R. Navarro, M. S. Millan, and J. Pladelloreans, "Detection of
local defects in textiles webs using Gabor filters", Opt. Eng., vol. 37, pp.
2297-2307, Aug. 1998.
[5] A. Kumar and G. Pang, "Fabric defect segmentation using multichannel
blob defectors", Opt. Eng., vol. 39, no.12, pp. 3176-3190, 2000.
[6] A. Kumar and G.K.H. Pang, "Defect detection in textured materials
using Gabor filters", IEEE Trans. Ind. Appl., vol. 38, no.2, pp. 425-440,
2002.
[7] A. Bodnarova, M. Bennamoun and S. Latham, "Optimal Gabor filters
for textile flaw detection", Pattern Recognition, vol. 35, pp. 2973-2991,
2002.
[8] P. Vautrot, N. Bonnet and M. Herbin, "Comparative study of different
spatial/spatial-frequency methods (Gabor filters, wavelets, wavelets
packets) for texture segmentation/classification", in Proceedings of the
1996 IEEE Inter. Conf. Image Processing, ICIP-96, vol. 3, 1996, pp.
145-148.
[9] A. Teuner, O. Pichler and B.J. Hosticka, "Unsupervised texture
segmentation of images using tuned matched Gabor fitlers", IEEE
Trans. Image Processing, vol. 4, no. 6, pp. 863-870, 1995.
[10] D. Casasent and J.S. Smokelin, "Neural net design of macro Gabor
wavelet filters for distortion-invariant object detection in clutter", Opt.
Eng., vol. 33, no. 7, pp. 2264-2271, 1994.
[11] R. Mehrotra, K.R. Namuduri, and N. Ranganathan, "Gabor Filter-Based
Edge Detection", Pattern Recognition, vol. 25, no. 12, pp. 1479-1494,
1992.
[12] Qinghua Zhang and Albert Benveniste, "Wavelet networks", IEEE
transactions on Neural Networks, vol. 3, no. 6, pp. 889-898, November
1992.
[13] V. Krueger and G. Sommer, "Gabor wavelet networks for object
representation", DAGM Symposium, Germany, September 2000, pp.
13-15.
[14] Volker Kruger and Gerald Sommer, "Gabor wavelet networks for
efficient heard pose estimation", Image and vision computing, vol. 20,
pp. 665-672, 2002.
[15] V. Krueger, "Gabor wavelet networks for object representation", Ph.D
thesis, Christian Albrechts University, Germany, 2001.
[16] Rogerio S. Feris and Roberto M. Cesar Junior, "Tracking facial features
using Gabor wavelet networks", Computer graphics and image
proceeding, proceedings XII Brazalian Symposium on, 2000, pp. 22-27.
[17] K. L. Mak and P. Peng, "Defect Detection in Textile Fabrics Using
Gabor Wavelet Networks", 18th International Conference on Computer
Applications in Industry and Engineering, Hawaii, USA, November
9-11, 2005, pp. 226-231.
[18] H. Sari-Sarraf, J.S. Goddard, "Vision systems for on-loom fabric
inspection", IEEE Trans. Ind. Appl., vol. 35, pp. 1252-1259, 1999.
[19] A.K. Jain and K. Karu, "Learning texture discrimination masks", IEEE
Trans. Pattern Anal. Mach. Intell., vol. 18, pp. 195-205, 1996.
[20] A.K. Jain and F. Furrokhnia, "Unsupervised texture segmentation using
Gabor filters", Pattern Recognition, vol. 23, pp. 1167-1186, 1991.
@article{"International Journal of Mechanical, Industrial and Aerospace Sciences:61860", author = "K. L. Mak and P. Peng", title = "Detecting Defects in Textile Fabrics with Optimal Gabor Filters", abstract = "This paper investigates the problem of automated defect
detection for textile fabrics and proposes a new optimal filter design
method to solve this problem. Gabor Wavelet Network (GWN) is
chosen as the major technique to extract the texture features from
textile fabrics. Based on the features extracted, an optimal Gabor filter
can be designed. In view of this optimal filter, a new semi-supervised
defect detection scheme is proposed, which consists of one real-valued
Gabor filter and one smoothing filter. The performance of the scheme
is evaluated by using an offline test database with 78 homogeneous
textile images. The test results exhibit accurate defect detection with
low false alarm, thus showing the effectiveness and robustness of the
proposed scheme. To evaluate the detection scheme comprehensively,
a prototyped detection system is developed to conduct a real time test.
The experiment results obtained confirm the efficiency and
effectiveness of the proposed detection scheme.", keywords = "Defect detection, Filtering, Gabor function, Gaborwavelet networks, Textile fabrics.", volume = "2", number = "1", pages = "67-6", }
