nach oben

Erschienen in:

2018 | OriginalPaper | Buchkapitel

Defect Detection in Textiles with Co-occurrence Matrix as a Texture Model Description

verfasst von : Karolina Nurzynska, Michał Czardybon

Erschienen in: Combinatorial Image Analysis

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Automatized inspection at textile production lines becomes very important. However, there is still a need to design methods which meet not only demands concerning accuracy of defect detection, but also ones related to the processing time. In this work, a novel approach for defect model definition is presented. It is derived from the idea of co-occurrence matrix. Due to scale incorporation and binarization of the model content it proved to be a very powerful descriptor of the novelties. Moreover, it also satisfies the requirements of short processing time. The defect mask achieved with the introduced method was compared visually to other popular solutions and show a very high accuracy and quality of defect description. The processing time is real-time as the response for a 1MP (megapixel) image is reached within tens of milliseconds.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Vorheriges Kapitel Improvement of Measurement Accuracy of Optical 3D Scanners by Discrete Systematic Error Estimation

Nächstes Kapitel Multiscale Graph-Cut for 3D Segmentation of Compact Objects

Ahonen, T., Hadid, A., Pietikäinen, M.: Face recognition with local binary patterns. In: Pajdla, T., Matas, J. (eds.) ECCV 2004. LNCS, vol. 3021, pp. 469–481. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-24670-1_36CrossRef

Amadasun, M., King, R.: Textural features corresponding to textural properties. IEEE Trans. Syst. Man Cybern. 19(5), 1264–1274 (1989)CrossRef

Blanchard, G., Lee, G., Scott, C.: Semi-supervised novelty detection. J. Mach. Learn. Res. 11, 2973–3009 (2010)MathSciNetMATH

Böttger, T., Ulrich, M.: Real-time texture error detection on textured surfaces with compressed sensing. Pattern Recogn. Image Anal. 26(1), 88–94 (2016)CrossRef

Ding, X., Li, Y., Belatreche, A., Maguire, L.P.: An experimental evaluation of novelty detection methods. Neurocomputing 135, 313–327 (2014)CrossRef

Han, Y., Shi, P.: An adaptive level-selecting wavelet transform for texture defect detection. Image Vis. Comput. 25(8), 1239–1248 (2007)CrossRef

Haralick, R.M., Shanmugam, K., Dinstein, I.: Textural features for image classification. IEEE Trans. Syst. Man Cybern. SMC 3(6), 610–621 (1973)CrossRef

Hoseini, E., Farhadi, F., Tajeripour, F.: Fabric defect detection using auto-correlation function. Int. J. Comput. Theory Eng. 5, 114–117 (2013)CrossRef

Hu, G.H.: Automated defect detection in textured surfaces using optimal elliptical gabor filters. Optik - Int. J. Light Electron Opt. 126(14), 1331–1340 (2015)CrossRef

10.

Iyer, M., Janakiraman, S.: Defect detection in pattern texture analysis. In: 2014 International Conference on Communication and Signal Processing, pp. 172–175, April 2014

11.

Latif-Amet, A., Ertüzün, A., Erçil, A.: An efficient method for texture defect detection: sub-band domain co-occurrence matrices. Image Vis. Comput. 18(6), 543–553 (2000)CrossRef

12.

Navarro, P., Fernandez-Isla, C., Alcover, P., Suardiaz, J.: Defect detection in textures through the use of entropy as a means for automatically selecting the wavelet decomposition level. Sensors (Bassel) 16, 1178 (2016)CrossRef

13.

Nurzynska, K., Kubo, M., Muramoto, K.: Snow particle automatic classification with texture operators. In: 2011 IEEE International Geoscience and Remote Sensing Symposium, pp. 2892–2895, July 2011

14.

Nurzynska, K., Kubo, M., Muramoto, K.: Texture operator for snow particle classification into snowflake and graupel. Atmos. Res. 118, 121–132 (2012)CrossRef

15.

Ojala, T., Pietikäinen, M., Mäenpää, T.: Gray scale and rotation invariant texture classification with local binary patterns. In: Vernon, D. (ed.) ECCV 2000. LNCS, vol. 1842, pp. 404–420. Springer, Heidelberg (2000). https://doi.org/10.1007/3-540-45054-8_27CrossRef

16.

Pimentel, M.A.F., Clifton, D.A., Clifton, L., Tarassenko, L.: A review of novelty detection. Signal Process. 99, 215–249 (2014)CrossRef

17.

Randen, T., Husoy, J.H.: Filtering for texture classification: a comparative study. IEEE Trans. Pattern Anal. Mach. Intell. 21(4), 291–310 (1999)CrossRef

18.

Sari, L., Ertüzün, A.: Texture defect detection using independent vector analysis in wavelet domain. In: 2014 22nd International Conference on Pattern Recognition, pp. 1639–1644, August 2014

19.

Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support vector method for novelty detection. In: Proceedings of the 12th International Conference on Neural Information Processing Systems, NIPS 1999, pp. 582–588. MIT Press, Cambridge (1999)

20.

Vaidelienė, G., Valantinas, J.: The use of Haar wavelets in detecting and localizing texture defects. Image Anal. Stereol. 35(3), 195–201 (2016)CrossRef

21.

Xie, X., Mirmehdi, M.: TEXEMS: texture exemplars for defect detection on random textured surfaces. IEEE Trans. Pattern Anal. Mach. Intell. 29(8), 1454–1464 (2007)CrossRef

22.

Xie, X., Mirmehdi, M.: Texture exemplars for defect detection on random textures. In: Singh, S., Singh, M., Apte, C., Perner, P. (eds.) ICAPR 2005. LNCS, vol. 3687, pp. 404–413. Springer, Heidelberg (2005). https://doi.org/10.1007/11552499_46CrossRef

23.

Yuan, X., Wu, L., Peng, Q.: An improved Otsu method using the weighted object variance for defect detection. Appl. Surface Sci. 349(Suppl. C), 472–484 (2015)CrossRef

Titel: Defect Detection in Textiles with Co-occurrence Matrix as a Texture Model Description
verfasst von: Karolina Nurzynska
Michał Czardybon
Verlag: Springer International Publishing
Buch: Combinatorial Image Analysis
Print ISBN: 978-3-030-05287-4

Electronic ISBN: 978-3-030-05288-1

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-3-030-05288-1_17

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"