Abstract
Defect detection on painted metallic surfaces is a challenging task in inspection due to the varying illuminative and reflective structure of the surface. This paper proposes a novelty detection scheme that models the defect-free surfaces by using Gaussian Mixture Models (GMMs) trained in Gabor space. It is shown that training using the texture representations obtained by Gabor filtering takes the advantage of multiscale analysis while reducing the computational complexity. Test results reported on defected metallic surfaces including pinhole, crater, hav, dust, scratch, and mound type of abnormalities demonstrate the superiority of developed integrated system with respect to the stand alone Gabor filtering as well as the spatial domain GMM classification.
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Jain, A.K., Farrokhnia, F., Alman, D.: Texture Analysis of Automotive Finishes. In: Proc.Vision ’90, pp. 8.1–8.16. Detroit Michigan (1990)
Xie, X., Mirmehdi, M.: TEXEMS: Texture Exemplars for Defect Detection on Random Textured Surfaces. IEEE Transactions On Pattern Analysis and Machine Intelligence 29(8), 1454–1464 (2007)
Kumar, A., Pang, G.K.H.: Defect Detection in Textured Materials Using Gabor Filters. IEEE Transactions on Industry Applications 38(2), 425–440 (2002)
Escofet, J., Navarro, R., Millán, M.S., Pladellorens, J.: Detection of Local Defects in Textile Webs Using Gabor Filters. Opt. Eng. 37, 3140–3149 (1996)
Daugman, J.G.: Two-Dimensional Spectral Analysis of Cortical Receptive Field Profiles. Vision Research 20, 847–856 (1980)
Liu, C.: Gabor-Based Kernel PCA with Fractional Power Polynomial Models for Face Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 26(5), 572–581 (2004)
Jain, A.K., Farrokhnia, F.: Unsupervised Texture Segmentation Using Gabor Filters. In: Proc. IEEE Conference on System, Man and Cybernetics, pp. 14–19 (1990)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Savran, Y., Gunsel, B. (2010). Novelty Detection on Metallic Surfaces by GMM Learning in Gabor Space. In: Campilho, A., Kamel, M. (eds) Image Analysis and Recognition. ICIAR 2010. Lecture Notes in Computer Science, vol 6112. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13775-4_33
Download citation
DOI: https://doi.org/10.1007/978-3-642-13775-4_33
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13774-7
Online ISBN: 978-3-642-13775-4
eBook Packages: Computer ScienceComputer Science (R0)