Abstract
Surface quality inspection is applied in the process of manufacturing products where the appearance is crucial for the product quality and customer acceptance, like for woven fabrics. The predominating approaches to detect defects are feature-based. Recently we investigated an alternative approach utilizing template matching in the context of regular or near-regular textured surface inspection. This paper reveals that the template matching approach belongs to the class of approximate nearest neighbour field (ANNF) algorithms which are common in a different field of image processing, namely structural image editing. By modifying a state-of-the-art ANNF algorithm the advantage of template matching algorithms for defect detection can be shown. Furthermore the importance of the chosen distance function is demonstrated in an explorative study and a concept to determine if the template matching approach is suitable for a given texture and defect type is demonstrated on a set of defect classes and texture types.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Barnes, C., Shechtman, E., Finkelstein, A., Goldman, D.: PatchMatch: A randomized correspondence algorithm for structural image editing. ACM Transactions on Graphics (Proc. SIGGRAPH)Â 28(3) (August 2009)
Barnes, C., Shechtman, E., Goldman, D.B., Finkelstein, A.: The generalized PatchMatch correspondence algorithm. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part III. LNCS, vol. 6313, pp. 29–43. Springer, Heidelberg (2010)
Buades, A., Coll, B.: A non-local algorithm for image denoising. In: Conference on Computer Vision and Pattern Recognition (CVPR), pp. 60–65 (2005)
Chin, R.T.: Automated visual inspection: 1981 to 1987. Computer Vision, Graphics, and Image Processing 41(3), 346–381 (1988)
He, K., Sun, J.: Computing nearest-neighbor fields via propagation-assisted KD-trees. In: Conference on Computer Vision and Pattern Recognition (CVPR), pp. 111–118. IEEE (2012)
Korman, S., Avidan, S.: Coherency sensitive hashing. In: International Conference on Computer Vision (ICCV), pp. 1607–1614. IEEE (2011)
Kumar, A.: Computer-vision-based fabric defect detection: A survey. IEEE Transactions on Industrial Electronics 55(1), 348–363 (2008)
Kumar, N., Zhang, L., Nayar, S.: What is a good nearest neighbors algorithm for finding similar patches in images? In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part II. LNCS, vol. 5303, pp. 364–378. Springer, Heidelberg (2008)
Moser, B.: Similarity measure for image and volumetric data based on Hermann Weyl’s discrepancy measure. IEEE Transactions on Pattern Analysis and Machine Intelligence 33(11), 2321–2329 (2011)
Ngan, H.Y., Pang, G.K., Yung, N.H.: Automated fabric defect detection-a review. Image and Vision Computing 29(7), 442–458 (2011)
Olonetsky, I., Avidan, S.: Treecann - KD tree coherence approximate nearest neighbor algorithm. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part IV. LNCS, vol. 7575, pp. 602–615. Springer, Heidelberg (2012)
Sanby, C., Norton-Wayne, L., Harwood, R.: The automated inspection of lace using machine vision. Mechatronics 5(23), 215–231 (1995)
Stübl, G., Bouchot, J.-L., Haslinger, P., Moser, B.: Discrepancy norm as fitness function for defect detection on regularly textured surfaces. In: Pinz, A., Pock, T., Bischof, H., Leberl, F. (eds.) DAGM/OAGM 2012. LNCS, vol. 7476, pp. 428–437. Springer, Heidelberg (2012)
Sureka, H., Narayanan, P.J.: Mixed-resolution patch-matching. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part VI. LNCS, vol. 7577, pp. 187–198. Springer, Heidelberg (2012)
Yazdi, H.R., King, T.: Automatic machine vision for lace inspection. In: Proceedings of Machine Vision Applications, Architectures, and Systems Integration V, vol. 2908, pp. 109–117 (1996)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Stübl, G., Moser, B., Scharinger, J. (2013). On Approximate Nearest Neighbour Field Algorithms in Template Matching for Surface Quality Inspection. In: Moreno-DÃaz, R., Pichler, F., Quesada-Arencibia, A. (eds) Computer Aided Systems Theory - EUROCAST 2013. EUROCAST 2013. Lecture Notes in Computer Science, vol 8112. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-53862-9_11
Download citation
DOI: https://doi.org/10.1007/978-3-642-53862-9_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-53861-2
Online ISBN: 978-3-642-53862-9
eBook Packages: Computer ScienceComputer Science (R0)