Abstract
Fault diagnosis of optical devices in industrial environment is a challenging but crucial task, since it ensures products’ nominal specification and manufacturing control. Defects detection and issued information processing are among chief phases for succeeding in such diagnosis. A new scratches and digs defects detection and characterization method exploiting Nomarski microscopy issued imaging has been developed. It allows automatic check of optical devices during industrial process. Issued images contain several items which have to be detected and then classified in order to discriminate between “false” defects and “abiding” ones. In this paper, a processing method is proposed for a first step of pattern recognition from Nomarski images. A first phase permits to extract items images and a second phase allows us to cluster them using an unsupervised neural network technique, Self-Organizing Map.
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References
P. Bouchareine: Métrologie des Surfaces. Techniques de l’Ingénieur, vol. R1390, 1999.
S. Chatterjee: Design Considerations and Fabrication Techniques of Nomarski Reflection Microscope. Optical Engineering, vol. 42, no. 8, pp. 2202-2212, 2003.
P. E. J. Flewitt and R. K. Wild: Light Microscopy. in Physical Methods for materials characterisation, 1994.
T. Kohonen: Self Organizing Maps, 3rd edition, Berlin: Springer, 2001.
P. Bourgeat, F. Meriaudeau, K. W. Tobin, and P. Gorria: Patterned Wafer Segmentation. Proceedings of SPIE, vol. 5132, no. Quality Control by Artificial Vision VI, pp. 36-44, 2004.
R. C. Gonzalez and R. E. Woods: Digital Image Processing, 2nd edition Addison- Wesley, 2002.
A. Choksuriwong, H. Laurent, and B. Emile: Comparison of invariant descriptors for object recognition. IEEE International Conference on Image Processing (ICIP), pp. 377-380, 2005.
S. Derrode, ‘Représentation de Formes Planes á Niveaux de Gris par Différentes Approximations de Fourier-Mellin Analytique en vue d’Indexation de Bases d’Images.;’ Thèse de Doctorat - Université de Rennes I, 1999.
F. Ghorbel: A Complete Invariant Description for Gray Level Images by the Harmonic Analysis Approach. Pattern Recognition, vol. 15, pp. 1043-1051, 1994.
G. Ravichandran and M. Trivedi: Circular-Mellin features for texture segmentation. IEEE Trans. Image Processing, vol. 4, pp. 1629-1640, 1995.
T. Kohonen, E. Oja, O. Simula, A. Visa, and J. Kangas: Engineering Applications of the Self-Organizing Maps. Proceedings of the IEEE, vol. 84, no. 10, pp. 1358- 1384, Oct.1996.
J. Heikkonen and J. Lampinen: Building Industrial Applications with Neural Networks., Proc. European Symposium on Intelligent Techniques, ESIT’99, 1999.
O. Silvén, M. Niskanen, and H. Kauppinen: Wood Inspection with Non- Supervised Clustering. Machine Vision and Applications, vol. 13, no. 5, pp. 275-285, 2000.
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Voiry, M., Amarger, V., Madani, K., Houbre, F. (2007). Design and Prototyping of an Industrial Fault Clustering System Combining Image Processing and Artificial Neural Network Based Approaches. In: Pejaś, J., Saeed, K. (eds) Advances in Information Processing and Protection. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-73137-7_31
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DOI: https://doi.org/10.1007/978-0-387-73137-7_31
Publisher Name: Springer, Boston, MA
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