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Issue No.11 - November (2010 vol.32)
pp: 2100-2105
Yannick Caulier , Fraunhofer Institute for Integrated Circuits, Fürth
Salah Bourennane , École Centrale Marseille, Instittut Fresnel, Marseille
Image capturing and image content description can be regarded as the two major steps of a computer vision process. This paper focuses on both within the field of specular surface inspection, by generalizing a previously defined stripe-based inspection method to free-form surfaces on the basis of a specific stripe illumination technique and by outlining a general feature-based stripe image characterization approach by means of new theoretical concepts. One major purpose of this paper is to propose a general stripe image interpretation approach on the basis of a three-step procedure: 1) comparison of different image content description techniques, 2) fusion of the most appropriate ones, and 3) selection of the optimal features. It is shown that this approach leads to an increase in the classification rates of more than 2 percent between the initial fused set and the selected one. The new contributions encompass 1) the generalization of a cylindrical specular surface enhancement technique to more complex specular geometries, 2) the generalization of the previously defined stripe image description by using the same number of features for the bright and the dark stripes, and 3) the definition of an optimal, in terms of classification rates and computational costs, stripe feature set.
Deflectometry, specular surfaces, image capturing, image description, stripe pattern, feature selection.
Yannick Caulier, Salah Bourennane, "Visually Inspecting Specular Surfaces: A Generalized Image Capture and Image Description Approach", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.32, no. 11, pp. 2100-2105, November 2010, doi:10.1109/TPAMI.2010.137
[1] J.R. Quinlan, C4.5: Programs for Machine Learning, fifth ed. Morgan Kaufmann, 2003.
[2] S.S. Keerthi, S.K. Shevade, C. Bhattacharya, and K.R.K. Murthy, "Improvements to Platt's SMO Algorithm for SVM Classifier Design," Neural Computation, vol. 13, no. 3, pp. 637-649, 2001.
[3] L. Breiman and L. Breiman, "Bagging Predictors," Machine Learning, vol. 24, pp. 123-140, 1996.
[4] B. Martin, "Instance-Based Learning: Nearest Neighbor with Generalization," PhD dissertation, Univ. of Waikato, 1995.
[5] G. Häulser, "Verfahren und Vorrichtung zur Ermittlung der Form oder der Abbildungseigenschaften von Spiegelnden oder Transparenter Objekten," Patent DE 19944354 A1, 1999.
[6] A. Williams, "Streifenmuster im Spiegelbild," Inspect Magazine, GIT Verlag GmbH & Co. KG, no. 02, 2008.
[7] P. Marino, M. Dominguez, and M. Alonso, "Machine-Vision Based Detection for Sheet Metal Industries," Proc. 25th Ann. Conf. IEEE Industrial Electronics Soc., vol. 3, pp. 1330-1335, Nov./Dec. 1999.
[8] F. Pernkopf and P. O'Leary, "Visual Inspection of Machined Metallic High-Precision Surfaces," EURASIP J. Applied Signal Processing, no. 1, pp. 667-678, Jan. 2002.
[9] S. Kammel, "Deflektometrische Untersuchung Spiegelnd Reflektierender Freiformflächen," PhD dissertation, Univ. of Karlsruhe, 2004.
[10] M. Petz and R. Tutsch, "Optical 3D Measurement of Reflecting Free Form Surfaces," technical report, 2002.
[11] G. Delcroix, R. Seulin, B. Lamalle, P. Gorria, and F. Merienne, "Study of the Imaging Conditions and Processing for the Aspect Control of Specular Surfaces," SPIE J. Electronic Imaging, vol. 10, no. 1, pp. 196-202, Jan. 2001.
[12] R. Seulin, F. Merienne, and P. Gorria, "Machine Vision System for Specular Surface Inspection: Use of Simulation Process as a Tool for Design and Optimization," Proc. Fifth Int'l Conf. Quality Control by Artificial Vision, 2001.
[13] S.K. Nayar, A.C. Sanderson, L.E. Weiss, and D.A. Simon, "Specular Surface Inspection Using Structured Highlight and Gaussian Images," IEEE Trans. Robotics and Automation, vol. 6, no. 2, pp. 208-218, Apr. 1990.
[14] I. Reindl and P. O'Leary, "Instrumentation and Measurement Method for the Inspection of Peeled Steel Rods," Proc. IEEE Conf. Instrumentation and Measurement, pp. 1-6, May 2007.
[15] F.P. Leon and J. Beyerer, "Active Vision and Sensor Fusion for Inspection of Metallic Surfaces," Intelligent Robots and Computer Vision XVI: Algorithms, Techniques, Active Vision, and Materials Handling, D.P. Casasent ed., pp. 394-405, SPIE, Oct. 1997.
[16] R. Woodham, Y. Iwahori, and R. Barman, "Photometric Stereo: Lambertian Reflectance and Light Sources with Unknown Direction and Strength," Technical Report TR-91-18, Univ. of British Columbia, 1991.
[17] Y. Caulier and K. Spinnler, "Ein Neues System zur Schnellen Prüfung Metallischer Oberflächen von Rohren und Stangen," product information, , 2010.
[18] Y. Caulier, K. Spinnler, S. Bourennane, and T. Wittenberg, "New Structured Illumination Technique for the Inspection of High Reflective Surfaces," EURASIP J. Image and Video Processing, 2008, doi: 10.1155/2008/237459.
[19] Y. Caulier, "Surface Inspection by Means of 1d Sensor and Structured Illumination," PhD dissertation, Univ. Erlangen-Nürnberg, Univ. Aix-Marseille III, June 2008.
[20] Y. Caulier, K. Spinnler, T. Wittenberg, and S. Bourennane, "Specific Features for the Analysis of Fringe Images," J. Optical Eng., vol. 47, no. 5, p. 057201, May 2008.
[21] J. Dy, C. Brodley, A. Kak, L. Broderick, and A. Aisen, "Unsupervised Feature Selection Applied to Content-Based Retrieval of Lung Images," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 25, no. 3, pp. 373-378, Mar. 2003.
[22] H. Peng, F. Long, and C. Ding, "Feature Selection Based on Mutual Information: Criteria of Max-Dependency, Max-Relevance, and Min-Redundancy," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 27, no. 8, pp. 1226-1238, Aug. 2005.
[23] S. Raudys and A. Jain, "Small Sample Size Effects in Statistical Pattern Recognition: Recommendations for Practitioners," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 13, no. 3, pp. 252-264, Mar. 1991.
[24] T. Randen and J. Husoy, "Filtering for Texture Classification: A Comparative Study," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 21, no. 4, pp. 291-310, Apr. 1999.
[25] T. Wagner and C. Kueblbeck, "Automatic Configuration of Systems for Surface Inspection," Proc. Conf. Machine Vision Applications in Industrial Inspection, pp. 128-138, 1996.
[26] T. Wagner, "Automatische Konfiguration von Bildverarbeitungssysteme," PhD dissertation, Univ. of Erlangen-Nürnberg, 1999.
[27] M. Tuceyran, "Texture Analysis," The Handbook of Pattern Recognition and Computer Vision, C.H. Chen, L.F. Pau, and P.S.P. Wang eds., second ed., pp. 207-248, World Scientific Publishing Co., 1998.
[28] W. Jüptner, T. Kreis, U. Mieth, and W. Osten, "Application of Neural Networks and Knowledge-Based Systems for Automatic Identification of Fault-Indicating Fringe Patterns," Proc. SPIE Photomechanics, pp. 16-26, 1994.
[29] H. Zhi and R.B. Johansson, "Interpretation and Classification of Fringe Patterns," Proc. 11th Int'l Conf. Image, Speech and Signal Analysis, vol. 3, pp. 105-108, Aug. 1992.
[30] H.I.M. Takeda and S. Kobayashi, "Fourier-Transform Method of Fringe-Pattern Analysis for Computer-Based Topography and Interferometry," J. Optical Soc. Am., vol. 72, no. 1. pp. 156-160, 1982.
[31] K. Qian, H.S. Seah, and A. Asundi, "Fault Detection by Interferometric Fringe Pattern Analysis Using Windowed Fourier Transform," Measurement Science and Technology, vol. 15, pp. 1582-1587, 2005.
[32] S. Krüger, G. Wernicke, W. Osten, D. Kayser, N. Demoli, and H. Gruber, "Fault Detection and Feature Analysis in Interferometric Fringe Patterns by the Application of Wavelet Filters in Convolution Processors," J. Electronic Imaging, vol. 10, no. 1, pp. 228-232, 2001.
[33] X. Li, "Wavelet Transform for Detection of Partial Fringe Patterns Induced by Defects in Nondestructive Testing of Holographic Interferometry and Electronic Speckle Pattern Interferometry," J. Optical Eng., vol. 39, pp. 2821-2827, Oct. 2000.
[34] Y. Chen, M. Nixon, and D. Thomas, "Statistical Geometrical Features for Texture Classification," Pattern Recognition, vol. 28, no. 4, pp. 537-552, 1995.
[35] R.M. Haralick, K. Shanmugam, and I. Dinstein, "Texture Features for Image Classification," IEEE Trans. System, Man, and Cybernetics, vol. 3, no. 6, pp. 610-621, Nov. 1973.
[36] J. Weska, "A Survey of Threshold Selection Techniques," Computer Graphics and Image Processing, vol. 7, no. 2, pp. 259-265, Apr. 1978.
[37] R. Kohavi and G.H. John, "Wrappers for Feature Subset Selection," Artificial Intelligence, vol. 97, pp. 273-324, May 2003.
[38] M. Farmer and A. Jain, "A Wrapper-Based Approach to Image Segmentation and Classification," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 14, no. 12, pp. 2060-2072, Dec. 2005.
[39] M. Law, M. Figueiredo, and A. Jain, "Simultaneous Feature Selection and Clustering Using Mixture Models," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 26, no. 9, pp. 1154-1166, Sept. 2004.
[40] R. Gutierrez-Osuna, "Pattern Analysis for Machine Olfaction: A Review," IEEE Sensors J., vol. 2, no. 3, pp. 189-202, June 2002.
[41] M.A. Hall, "Correlation-Based Feature Selection for Machine Learning," PhD dissertation, Univ. of Waikato, 1999.
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