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Simulation of Automated Visual Inspection Systems for Specular Surfaces Quality Control

  • Juan Manuel García-Chamizo
  • Andrés Fuster-Guilló
  • Jorge Azorín-López
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4872)

Abstract

This paper proposes the use of simulations as a design mechanism for visual inspection systems of specular surfaces. The system requirements and the characteristics of the objects involve a technological design problem for each of the solutions to be developed. A generic model is proposed. It may be adapted or particularised to solve specific inspection problems using simulations. The method results in a flexible low cost design, reducing the distance between the design model and system implementation in a manufacturing procedure. The proposed simulator generates model-based architectures. The paper shows the results on application of metallized automobile logos.

Keywords

automated visual inspection specular surfaces simulation quality control 

References

  1. 1.
    Swaminathan, R., Kang, S.B., Szeliski, R., Criminisi, A., Nayar, S.K.: On the Motion and Appearance of Specularities in Image Sequences. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2350, Springer, Heidelberg (2002)Google Scholar
  2. 2.
    Lin, S., Shum, H.: Separation of Diffuse and Specular Reflection in Color Images. In: Proc. of IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2001 (2001)Google Scholar
  3. 3.
    Bhat, D.N., Nayar, S.K: Stereo and specular refection. International Journal of Computer Vision 26(2), 91–106 (1998)CrossRefGoogle Scholar
  4. 4.
    Schultz, H.: Retrieving Shape Information from Multiple Images of a Specular Surface. IEEE Transactions on Pattern Analysis and Machine Intelligence 16(2) (1994)Google Scholar
  5. 5.
    Oren, M., Nayar, S.K.: A theory of specular surface geometry. International Journal of Computer Vision 24(2), 105–124 (1997)CrossRefGoogle Scholar
  6. 6.
    Svarese, S., Perona, P.: Local Análisis for 3D Reconstruction of Specular Surfaces. In: Proc. of IEEE Computer Society Conference on CVPR 2001, IEEE Computer Society Press, Los Alamitos (2001)Google Scholar
  7. 7.
    Ragheb, H., Hancock, E.R.: A Probabilistic Framework for Specular Shape-form-Shading. In: Proc. 16th International Conference on Pattern Recognition, ICPR 2002 (2002)Google Scholar
  8. 8.
    Pernkopfa, F., O’Leary, P.: Image acquisition techniques for automatic visual inspection of metallic surfaces. NDT&E International, Elsevier 36, 609–617 (2003)CrossRefGoogle Scholar
  9. 9.
    Perard, D.: Automated visual inspection of specular surfaces with structured-lighting reflection techniques, Fortschritt-Berichte VDI, vol. 8 (869). VDI Verlag, Dusseldorf (2001)Google Scholar
  10. 10.
    Puente León, F., Kammel, S.: Inspection of specular and painted surfaces with centralized fusion techniques. Measurement  (2006)Google Scholar
  11. 11.
    Seulin, R., Merienne, F., Gorria, P.: Simulation of specular surface imaging based on computer graphics: application on a vision inspection system. Journal of Applied Signal Processing - Special issue on Applied Visual Inspection, EURASIP 2002 7, 649–658 (2002)Google Scholar
  12. 12.
    Seulin, R., Merienne, F., Gorria, P.: Dynamic lighting system for specular surface inspection. In: Conference on Machine Vision Applications in Industrial Inspection VII, SPIE, vol. 4301, pp. 199–206 (2001)Google Scholar
  13. 13.
    Aluze, D., Merienne, A.F., Dumont, C., Gorria, P.: Vision system for defect imaging, detection and characterization on a specular surface of 3D object. Image and Vision Computing, Elsevier Science 20, 569–580 (2002)CrossRefGoogle Scholar
  14. 14.
    Morel, O., Stolz, C., Gorria, P.: Polarization imaging for 3D inspection of highly reflective metallic objects. Optics and Spectroscopy 101(1), 15–21 (2006)CrossRefGoogle Scholar
  15. 15.
    Newman, T.S., Jain, A.K.: A Survey of automated visual inspection. Computer Vision and Image Understanding 61(2), 231–262 (1995)CrossRefGoogle Scholar
  16. 16.
    Zhang, X., North, W.P.T.: Analysis of 3-D surface waviness on standard artifacts by retroreflective metrology. Optical Engineering 39(1), 183–186 (2000)CrossRefGoogle Scholar
  17. 17.
    Hung, Y.Y., Shang, H.M.: Nondestructive testing of specularly reflective objects using reflection three-dimensional computer vision technique. Optical Engineering 42(5), 1343–1347 (2003)CrossRefGoogle Scholar
  18. 18.
    Rocchini, C., Cignoni, P., Montani, C., Pingi, P., Scopigno, R.: A Low Cost Optical 3D Scanner. In: Computer Graphics Forum. Eurographics 2001 Conference Proc., vol. 20(3), pp. 299–308 (2001)Google Scholar
  19. 19.
    Malamas, E.N., Petrakis, E.G.M., Zervakis, M., Petit, L., Legat, J.D.: A survey on industrial vision systems, applications and tools. Image and Vision Computing 21, 171–188 (2003)CrossRefGoogle Scholar
  20. 20.
    García-Chamizo, J.M., Fuster-Guilló, A., Azorín-López, J.: Automatic Generation of Image Acquisition Conditions for the Quality Control of Specular Surfaces. In: IEEE/SPIE 8th International Conference on Quality Control by Artificial Vision (QCAV 2007), Le Creusot, France (2007)Google Scholar
  21. 21.
    Kammel, S.: Automated optimization of measurement setups for the inspection of specular surfaces. In: Machine Vision and Three-Dimensional Imaging Systems for Inspection and Metrology II, Proc. SPIE 4567, pp. 199–206 (2002)Google Scholar
  22. 22.
    Seulin, R., Merienne, F., Gorria, P.: Machine vision system for specular surface inspection: use of simulation process as a tool for design and optimization. In: International Conference on Quality Control by Artificial Vision, Le Creusot, France, vol. 1, pp. 147–152 (2001)Google Scholar
  23. 23.
    García-Chamizo, J.M., Fuster-Guilló, A., Azorín-López, J.: Visual Input Amplification for Inspecting Specular Surfaces. In: Proc. IEEE ICIP 2006, IEEE, Atlanta, United States (2006)Google Scholar
  24. 24.
    Nicodemus, F.E., Richmond, J.C., Hsia, J.J., Ginsberg, I.W., Limperis, T.: Geometrical considerations & nomenclature for reflectance. NBS Monograph 160, National Bureau of Standards, Washington, D.C. U.S. Department of Commerce (October 1977)Google Scholar
  25. 25.
    Hughes, R.: The Ising Model, Computer Simulation, and Universal Physics. Models as Mediators. Cambridge University Press, Cambridge (1999)Google Scholar
  26. 26.
    Norton, S., Suppe, F.: Why Atmospheric Modeling is Good Science. Changing the Atmosphere: Expert Knowledge and Environmental Governance. MIT Press, Cambridge, MA (2001)Google Scholar
  27. 27.
    Cook, R.L., Torrance, K.E.: A Reflectance Model for Computer Graphics. ACM Transactions on Graphics 1(1), 7–24 (1982)CrossRefGoogle Scholar
  28. 28.
    Ngan, A., Durand, F., Matusik, W.: Experimental Analysis of BRDF Models. In: Proc. of Eurographics Symposium on Rendering (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Juan Manuel García-Chamizo
    • 1
  • Andrés Fuster-Guilló
    • 1
  • Jorge Azorín-López
    • 1
  1. 1.U.S.I. Industrial Information Technology and Computer Networks, Information Technology and Computing Dept. University of Alicante. P.O. Box 99. E-03080. Alicante.Spain

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