Advertisement

Kernel Particle Filter for Visual Quality Inspection from Monocular Intensity Images

  • Dirk Stößel
  • Gerhard Sagerer
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4174)

Abstract

Industrial part assembly has come a long way and so has visual quality inspection. Nevertheless, the key issue in automated industrial quality inspection, i.e. the pose recovery of the objects under inspection, is still a challenging task for assemblies with more than two rigid parts. This paper presents a system for the pose recovery of assemblies consisting of an arbitrary number of rigid subparts. In an offline stage, the system extracts edge information from CAD models. Online, the system uses a novel kernel particle filter to recover the full pose of the visible subparts of the assembly under inspection. The accuracy of the pose estimation is evaluated and compared to state-of-the-art systems.

Keywords

Kernel Density Estimate Bandwidth Parameter Rigid Part Dual Quaternion Posterior Probability Density 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Goddard, J.S.: Pose and Motion Estimation from Vision using Dual Quaternion-Based Extended Kalman Filtering. PhD thesis, Univ. of Tenessee, Knoxville (1997)Google Scholar
  2. 2.
    Rosenhahn, B., Sommer, G.: Pose Estimation in Conformal Geometric Algebra. Journal of Mathematical Imaging and Vision 22, 27–70 (2005)CrossRefMathSciNetGoogle Scholar
  3. 3.
    Besl, P.J., McKay, N.D.: A method for registration of 3-D shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence 14(2), 239–256 (1992)CrossRefGoogle Scholar
  4. 4.
    Chang, C., Ansari, R.: Kernel Particle Filter: Iterative Sampling for Efficient Visual Tracking. In: ICIP 2003, pp. 977–980 (2003)Google Scholar
  5. 5.
    Schmidt, J., Kwolek, B., Fritsch, J.: Kernel Particle Filter for Real-Time 3D Body Tracking in Monocular Color Images. In: Proc. of Automatic Face and Gesture Recognition, Southampton, UK, pp. 567–572. IEEE, Los Alamitos (2006)Google Scholar
  6. 6.
    Chang, C., Ansari, R.: Kernel Particle Filter for Visual Tracking. IEEE Signal Processing Letters 12(3), 242–245 (2005)CrossRefGoogle Scholar
  7. 7.
    Comaniciu, D., Ramesh, V., Meer, P.: The Variable Bandwidth Mean Shift and Data-Driven Scale Selection. In: ICCV 2001, vol. 1, pp. 438–445. IEEE, Los Alamitos (2001)Google Scholar
  8. 8.
    Smith, S.M., Brady, J.M.: SUSAN - a new approach to low level image processing. International Journal of Computer Vision 23(1), 45–78 (1997)CrossRefGoogle Scholar
  9. 9.
    Borgefors, G.: Distance Transformations in Digital Images. Computer Vision, Graphics, and Image Processing 34, 344–371 (1986)CrossRefGoogle Scholar
  10. 10.
    Stößel, D., Hanheide, M., Sagerer, G., Krüger, L., Ellenrieder, M.M.: Feature and Viewpoint Selection for Industrial Car Assembly. In: Rasmussen, C.E., Bülthoff, H.H., Schölkopf, B., Giese, M.A. (eds.) DAGM 2004. LNCS, vol. 3175, pp. 528–535. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  11. 11.
    Isard, M., Blake, A.: ICONDENSATION: Unifying Low-Level and High-Level Tracking in a Stochastic Framework. In: Burkhardt, H.-J., Neumann, B. (eds.) ECCV 1998. LNCS, vol. 1406, pp. 893–908. Springer, Heidelberg (1998)Google Scholar
  12. 12.
    Rucklidge, W.: Efficient Visual Recognition Using the Hausdorff Distance. LNCS, vol. 1173. Springer, Heidelberg (1996)MATHCrossRefGoogle Scholar
  13. 13.
    Duda, R.O., Hart, P.E.: Pattern Classification and Scene Analysis. Wiley & Sons, New York (1973)MATHGoogle Scholar
  14. 14.
    Comaniciu, D., Meer, P.: Mean Shift: A Robust Approach Toward Feature Space Analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(5), 603–619 (2002)CrossRefGoogle Scholar
  15. 15.
    Kölzow, T.: System zur Klassifikation und Lokalisation von 3D-Objekten durch Anpassung vereinheitlichter Merkmale in Bildfolgen. PhD thesis, Bielefeld University (2002) (in German)Google Scholar
  16. 16.
    von Bank, C., Gavrila, D.M., Wöhler, C.: A Visual Quality Inspection System Based on a Hierarchical 3D Pose Estimation Algorithm. In: Michaelis, B., Krell, G. (eds.) DAGM 2003. LNCS, vol. 2781, pp. 179–186. Springer, Heidelberg (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Dirk Stößel
    • 1
  • Gerhard Sagerer
    • 1
  1. 1.Applied Computer Science, Faculty of TechnologyBielefeld UniversityBielefeldGermany

Personalised recommendations