Skip to main content
Springer Nature Link
Log in

Kernel Particle Filter for Visual Quality Inspection from Monocular Intensity Images

  • Conference paper
Pattern Recognition (DAGM 2006)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 4174))

Included in the following conference series:

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  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. Rosenhahn, B., Sommer, G.: Pose Estimation in Conformal Geometric Algebra. Journal of Mathematical Imaging and Vision 22, 27–70 (2005)

    Article  MathSciNet  Google Scholar 

  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)

    Article  Google Scholar 

  4. Chang, C., Ansari, R.: Kernel Particle Filter: Iterative Sampling for Efficient Visual Tracking. In: ICIP 2003, pp. 977–980 (2003)

    Google Scholar 

  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. Chang, C., Ansari, R.: Kernel Particle Filter for Visual Tracking. IEEE Signal Processing Letters 12(3), 242–245 (2005)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  9. Borgefors, G.: Distance Transformations in Digital Images. Computer Vision, Graphics, and Image Processing 34, 344–371 (1986)

    Article  Google Scholar 

  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)

    Chapter  Google Scholar 

  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. Rucklidge, W.: Efficient Visual Recognition Using the Hausdorff Distance. LNCS, vol. 1173. Springer, Heidelberg (1996)

    Book  MATH  Google Scholar 

  13. Duda, R.O., Hart, P.E.: Pattern Classification and Scene Analysis. Wiley & Sons, New York (1973)

    MATH  Google Scholar 

  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)

    Article  Google Scholar 

  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. 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)

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Applied Computer Science, Faculty of Technology, Bielefeld University, P.O. Box 100131, 33501, Bielefeld, Germany

    Dirk Stößel & Gerhard Sagerer

Authors
  1. Dirk Stößel
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Gerhard Sagerer
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Norwegian Information Security Laboratory, Gjøvik University College, Norway

    Katrin Franke

  2. Fraunhofer FIRST (IDA), Berlin, Germany

    Klaus-Robert Müller

  3. Department of Security Technology, Fraunhofer Institute for Production Systems and Design Technology (IPK), Pascalstr. 8-9, 10587, Berlin, Germany

    Bertram Nickolay

  4. Department of Electronic Imaging Technology, Fraunhofer Institute for Information and Communication Technology, Heinrich Hertz Institute (HHI), Einsteinufer 37, 10587, Berlin, Germany

    Ralf Schäfer

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Stößel, D., Sagerer, G. (2006). Kernel Particle Filter for Visual Quality Inspection from Monocular Intensity Images. In: Franke, K., Müller, KR., Nickolay, B., Schäfer, R. (eds) Pattern Recognition. DAGM 2006. Lecture Notes in Computer Science, vol 4174. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11861898_60

Download citation

  • DOI: https://doi.org/10.1007/11861898_60

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-44412-1

  • Online ISBN: 978-3-540-44414-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics