Skip to main content

Kalman filter based matching technique for 3D object recognition

  • Poster Session I
  • Conference paper
  • First Online:
Computer Vision — ACCV'98 (ACCV 1998)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1351))

Included in the following conference series:

  • 132 Accesses

Abstract

A recursive matching technique which uses the Kalman filtering for the recognition of 3D objects is presented. We make use of model based methodology in which both the models and scenes are assumed to be described by quadratic equations. The parameter matrices involved in the matrix form of quadratic equations are used in the matching process. The model features derived from these matrices are rotated and translated so as to match with those of the scene. The features consist of Euler parameters which represent the orientation of a surface and translation vector. The matching is formulated so as to apply the Kalman filter equations and the trace of the error covariance matrix (the error measure) guides the search process in pairing a model with a scene.

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

Access this chapter

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. Besl, P.J. and Jain, R.C., Three-Dimensional Object Recognition, Computing Surveys, Vol. 17, No. 1, 1985, pp. 75–145.

    Google Scholar 

  2. Chin, R.T. and Dyer, C.R., Model-Based Recognition in Robot Vision, ACM Computing Surveys, Vol. 18, No. 1, 1986, pp. 67–108.

    Google Scholar 

  3. Chou, J.C.K. and Kamel, M., Quaternions approach to the Kinematic Equation of Rotation, AaAx = AxAb of a Robotic Manipulator, Proc. IEEE Int. Conf, on Robotics and Automation, Philadelphia, 1988, pp. 656–662.

    Google Scholar 

  4. Faugeras, O.D. and Hebert, M., A 3D Recognition and Positioning Algorithm using Geometrical Matching between Primitive Surfaces, Proc. 8th IJCAI, Karlschule, Germany, 1983, pp. 116–120.

    Google Scholar 

  5. Faugeras, O.D. and Hebert, M., The Representation, Recognition and Locating 3D Objects, Int. J. Robotics Research, Vol. 13, 1986, pp. 155–167.

    Google Scholar 

  6. Fisher, R.B., From Surfaces to Objects: Computer Vision and Three Dimensional Analysis, Wiley, New York, 1989.

    Google Scholar 

  7. Grimson, W.E.L. and Lozano-Perez, T., Model-Based Recognition and Localization from Sparse Range data or Tactile data, Int. J. Robotics Research, Vol. 3, 1984, pp. 3–35.

    Google Scholar 

  8. Hall, E.L., Tio, J.B.K., McPherson, C.A., Draper, C.S. and Sadjadi, F.A., Measuring Curved Surfaces for Robot Vision, Computer, Vol. 15, No. 12, 1982, pp. 42–54.

    Google Scholar 

  9. Hanmandlu, M., Rangaiah, C. and Biswas, K.K., Quadrics based matching technique for 3D Object Recognition, Image and Vision Computing, Vol. 10, No. 9, 1992, pp. 577–588.

    Google Scholar 

  10. Hanmandlu,M. and Shantaram, V., Contour Based Matching Technique for 3D Object Recognition using the Kalman filter, Proc. of 3rd Asian Conference on Computer Vision, Hong Kong, Jan. 8–11, 1998.

    Google Scholar 

  11. Paul, R.P., Robot Manipulators: Mathematics, Programming and Control, MIT Press, Cambridge, MA, 1981.

    Google Scholar 

  12. Press, W., Flannery, B., Teukolsky, S. and Vetterling, W., Numerical Recipes in C: The Art of Scientific Computing, Cambridge, 1986.

    Google Scholar 

  13. Sadjadi, F.A. and Hall, E.L., Three Dimensional Moment Invariants, IEEE Trans. on Pattern Analysis and Machine Intelligence., Vol. 2, 1980, pp. 127136.

    Google Scholar 

  14. Suk, M. and Bhandarkar, S.M., Three-Dimensional Object Recognition from Range Images, Springer-Verlag, Tokyo, 1992.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Roland Chin Ting-Chuen Pong

Rights and permissions

Reprints and permissions

Copyright information

© 1997 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Hanmandlu, M., Shantaram, V. (1997). Kalman filter based matching technique for 3D object recognition. In: Chin, R., Pong, TC. (eds) Computer Vision — ACCV'98. ACCV 1998. Lecture Notes in Computer Science, vol 1351. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-63930-6_155

Download citation

  • DOI: https://doi.org/10.1007/3-540-63930-6_155

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-63930-5

  • Online ISBN: 978-3-540-69669-8

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics