Skip to main content

Efficient matching of space curves

  • Conference paper
  • First Online:
Computer Analysis of Images and Patterns (CAIP 1995)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 970))

Included in the following conference series:

Abstract

The aim is to provide invariant signatures for matching space curves under Euclidean motions. Semi-differential rather than differential invariants can be used for the description and recognition of space curves. They have the advantage of being more robust, but on the other hand introduce the problem of finding good reference points. In order to avoid a search for such points, an alternative scheme is propounded. Strategies are used that make the reference point dependent on the point where the shape is being described. Actually, the reference point is attached to the point under scrutiny in an invariant manner. “Sliding Pairs of Constant Total Curvature” were seen experimentally to provide stable point pairs on all scales allowing to reduce the size of the description from n 2 to n, where n is the number of evenly spaced sample points taken from a curve.

This work has been done during the visit of T. Pajdla at ESAT, K.U.Leuven. Support by Esprit Basic Research Action ‘VIVA’ and project IUAP-50 (Inter-Universitaire Attractie-Pool) on Robotics and Industrial Automation, financed by the Belgian Federal Services for Scientific, Technological, and Cultural Affairs, is gratefully acknowledged.

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.

References

  1. E. Kishon, T. Hastie, and H. Wolfson. 3-D curve matching using splines. Journal of Robotic Systems, 8(6): 723–743, 1991.

    Google Scholar 

  2. A. Guéziec and N. Ayache. Smoothing and matching of 3-D spatial curves. International Journal of Computer Vision, 12(1): 79–104, 1994.

    Google Scholar 

  3. T. Pajdla and L. Van Gool. Matching of 3-D curves using semi-differential invariants. In 5th International Conference on Computer Vision, Cambridge, MA, June 1995.

    Google Scholar 

  4. J.T. Schwartz and M. Sharir. Identification of partially obscured objects in two or three dimensions by matching of noisy characteristic curves. The International Journal of Robotics Research, 6: 29–44, 1987.

    Google Scholar 

  5. L. Van Gool, T. Moons, E. Pauwels, and A. Oosterlinck. Applications of Invariance in Vision, pages 157–192. MIT Press, Boston, 1992.

    Google Scholar 

  6. T. Pajdla and L. Van Gool. Euclidean invariant descriptions for matching of curves extracted from range images. ESAT MI2 Technical Report Nr. KUL/ESAT/MI2/9417, Katholieke Universiteit Leuven, Belgium, 1994.

    Google Scholar 

  7. R.I. Hartley. Euclidean reconstruction from uncalibrated views. In 2nd ESPRIT — ARPA Workshop on Invariants in Computer Vision, pages 187–202, Ponta Delgada, Azores, October 1993.

    Google Scholar 

  8. M.P. doCarmo. Differential Geometry of Curves and Surfaces. Engelwood Cliffs, New Jersey, 1976.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Václav Hlaváč Radim Šára

Rights and permissions

Reprints and permissions

Copyright information

© 1995 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Pajdla, T., van Gool, L. (1995). Efficient matching of space curves. In: Hlaváč, V., Šára, R. (eds) Computer Analysis of Images and Patterns. CAIP 1995. Lecture Notes in Computer Science, vol 970. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-60268-2_276

Download citation

  • DOI: https://doi.org/10.1007/3-540-60268-2_276

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-60268-2

  • Online ISBN: 978-3-540-44781-8

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics