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Image-based self-localization by means of zero phase representation in panoramic images

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Advances in Pattern Recognition — ICAPR 2001 (ICAPR 2001)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2013))

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Abstract

The paradigm - image-based localization using panoramic images - is elaborated. Panoramic images provide complete views of an environment and their information content does not change if a panoramic camera is rotated. The “zero phase representation” of cylindrical panoramic images, an example of a rotation invariant representation, is constructed for the class of images which have non-zero first harmonic in column direction. It is an invariant and fully discriminative representation. The zero phase representation is demonstrated by an experiment with real data and it is shown that the alternative autocorrelation representation is outperformed.

This work was supported by the MSMT VS96049, J04/98:210000012 and GACR 102/00/1679 grants.

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References

  1. N. Aihara, H. Iwasa, N. Yokoya, and H. Takemura. Memory-based self-localization using omnidirectional images. In 14th Int. Conf. on Pattern Recognition, Brisbane, Australia, volume II, pages 1799–1803. IEEE Computer Society Press, August 1998.

    Google Scholar 

  2. S. Baker and S. K. Nayar. A theory of catadioptric image formation. In 6th Int. Conf. on Computer Vision, pages 35–42, India, January 1998. IEEE Computer Society, Narosa Publishing House.

    Google Scholar 

  3. T. S. Collet. Insect navigation en route to the goal: Multiple strategies for the use of landmarks. J. Exp. Biol., 199.01:227–235, 1996.

    Google Scholar 

  4. M. Jogan and A. Leonardis. Mobile robot localization using panoramic eigen-images. In 4th Computer Vision Winter Workshop, Rastenfeld, Austria. Pattern Recognition and Image Processing Group of the Vienna University of Technology, pages 13–23, February 1999.

    Google Scholar 

  5. S.P.D. Judd and T. S. Collet. Multiple stored views and landmark guidance in ants. Nature, 392:710–714, April 1998.

    Google Scholar 

  6. H. G. Krapp and R. Hengstenberg. Estimation of self-motion by optic ow processing in single visual interneurons. Nature, 384:463–466, 1996.

    Article  Google Scholar 

  7. S. K. Nayar, S. A. Nene, and H. Murase. Subspace methods for robot vision. RA, 12(5):750–758, October 1996.

    Google Scholar 

  8. R. C. Nelson and J. Aloimonos. Finding motion parameters from spherical motion fields (or advantages of having eyes in the back of your head). Biol. Cybern., 58:261–273, 1996.

    Article  Google Scholar 

  9. E. Oja. Subspace Methods of Patter Recognition. Research Studies Press, Hert-fordshire, 1983.

    Google Scholar 

  10. T. Pajdla. Robot localization using shift invariant representation of panoramic images. Tech. Rep. K335-CMP-1998-170, Czech Technical University, Dpt. of Cybernetics, Faculty of Electrical Engineering, Karlovo nám. 13, 12135 Praha, Czech Republic, November 1998. ftp://cmp.felk.cvut.cz/pub/cmp/articles/pajdla/Pajdla-TR-170.ps.gz.

  11. T. Pajdla. Robot localization using panoramic images. In 4th Computer Vision Winter Workshop, Rastenfeld, Austria. Pattern Recognition and Image Processing Group of the Vienna University of Technology, pages 1–12, February 1999.

    Google Scholar 

  12. M.V. Srinivasan. An image-interpolation technique for the computation of optical flow and egomotion. Biol Cybern, 71:401–415, 1994.

    Article  MATH  Google Scholar 

  13. M.V. Srinivasan. Ants match as they march. Nature, 392:660–661, 1998.

    Article  Google Scholar 

  14. M. V. Srinivasan and S. W. Zhang. Flies go with the flow. Nature, 384:411, 1996.

    Google Scholar 

  15. T. Svoboda, T. Pajdla, and V. Hlaváč. Epipolar geometry for panoramic cameras. In 5th European Conference on Computer Vision, Freiburg, Germany, number 1406 in Lecture Notes in Computer Science, pages 218–232. Springer, June 1998.

    Google Scholar 

  16. K. Weber, S. Venkatesh, and M.V. Srinivasan. An insect-based approach to robotic homing. In 14th Int. Conf. on Pattern Recognition, Brisbane, Australia, volume I, pages 297–299. IEEE Computer Society Press, August 1998.

    Google Scholar 

  17. Y. Yagi, K. Yamazawa, and M. Yachida. Rolling motion estimation for mobile robot by using omnidirectional image sensor hyperomnivision. In 13th Int. Conf. on Pattern Recognition, Vienna, Austria, pages 946–950. IEEE Computer Society Press, September 1996.

    Google Scholar 

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Author information

Authors and Affiliations

  1. Center for Machine Perception, Faculty of Electrical Engineering, Czech Technical University, Karlovo náměstí 13, 121 35, Prague 2, Czech Republic

    Tomáš Pajdla & Václav Hlaváč

Authors
  1. Tomáš Pajdla
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  2. Václav Hlaváč
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Editor information

Editors and Affiliations

  1. Department of Computer Science, University of Exeter, EX4 4PT, Exeter, UK

    Sameer Singh

  2. Computational Intelligence Group, Tuiuti University of Parana, Curitiba, Brazil

    Nabeel Murshed

  3. Institute of Computer Aided Automation PRIP-Group 1832, Vienna University of Technology, Favoritenstr. 9/2/4, 1040, Wien, Austria

    Walter Kropatsch

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© 2001 Springer-Verlag Berlin Heidelberg

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Pajdla, T., Hlaváč, V. (2001). Image-based self-localization by means of zero phase representation in panoramic images. In: Singh, S., Murshed, N., Kropatsch, W. (eds) Advances in Pattern Recognition — ICAPR 2001. ICAPR 2001. Lecture Notes in Computer Science, vol 2013. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44732-6_3

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  • DOI: https://doi.org/10.1007/3-540-44732-6_3

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-41767-5

  • Online ISBN: 978-3-540-44732-0

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