Advertisement

Active Surface Reconstruction Using the Gradient Strategy

  • Marcel Mitran
  • Frank P. Ferrie
Conference paper
First Online:
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2353)

Abstract

This paper describes the design and implementation of an active surface reconstruction algorithm for two-frame image sequences using passive imaging. A novel strategy based on the statistical grouping of image gradient features is used. It is shown that the gradient of the intensity in an image can successfully be used to drive the direction of the viewer’s motion. As such, an increased efficiency in the accumulation of information is demonstrated through a significant increase in the convergence rate of the depth estimator (3 to 4 times for the presented results) over traditional passive depth-from-motion. The viewer is considered to be restricted to a short baseline. A maximal-estimation framework is adopted to provide a simple approach for propagating information in a bottom-up fashion in the system. A Kalman filtering scheme is used for accumulating information temporally. The paper provides results for real-textured data to support the findings.

Keywords

Image-features surface geometry structure-from-motion active vision autonomous robot navigation 
Download to read the full conference paper text

References

  1. 1.
    Aloimonos, Y. and Bandyopadhyan, A.: Active Vision. First International Conference on Computer Vision, June 1987.Google Scholar
  2. 2.
    Anandan, P.: A Computational Framework and an Algorithm for Measurement of Visual Motion, International Journal of Computer Vision, Vol. 2, pp. 283–310, 1989.CrossRefGoogle Scholar
  3. 3.
    Azarbayejani, A., Horowitz, B. and Pentland, A.: Recursive Estimation of Structure and Motion using Relative Orientation Constraints. IEEE Conference on Computer Vision and Pattern Recognition, 1993.Google Scholar
  4. 4.
    Bajcsy, R.: Active Perception. Proceedings of the IEEE, Vol. 76, No 8, August 1988.Google Scholar
  5. 5.
    Barron, J.L., Fleet, D.J. and Beauchemin, S.S.: Performance of Optical Flow Techniques. International Journal of Computer Vision, Vol. 12:1, pp. 43–77, 1994.CrossRefGoogle Scholar
  6. 6.
    Beardsley, P.A., Zisserman, A., and Murray, D.W.: Sequential Updating of Projective and Affine Structure from Motion. International Journal of Computer Vision, vol. 23, no. 3, pages 235–259, 1997.CrossRefGoogle Scholar
  7. 7.
    Bertero, M., Poggio, T.A., and Torre, V.: Ill-Posed Problems in Early Vision. Proceedings of the IEEE, Vol. 76, No. 8, pp. 869–889, August 1988.CrossRefGoogle Scholar
  8. 8.
    Cover, T.M. and Thomas, J. A.: Elements of Information Theory. John-Wiley and Sons, 1991.Google Scholar
  9. 9.
    Dudek, G. and Jenkins, M.: Computational Principles of Mobile Robotics. Cambridge University Press, Cambridge, 2000.zbMATHGoogle Scholar
  10. 10.
    Faugeras, O.: Three-Dimensional Computer Vision, MIT Press, Boston, Mass., 1993.Google Scholar
  11. 11.
    Fleet, D.J. and Langley, K.: Recursive Filters for Optical Flow. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 17, No. 1, pp. 61–67, Jan 1995.CrossRefGoogle Scholar
  12. 12.
    Foley, J.D., van Damn, A., Feiner, S.K., and Huges, J.F.: Computer Graphics: Practices and Principles 2nd Edition. Addison Wesley, 1990.Google Scholar
  13. 13.
    Fradkin, M., Roux M., Maître, H., and Leloğlu, U.M.: Surface Reconstruction from Aerial Images in Dense Urban Areas. IEEE Conference on Computer Vision and Pattern Recognition, Vol. 2, pp. 262–267, 1999.Google Scholar
  14. 14.
    Horn, B.K.P: Robot Vision. The MIT Press, Cambridge, Massachusetts, 1986.Google Scholar
  15. 15.
    Huang, L. and Aloimonos, Y.: Relative Depth Motion using Normal Flow: An Active and Purposive Solution. Proceedings of the IEEE Workshop on Visual Motion, 1991.Google Scholar
  16. 16.
    Kang, S.B. and Szeliski, R.: 3-D Scene Data Recovery using Omnidirectional Multibase-line Stereo. Cambridge Research Lab, Technical Report, Oct 1995.Google Scholar
  17. 17.
    Mathur, S., and Ferrie, F.P.: Edge Localization in Surface Reconstruction Using Optimal Estimation Theory. Computer Vision and Pattern Recognition, pp. 833–838, 1997Google Scholar
  18. 18.
    Matthies, L., Kanade, T. and Szeliski, R.: Kalman Filter-based Algorithms for Estimating Depth from Image Sequences, International Journal of Computer Vision, 3, 209–236, 1989.CrossRefGoogle Scholar
  19. 19.
    Negahdaripour, S., Yu, C.H, and Shokrollahi A.H.: Recovering Shape and Motion From Undersea Images. IEEE Journal of Oceanic Engineering, Vol. 15, No. 3, pp 189–198, 1990.CrossRefGoogle Scholar
  20. 20.
    Okutomi M. and Kanade, T.: A Multiple-Baseline Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 15, No. 4, pp. 355–363, April 1993.CrossRefGoogle Scholar
  21. 21.
    Puzicha, J., Hofman, T., and Buhmann J.: Histogram Clustering for Unsupervised Image Segementation. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Vol. 2, pp. 602–608, 1999.Google Scholar
  22. 22.
    Sandini, G. and Tistarelli, M.: Active Tracking Strategy for Monocular Depth Inference Over Multiple Frames. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 12, No. 1, January 1990.Google Scholar
  23. 23.
    Verri, A. and Poggio, T.A.: Motion Field and Optical Flow: Qualitative Properties. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 11, No. 5, May. 1989.Google Scholar
  24. 24.
    Whaite, P. and Ferrie, F.P.: Autonomous Exploration Driven by Uncertainty. IEEE Conference on Computer Vision and Pattern Recognition, June 1994.Google Scholar
  25. 25.
    Xiong, Y. and Shafer, S.: Dense Structure from a Dense Optical Flow Sequence. Computer Vision and Image Understanding, Vol. 69, No. 2, pp. 222–245, 1998.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Marcel Mitran
    • 1
  • Frank P. Ferrie
    • 1
  1. 1.Centre for Intelligent MachinesMcGill UniversityMontrealCanada

Personalised recommendations

Cite paper