Skip to main content
SpringerLink
Log in

A learning approach to fixating on 3D targets with active cameras

  • Session F2B: Active Vision
  • 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:

Abstract

Fixation of an active camera pair on a given target requires that the pan and tilt angles of the cameras must be set to bring the target to image centers. However, the calibration needed to achieve a specific configuration of real cameras involves tedious estimation of a number of imaging parameters. Fortunately, this excercise is not essential for fixation if images are acquired and used as feedback during the fixation process to continuously direct the cameras to the target. This paper defines a direct mapping from the changes in the direction of target motion in the image plane to changes in camera angles necessary to reduce the disparity between image center and the image plane target location. The mapping captures camera calibration, as well as other effects such as deviations from the assumed imaging model which are difficult to characterize and capture in calibration. The mapping is formulated as a task in nonlinear function approximation and learnt from real data. For computational efficiency, learning is done at multiple resolutions and using a PROBART network. Experimental results are presented using an active vision system.

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.

References

  1. L. Abbott and N. Ahuja. Surface reconstruction by dynamic integration of focus, camera vergence and stereo. In Proc. IEEE International Conference on Computer Vision, pages 532–543, 1988.

    Google Scholar 

  2. N. Ahuja and A. L. Abbott. Active stereo: Integrating disparity, vergence, focus, aperture, and calibration for surface estimation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 15(10):1007–1029, 1993.

    Google Scholar 

  3. J. Aloimonos, I. Weiss, and A. Bandyopadhyay. Active vision. International Journal of Computer Vision, 1:333–356, 1988.

    Google Scholar 

  4. R. Bajcsy. Active perception. Proceedings of the IEEE, 78:996–1005, 1988.

    Google Scholar 

  5. D. Ballard and C. Brown. Principles of animate vision. In Y. Aloimonos, editor, Active Perception. Hillsdale, N.J.: Lawrence Erlbaum Associates, 1993.

    Google Scholar 

  6. G. A. Carpenter, S. Grossberg, and D. B. Rosen. Fuzzy ART: Fast stable learning and categorization of analog patterns by an adaptive resonance system. Neural Networks, 4:759–771, 1991.

    Google Scholar 

  7. S. Das and N. Ahuja. A comparative study of stereo, vergence, and focus as depth cues for active vision. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition, pages 194–199, 1993.

    Google Scholar 

  8. J. Denavit and R. S. Hartenberg. A kinematic notation for lower-pair mechanisms based on matrices. ASME Journal of Applied Mechanics, pages 215–221, 1955.

    Google Scholar 

  9. E. D. Dickmanns and W. Graefe. Applications of dynamic monocular machine vision. Machine Vision and Applications, 1:241–261, 1988.

    Google Scholar 

  10. N. J. Ferrier. The harvard binocular head. Technical Report 91-8, Harvard Robotics Laboratory, 1991.

    Google Scholar 

  11. F. Fuma, E. P. Krotkov, and J. Summers. The pennsylvania active camera system. Technical Report MS-CIS-86-15, GRASP Laboratory, University of Pennsylvania, 1986.

    Google Scholar 

  12. S. Hutchinson, G. D. Hager, and P. I. Corke. A tutorial on visual servo control. IEEE Transactions on Robotics and Automation, 12(5):651–670, 1996.

    Google Scholar 

  13. S. Marriott and R. F. Harrison. A modified fuzzy artmap architecture for the approximation of noisy mappings. Neural Networks, 8:619–641, 1995.

    Google Scholar 

  14. M. J. Swain and M. Stricker. Promising directions in active vision. Technical Report CS 91-27, University of Chicago, 1991.

    Google Scholar 

  15. W. B. Thompson and J. K. Kearney. Inexact vision. In Proc. Workshop on Motion: Representation and Analysis, pages 15–22, 1986.

    Google Scholar 

  16. G.-Q. Wei and S. D. Ma. Implicit and explicit camera calibration: Theory and experiments. IEEE Transactions on Pattern Analysis and Machine Intelligence, 16:469–480, 1994.

    Google Scholar 

  17. L. Zadeh. Fuzzy sets. Information Control, 8:338–353, 1965.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. The Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, 405 N. Mathews Avenue, 61801, Urbana, IL

    Narayan Srinivasa & Narendra Ahuja

Authors
  1. Narayan Srinivasa
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Narendra Ahuja
    View author publications

    You can also search for this author in PubMed Google Scholar

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

Srinivasa, N., Ahuja, N. (1997). A learning approach to fixating on 3D targets with active cameras. 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_175

Download citation

  • DOI: https://doi.org/10.1007/3-540-63930-6_175

  • 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

Search

Navigation