Advertisement

Feature Based Methods for Structure and Motion Estimation

  • P. H. S. Torr
  • A. Zisserman
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1883)

Abstract

This report is a brief overview of the use of “feature based” methods in structure and motion computation. A companion paper by Irani and Anandan [16] reviews “direct” methods.

Keywords

Computer Vision Motion Estimation Interest Point Fundamental Matrix Bundle Adjustment 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    S. Avidan and A. Shashua. Threading fundamental matrices. In Proc. 5th European Conference on Computer Vision, Freiburg, Germany, pages 124–140, 1998.Google Scholar
  2. 2.
    C. Baillard and A. Zisserman. Automatic reconstruction of piecewise planar models from multiple views. In Proc. IEEE Conference on Computer Vision and Pattern Recognition, pages 559–565, June 1999.Google Scholar
  3. 3.
    P. A. Beardsley, P. H. S. Torr, and A. Zisserman. 3D model aquisition from extended image sequences. In Proc. 4th European Conference on Computer Vision, LNCS 1065, Cambridge, pages 683–695, 1996.Google Scholar
  4. 4.
    P. A. Beardsley, A. Zisserman, and D. W. Murray. Navigation using affine structure and motion. In Proc. European Conference on Computer Vision, LNCS 800/801, pages 85–96. Springer-Verlag, 1994.Google Scholar
  5. 5.
    J. Bergen, P. Anandan, K.J. Hanna, and R. Hingorani. Hierarchical model-based motion estimation. In Proc. European Conference on Computer Vision, LNCS 588, pages 237–252. Springer-Verlag, 1992.Google Scholar
  6. 6.
    J. M. Brady. Seeds of perception. In Proceedings of the 3rd Alvey Vision Conference, pages 259–265, 1987.Google Scholar
  7. 7.
    D. Capel and A. Zisserman. Automated mosaicing with super-resolution zoom. In Proc. IEEE Conference on Computer Vision and Pattern Recognition, Santa Barbara, pages 885–891, June 1998.Google Scholar
  8. 8.
    D. Capel and A. Zisserman. Super-resolution enhancement of text image sequences. In Proc. International Conference on Pattern Recognition, 2000.Google Scholar
  9. 9.
    I.J. Cox, S.L. Hingorani, and S.B. Rao. A maximum likelihood stereo algorithm. Computer vision and image understanding, 63(3):542–567, 1996.CrossRefGoogle Scholar
  10. 10.
    A. W. Fitzgibbon and A. Zisserman. Automatic camera recovery for closed or open image sequences. In Proc. European Conference on Computer Vision, pages 311–326. Springer-Verlag, June 1998.Google Scholar
  11. 11.
    K.J. Hanna and E. Okamoto. Combining stereo and motion analysis for direct estimation of scene structure. In Proc. IEEE Conference on Computer Vision and Pattern Recognition, pages 357–365, 1993.Google Scholar
  12. 12.
    C. J. Harris and M. Stephens. A combined corner and edge detector. In Proc. 4th Alvey Vision Conference, Manchester, pages 147–151, 1988.Google Scholar
  13. 13.
    R. I. Hartley. Self-calibration from multiple views with a rotating camera. In Proc. European Conference on Computer Vision, LNCS 800/801, pages 471–478. Springer-Verlag, 1994.Google Scholar
  14. 14.
    R. I. Hartley and A. Zisserman. Multiple View Geometry in Computer Vision. Cambridge University Press, ISBN: 0521623049, 2000.Google Scholar
  15. 15.
    P. Havaldar and G. Medioni. Segmented shape descriptions from 3-view stereo. In Proc. International Conference on Computer Vision, pages 102–108, 1995.Google Scholar
  16. 16.
    M. Irani and P. Anandan. About direct methods. In Vision Algorithms: Theory and Practice. Springer-Verlag, 2000.Google Scholar
  17. 17.
    M. Irani, P. Anandan, and S. Hsu. Mosaic based representations of video sequences and their applications. In Proc. 5th International Conference on Computer Vision, Boston, pages 605–611, 1995.Google Scholar
  18. 18.
    M. Irani and S. Peleg. Motion analysis for image enhancement: Resolution, occlusion, and transparency. Journal of Visual Communication and Image Representation, 4:324–335, 1993.CrossRefGoogle Scholar
  19. 19.
    M. Irani, B. Rousso, and S. Peleg. Computing occluding and transparent motions. International Journal of Computer Vision, 12(1):5–16, 1994.CrossRefGoogle Scholar
  20. 20.
    R. Koch. 3D surface reconstruction from stereoscopic image sequences. In Proc. 5th International Conference on Computer Vision, Boston, pages 109–114, 1995.Google Scholar
  21. 21.
    K. Kutulakos and S. Seitz. A theory of shape by space carving. In Proc. 7th International Conference on Computer Vision, Kerkyra, Greece, pages 307–314, 1999.Google Scholar
  22. 22.
    S. Laveau. Géométrie d’un système de N caméras. Théorie, estimation et applications. PhD thesis, INRIA, 1996.Google Scholar
  23. 23.
    M. Pollefeys, R. Koch, and L. Van Gool. Self calibration and metric reconstruction in spite of varying and unknown internal camera parameters. In Proc. 6th International Conference on Computer Vision, Bombay, India, pages 90–96, 1998.Google Scholar
  24. 24.
    W. Press, B. Flannery, S. Teukolsky, and W. Vetterling. Numerical Recipes in C Cambridge University Press, 1988.Google Scholar
  25. 25.
    P. Pritchett and A. Zisserman. Matching and reconstruction from widely separated views. In R. Koch and L. Van Gool, editors, 3D Structure from Multiple Images of Large-Scale Environments, LNCS 1506, pages 78–92. Springer-Verlag, June 1998.CrossRefGoogle Scholar
  26. 26.
    C. Rothwell, A. Zisserman, D. Forsyth, and J. Mundy. Planar object recognition using projective shape representation. International Journal of Computer Vision, 16(2), 1995.Google Scholar
  27. 27.
    C. Schmid, R. Mohr, and C. Bauckhage. Comparing and evaluating interest points. In Proc. International Conference on Computer Vision, pages 230–235, 1998.Google Scholar
  28. 28.
    S.M. Seitz and C.R. Dyer. Photorealistic scene reconstruction by voxel coloring. In Proc. IEEE Conference on Computer Vision and Pattern Recognition, Puerto Rico, pages 1067–1073, 1997.Google Scholar
  29. 29.
    G. Stein and A. Shashua. Model-based brightness constraints: on direct estimation of structure and motion. In Proc. IEEE Conference on Computer Vision and Pattern Recognition, pages 400–406, 1997.Google Scholar
  30. 30.
    P. Sturm. Vision 3D non calibrée: Contributions à la reconstruction projective et etude des mouvements critiques pour l’auto calibrage. PhD thesis, INRIA Rhône-Alpes, 1997.Google Scholar
  31. 31.
    P. H. S. Torr, A. W. Fitzgibbon, and A. Zisserman. The problem of degeneracy in structure and motion recovery from uncalibrated image sequences. International Journal of Computer Vision, 32(1):27–44, August 1999.Google Scholar
  32. 32.
    P. H. S. Torr and D. W. Murray. The development and comparison of robust methods for estimating the fundamental matrix. International Journal of Computer Vision, 24(3):271–300, 1997.CrossRefGoogle Scholar
  33. 33.
    P. H. S. Torr and A. Zisserman. Robust parameterization and computation of the trifocal tensor. Image and Vision Computing, 15:591–605, 1997.CrossRefGoogle Scholar
  34. 34.
    P. H. S. Torr and A. Zisserman. Robust computation and parameterization of multiple view relations. In Proc. 6th International Conference on Computer Vision, Bombay, India, pages 727–732, January 1998.Google Scholar
  35. 35.
    P. H. S. Torr, A. Zisserman, and S. Maybank. Robust detection of degenerate configurations for the fundamental matrix. Computer Vision and Image Understanding, 71(3):312–333, September 1998.Google Scholar
  36. 36.
    J. Weber and J. Malik. Rigid body segmentation and shape description from dense optical flow under weak perspective. In Proc. International Conference on Computer Vision, pages 251–256, 1995.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • P. H. S. Torr
    • 1
  • A. Zisserman
    • 2
  1. 1.Microsoft Research LtdCambridgeUK
  2. 2.Department of Engineering ScienceUniversity of OxfordOxfordUK

Personalised recommendations