Advertisement

A statistical process for surface tracking

  • Nigel G. Sharp
  • Edwin R. Hancock
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1176)

Abstract

This paper describes a novel approach to surface tracking in volumetric image stacks. It draws on a statistical model of the uncertainties inherent in the characterisation of feature contours to compute an evidential field for putative inter-frame displacements. This field is computed using Gaussian density kernels which are parameterised in terms of the variance-covariance matrices for contour displacement. The underlying variance model accommodates the effects of raw image noise on the estimated surface normals. The evidential field effectively couples contour displacements to the intensity features on successive frames through a statistical process of contour tracking. Hard contours are extracted using a dictionary-based relaxation process. The method is evaluated on both MRI data and simulated data.

Keywords

Volumetric Image Feature Contour Surface Tracking Contour Displacement Contour Tracking 
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.

References

  1. 1.
    Blake A., Curwen, R. and Zisserman A., “Affine Invariant Contour Tracking with Automatic control of Spatiotemporal Scale”, Proceedings of the Fourth International Conference on Computer Vision, pp. 66–75, 1993.Google Scholar
  2. 2.
    Bruckstein A.M. and Shaked D., “Projective Invariant Smoothing and Evolution of Planar Curves”, Aspects of Visual Form Processing, Edited by C. Arcelli, L. Cordelia and G. Sanniti di Baja, pp. 109–119, 1994.Google Scholar
  3. 3.
    Gueziec A., “Large Deformable Splines, Crest Lines and Matching”, Proceedings of the Fourth International Conference on Computer Vision, pp 650–657, 1993.Google Scholar
  4. 4.
    Hancock E.R. and Kittler J., “Combining Evidence in probabilistic relaxation”, International Journal of Pattern Recognition and Artificial Intelligence, 3, pp. 29–52, 1989.Google Scholar
  5. 5.
    Hancock E.R. and Kittler J., “Edge labelling using dictionary based relaxation”, IEEE PAMI, PAMI 12, pp. 165–181, 1990.Google Scholar
  6. 6.
    Hancock E.R. and Kittler J., “Adaptive estimation of hysteresis thresholds”, Proceedings IEEE Computer Vision and Pattern Recognition Conference, pp. 196–201, 1991.Google Scholar
  7. 7.
    Hancock E.R., “Resolving Edge-Line Ambiguities using Probabilistic Relaxation”, Proceedings IEEE CVPR Conference, pp. 300–306, 1993.Google Scholar
  8. 8.
    Ivins J. and Porrill J., “Active Region Models for Segmenting Texture and Colour”, Image and Vision Computing, 13, pp. 431–438, 1995.Google Scholar
  9. 9.
    Kass M., Witken A. and Terzopoulos D., “Constraints on deformable models: Recovering 3D Shape from non-rigid motion”, Artificial Intelligence, 36, pp 91–123, 1988.Google Scholar
  10. 10.
    Kimia B.B., Tannenbaum A. and Zucker S.W., “On the Shape Triangle”, Aspects of Visual Form Processing, Edited by C. Arcelli, L. Cordelia and G. Sanniti di Baja, pp. 307–323, 1994.Google Scholar
  11. 11.
    Monga O., Deriche R., and Malandain G., “Recursive Filtering and edge closing; two primary tools for 3D edge detection”, Image and Vision Computing, 9, pp., 1991.Google Scholar
  12. 12.
    Osher S.J. and Sethian J.A., “Fronts Propagating with Curvature Dependent Speed: Algorithms Based on Hamilton-Jacobi Formulations”, Journal of Computational Physics, 79, pp. 12–49, 1988.Google Scholar
  13. 13.
    Sander P.T. and Zucker S.W., “Inferring surface trace and differential structure from 3D images”, IEEE PAMI, PAMI 12, pp. 833–854, 1990.Google Scholar
  14. 14.
    Sharp N.G. and Hancock E.R., “Feature Tracking by Multi-frame Relaxation”, Image and Vision Computing, 13, pp. 637–644, 1995.Google Scholar
  15. 15.
    Zucker S.W, David C., Dobbins A., and Iverson L., “The organisation of curve detection: tangent fields and fine-spline coverings”, Proc. Second Int. Conf. Computer Vision, pp. 577–586, 1988.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1996

Authors and Affiliations

  • Nigel G. Sharp
    • 1
  • Edwin R. Hancock
    • 1
  1. 1.Department of Computer ScienceUniversity of YorkUK

Personalised recommendations