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Spatio-temporal Prior Shape Constraint for Level Set Segmentation

  • Timothée Bailloeul
  • Véronique Prinet
  • Bruno Serra
  • Philippe Marthon
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3757)

Abstract

This paper exposes a novel formulation of prior shape constraint incorporation for the level set segmentation of objects from corrupted images. Applicable to variational frameworks, the proposed scheme consists in weighting the prior shape constraint by a function of time and space to overcome local minima issues of the energy functional. Pose parameters which make the prior shape constraint invariant from global transformations are estimated by the downhill simplex algorithm, which is more tractable and robust than the traditional gradient descent. The proposed scheme is simple, easy to implement and can be generalized to any variational approach incorporating a single prior shape. Results illustrated with different kinds of images demonstrate the efficiency of the method.

Keywords

Gradient Descent Active Contour Variational Framework Global Transformation Shape Constraint 
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.

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References

  1. 1.
    Staib, L.H., Duncan, J.S.: Boundary finding with parametrically deformable models. IEEE Transactions on Pattern Analysis and Machine Intelligence 14, 1061–1075 (1992)CrossRefGoogle Scholar
  2. 2.
    Cootes, T., Cooper, D., Taylor, C., Graham, J.: Active shape models - their training and application. Computer Vision and Image Understanding 61, 38–59 (1995)CrossRefGoogle Scholar
  3. 3.
    Leventon, M., Grimson, E., Faugeras., O.: Statistical shape influence in geodesic active contours. In: Comp. Vision and Patt. Recon, CVPR (2000)Google Scholar
  4. 4.
    Chen, Y., Tagare, H., Thiruvenkadam, S., Huang, F., Wilson, D., Gopinath, K., Briggs, R., Geiser, E.: Using prior shapes in geometric active contours in a variational framework. International Journal of Computer Vision 50(3), 315–328 (2002)zbMATHCrossRefGoogle Scholar
  5. 5.
    Cremers, D., Tischhauser, F., Weickert, J., Schnorr, C.: Diffusion snakes: introducing statistical shape knowledge into the mumford-shah functional. In: ICCV (2002)Google Scholar
  6. 6.
    Paragios, N., Rousson, M.: Shape priors for level set representations. In: European Conference in Computer Vision, vol. 2, pp. 78–92 (2002)Google Scholar
  7. 7.
    Foulonneau, A., Charbonnier, P., Heitz, F.: Geometric shape priors for region-based active contours. In: IEEE Int. Conf. Image Processing, ICIP 2003 (2003)Google Scholar
  8. 8.
    Chan, T., Zhu, W.: Level set based shape prior segmentation. Technical report, UCLA (2003)Google Scholar
  9. 9.
    Cremers, D., Sochen, N., Schnörr, C.: Towards recognition-based variational segmentation using shape priors and dynamic labeling. In: Intl. Conf. on Scale-Space Theories in Computer Vision (2003)Google Scholar
  10. 10.
    Cremers, D., Soatto, S.: A pseudo-distance for shape priors in level set segmentation. In: Proc. ICCV 2003 (2003)Google Scholar
  11. 11.
    Cremers, D., Osher, S., Soatto, S.: Kernel density estimation and intrinsic alignment for knowledge-driven segmentation: teaching level sets to walk. In: Rasmussen, C.E., Bülthoff, H.H., Schölkopf, B., Giese, M.A. (eds.) DAGM 2004. LNCS, vol. 3175, pp. 36–44. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  12. 12.
    Tsai, A., Yezzi, A., Wells, W., Tempany, C., Tucker, D., Fan, A., Grimson, E., Willsky, A.: Model-based curve evolution technique for image segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 463–468 (2001)Google Scholar
  13. 13.
    Kass, M., Witkin, A., Terzopoulos, D.: Snakes: active contour models. In: 1st International Conference on Computer Vision, pp. 259–268 (1987)Google Scholar
  14. 14.
    Osher, S., Sethian, J.: Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations. Journal of Computational Physics 79, 12–49 (1988)zbMATHMathSciNetCrossRefGoogle Scholar
  15. 15.
    Sethian, J.: Level set methods and fast marching methods: evolving interfaces in computational geometry, fluid mechanics, computer vision and materials science. Cambridge University Press, Cambridge (1999)zbMATHGoogle Scholar
  16. 16.
    Bailloeul, T., Prinet, V., Serra, B., Marthon, P., Chen, P., Zhang, H.: Digital building map refinement from knowledge-driven active contours and very high resolution optical imagery. In: Proc. ISPRS Hannover Workshop (2005)Google Scholar
  17. 17.
    Chen, Y., Thiruvenkadam, S., Tagare, H., Huang, F., Wilson, D., Geiser, E.A.: On the incorporation of shape priors into geometric active contours. In: IEEE 1st Worshop on Vatiational Framework and Level Sets methods (2001)Google Scholar
  18. 18.
    Nelder, J.A., Mead, R.: A simplex method for function minimization. Computer Journal 7(4), 308–313 (1965)zbMATHGoogle Scholar
  19. 19.
    Paragios, N., Deriche, R.: Geodesic active regions: A new paradigm to deal with frame partition problems in computer vision. Journal of Visual Communication and Image Representation 13, 249–268 (2002)CrossRefGoogle Scholar
  20. 20.
    Paragios, N., Rousson, M., Ramesh, V.: Matching distance functions: A shape-to-area variational approach for global-to-local registration. In: European Conference in Computer Vision (2002)Google Scholar
  21. 21.
    Yui, S., Hara, K., Zha, H., Hasegawa, T.: A fast narrow band method and its application in topology-adaptative 3-d modeling. In: 16th Intl Conf. on Pattern Recognition (ICPR 2002), vol. 4 (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Timothée Bailloeul
    • 1
    • 2
    • 3
  • Véronique Prinet
    • 1
  • Bruno Serra
    • 2
  • Philippe Marthon
    • 3
  1. 1.LIAMA, Institute of AutomationChinese Academy of SciencesBeijingChina
  2. 2.Alcatel SpaceCannes La BoccaFrance
  3. 3.LIMA (IRIT)ToulouseFrance

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