On-Line, Incremental Learning of a Robust Active Shape Model

  • Michael Fussenegger
  • Peter M. Roth
  • Horst Bischof
  • Axel Pinz
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4174)


Active Shape Models are commonly used to recognize and locate different aspects of known rigid objects. However, they require an off-line learning stage, such that the extension of an existing model requires a complete new re-training phase. Furthermore, learning is based on principal component analysis and requires perfect training data that is not corrupted by partial occlusions or imperfect segmentation. The contribution of this paper is twofold: First, we present a novel robust Active Shape Model that can handle corrupted shape data. Second, this model can be created on-line through the use of a robust incremental PCA algorithm. Thus, an already partially learned Active Shape Model can be used for segmentation of a new image in a level set framework and the result of this segmentation process can be used for an on-line update of the robust model. Our experimental results demonstrate the robustness and the flexibility of this new model, which is at the same time computationally much more efficient than previous ASMs using batch or iterated batch PCA.


Training Image Segmentation Result Reconstruction Error Incremental Learn Shape Representation 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Black, M.J., Jepson, A.D.: Eigentracking: Robust matching and tracking of articulated objects using a view-based representation. In: Proc. European Conf. on Computer Vision, pp. 329–342 (1996)Google Scholar
  2. 2.
    Brand, M.: Incremental singular value decomposition of uncertain data with missing values. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2350, pp. 707–720. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  3. 3.
    Brox, T., Weickert, J.: Level set based image segmentation with multiple regions. In: Rasmussen, C.E., Bülthoff, H.H., Schölkopf, B., Giese, M.A. (eds.) DAGM 2004. LNCS, vol. 3175, pp. 415–423. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  4. 4.
    Cootes, T.F., Cooper, D.H., Taylor, C.J., Graham, J.: A trainable method of parametric shape description, pp. 289–294 (1992)Google Scholar
  5. 5.
    Cootes, T.F., Taylor, C.J., Cooper, D.H., Gaham, J.: Active shape models - their training and application  61(1), 38–59 (1995)Google Scholar
  6. 6.
    Cremers, D., Sochen, N., Schnoerr, C.: Towards recognition-based variational segmentation using shape priors and dynamic labeling. In: Proc. of Scale-Space, pp. 388–400 (2003)Google Scholar
  7. 7.
    de la Torre, F., Black, M.J.: Robust principal component analysis for computer vision. In: Proc. IEEE Intern. Conf. on Computer Vision, vol. I, pp. 362–369 (2001)Google Scholar
  8. 8.
    Fussenegger, M., Deriche, R., Pinz, A.: A multiphase level set based segmentation framework with pose invariant shape priors. In: Narayanan, P.J., Nayar, S.K., Shum, H.-Y. (eds.) ACCV 2006. LNCS, vol. 3852, pp. 395–404. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  9. 9.
    Hall, P., Marshall, D., Martin, R.: Incremental eigenanalysis for classification. In: Proc. British Machine Vision Conf., vol. I, pp. 286–295 (1998)Google Scholar
  10. 10.
    Hall, P., Marshall, D., Martin, R.: Merging and splitting eigenspace models. IEEE Trans. on Pattern Analysis and Machine Intelligence 22(9), 1042–1049 (2000)CrossRefGoogle Scholar
  11. 11.
    Hotelling, H.: Analysis of a complex of statistical variables with principal components. Journal of Educational Psychology 24, 417–441 (1933)CrossRefGoogle Scholar
  12. 12.
    Leonardis, A., Bischof, H.: Robust recognition using eigenimages. Computer Vision and Image Understanding 78(1), 99–118 (2000)CrossRefGoogle Scholar
  13. 13.
    Li, Y.: On incremental and robust subspace learning. Pattern Recognition 37(7), 1509–1518 (2004)MATHCrossRefGoogle Scholar
  14. 14.
    Osher, S.J., Sethian, J.A.: Fronts propagation with curvature depend speed: Algorithms based on Hamilton-Jacobi formulations. Journal of Comp. Phys. 79, 12–49 (1988)MATHCrossRefMathSciNetGoogle Scholar
  15. 15.
    Rao, R.: Dynamic appearance-based recognition. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition, pp. 540–546 (1997)Google Scholar
  16. 16.
    Rousson, M., Paragios, N.: Shape Priors for Level Set Representations. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2351, pp. 78–92. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  17. 17.
    Roweis, S.: EM algorithms for PCA and SPCA. In: Proc. Conf. on Neural Information Processing Systems, pp. 626–632 (1997)Google Scholar
  18. 18.
    Skočaj, D., Bischof, H., Leonardis, A.: A robust PCA algorithm for building representations from panoramic images. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2353, pp. 761–775. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  19. 19.
    Skočaj, D., Leonardis, A.: Weighted and robust incremental method for subspace learning. In: Proc. IEEE Intern. Conf. on Computer Vision, vol. II, pp. 1494–1501 (2003)Google Scholar
  20. 20.
    Tipping, M.E., Bishop, C.M.: Probabilistic principal component analysis. Journal of the Royal Statistical Society B 61, 611–622 (1999)MATHCrossRefMathSciNetGoogle Scholar
  21. 21.
    Xu, L., Yuille, A.L.: Robust principal component analysis by self-organizing rules based on statistical physics approach. IEEE Trans. on Neural Networks 6(1), 131–143 (1995)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Michael Fussenegger
    • 1
  • Peter M. Roth
    • 2
  • Horst Bischof
    • 2
  • Axel Pinz
    • 1
  1. 1.Institute of Electrical Measurement and Measurement Signal ProcessingGraz University of TechnologyGrazAustria
  2. 2.Institute for Computer Graphics and VisionGraz University of TechnologyGrazAustria

Personalised recommendations