Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
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)
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)
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)
Cootes, T.F., Cooper, D.H., Taylor, C.J., Graham, J.: A trainable method of parametric shape description, pp. 289–294 (1992)
Cootes, T.F., Taylor, C.J., Cooper, D.H., Gaham, J.: Active shape models - their training and application 61(1), 38–59 (1995)
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)
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)
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)
Hall, P., Marshall, D., Martin, R.: Incremental eigenanalysis for classification. In: Proc. British Machine Vision Conf., vol. I, pp. 286–295 (1998)
Hall, P., Marshall, D., Martin, R.: Merging and splitting eigenspace models. IEEE Trans. on Pattern Analysis and Machine Intelligence 22(9), 1042–1049 (2000)
Hotelling, H.: Analysis of a complex of statistical variables with principal components. Journal of Educational Psychology 24, 417–441 (1933)
Leonardis, A., Bischof, H.: Robust recognition using eigenimages. Computer Vision and Image Understanding 78(1), 99–118 (2000)
Li, Y.: On incremental and robust subspace learning. Pattern Recognition 37(7), 1509–1518 (2004)
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)
Rao, R.: Dynamic appearance-based recognition. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition, pp. 540–546 (1997)
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)
Roweis, S.: EM algorithms for PCA and SPCA. In: Proc. Conf. on Neural Information Processing Systems, pp. 626–632 (1997)
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)
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)
Tipping, M.E., Bishop, C.M.: Probabilistic principal component analysis. Journal of the Royal Statistical Society B 61, 611–622 (1999)
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)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Fussenegger, M., Roth, P.M., Bischof, H., Pinz, A. (2006). On-Line, Incremental Learning of a Robust Active Shape Model. In: Franke, K., Müller, KR., Nickolay, B., Schäfer, R. (eds) Pattern Recognition. DAGM 2006. Lecture Notes in Computer Science, vol 4174. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11861898_13
Download citation
DOI: https://doi.org/10.1007/11861898_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-44412-1
Online ISBN: 978-3-540-44414-5
eBook Packages: Computer ScienceComputer Science (R0)