Abstract
In this paper, we deal with the problem of partially observed objects. These objects are defined by a set of points and their shape variations are represented by a statistical model. We present two models in this paper: a linear model based on PCA and a non-linear model based on KPCA. The present work attempts to localize of non visible parts of an object, from the visible part and from the model, using the variability represented by the models. Both are applied to synthesis data and to cephalometric data with good results.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Cootes, T.F., Edwards, G.J., Taylor, C.J.: Active Appearance Models. IEEE PAMI 23(6), 681–685 (2001)
Hutton, T.J., Cunningham, S., Hammond, P.: An Evaluation of Active Shape Models for the Automatic Identification of Cephalometric Landmarks. European Journal of Orthodontics 22(5), 499–508 (2000)
Mika, S., Schölkopf, B., Smola, A.J., Müller, K.-R., Scholz, M., Rätsch, G.: Kernel PCA and de-noising in feature spaces. In: Kearns, M.S., Solla, S.A., Cohn, D.A. (eds.) Advances in Neural Information Processing Systems, vol. 11, pp. 536–542. MIT Press, Cambridge (1999)
Schölkopf, B., Smola, A., Müller, K.: Non linear component Analysis as a Kernel Eignevalue Problem. Non linear component Analysis as a Kernel Eignevalue Problem. Neural Computation 10(5), 1299–1319 (1998)
Kwok, J.T., Tsang, I.W.: The Pre-Image Problem in Kernel Methods. In: Proceedings of ICML 2003, pp. 408-415, (2003)
Bakir, G.H., Weston, J., Schölkopf, B.: Learning to Find Pre-Images. In: Thrun, S., Saul, L., Schölkopf, B. (eds.) Advances in Neural Information Processing Systems, vol. 16, pp. 449–456. MIT Press, Cambridge (2004)
Sclaroff, S., Pentland, A.P.: Modal Matching for Correspondence and Recognition. IEEE Transactions on Pattern Recognition and Machine Intelligence 17(6), 545–561 (1995)
Romaniuk, B., Desvignes, M., Revenu, M., Deshayes, M.J.: Linear and Non-Linear Model for Statistical Localization of Landmarks. In: ICPR, vol. 4, pp. 393–396 (2002)
Romaniuk, B., Desvignes, M.: Contour Tracking by Minimal Cost Path Approach. Application to Cephalometry. In: International Conference on Image Processing ICIP, Singapore (October 2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Berar, M., Desvignes, M., Bailly, G., Payan, Y., Romaniuk, B. (2005). Missing Data Estimation Using Polynomial Kernels. In: Singh, S., Singh, M., Apte, C., Perner, P. (eds) Pattern Recognition and Data Mining. ICAPR 2005. Lecture Notes in Computer Science, vol 3686. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11551188_42
Download citation
DOI: https://doi.org/10.1007/11551188_42
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28757-5
Online ISBN: 978-3-540-28758-2
eBook Packages: Computer ScienceComputer Science (R0)Springer Nature Proceedings Computer Science
Keywords
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.