Learning 3D AAM Fitting with Kernel Methods

  • Marina A. Cidota
  • Dragos Datcu
  • Leon J. M. Rothkrantz
Conference paper

DOI: 10.1007/978-3-642-29347-4_56

Part of the Lecture Notes in Computer Science book series (LNCS, volume 7267)
Cite this paper as:
Cidota M.A., Datcu D., Rothkrantz L.J.M. (2012) Learning 3D AAM Fitting with Kernel Methods. In: Rutkowski L., Korytkowski M., Scherer R., Tadeusiewicz R., Zadeh L.A., Zurada J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2012. Lecture Notes in Computer Science, vol 7267. Springer, Berlin, Heidelberg

Abstract

The active appearance model (AAM) has proven to be a powerful tool for modeling deformable visual objects. AAMs are nonlinear parametric models in terms of the relation between the pixel intensities and the parameters of the model. In this paper, we propose a fitting procedure for a 3D AAM based on kernel methods for regression. The use of kernel functions provides a powerful way of detecting nonlinear relations using linear algorithms in an appropriate feature space. For analysis, we have chosen the relevance vector machines (RVM) and the kernel ridge method. The statistics computed on data generated with our 3D AAM implementation show that the kernel methods give better results compared to the linear regression models. Although they are less computational efficient, due to their higher accuracy the kernel methods have the advantage of reducing the searching space for the 3D AAM fitting algorithm.

Keywords

3D-Active Appearance Models nonlinear optimization kernel methods 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Marina A. Cidota
    • 1
  • Dragos Datcu
    • 2
  • Leon J. M. Rothkrantz
    • 2
  1. 1.Faculty of Mathematics and Computer ScienceUniversity of BucharestBucharestRomania
  2. 2.Netherlands Defense AcademyDen HelderThe Netherlands

Personalised recommendations