Nonlinear Shape Statistics in Mumford—Shah Based Segmentation

  • Daniel Cremers
  • Timo Kohlberger
  • Christoph Schnörr
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2351)


We present a variational integration of nonlinear shape statistics into a Mumford—Shah based segmentation process. The nonlinear statistics are derived from a set of training silhouettes by a novel method of density estimation which can be considered as an extension of kernel PCA to a stochastic framework.

The idea is to assume that the training data forms a Gaussian distribution after a nonlinear mapping to a potentially higher-dimensional feature space. Due to the strong nonlinearity, the corresponding density estimate in the original space is highly non–Gaussian. It can capture essentially arbitrary data distributions (e.g. multiple clusters, ring- or banana–shaped manifolds).

Applications of the nonlinear shape statistics in segmentation and tracking of 2D and 3D objects demonstrate that the segmentation process can incorporate knowledge on a large variety of complex real—world shapes. It makes the segmentation process robust against misleading information due to noise, clutter and occlusion.


Segmentation shape learning nonlinear statistics density estimation Mercer kernels variational methods probabilistic kernel PCA 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Daniel Cremers
    • 1
  • Timo Kohlberger
    • 1
  • Christoph Schnörr
    • 1
  1. 1.Computer Vision, Graphics and Pattern Recognition Group Department of Mathematics and Computer ScienceUniversity of MannheimMannheimGermany

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