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Dynamical Statistical Shape Priors for Level Set Based Sequence Segmentation

  • Daniel Cremers
  • Gareth Funka-Lea
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3752)

Abstract

In recent years, researchers have proposed to introduce statistical shape knowledge into the level set method in order to cope with insufficient low-level information. While these priors were shown to drastically improve the segmentation of images or image sequences, so far the focus has been on statistical shape priors that are time-invariant. Yet, in the context of tracking deformable objects, it is clear that certain silhouettes may become more or less likely over time. In this paper, we tackle the challenge of learning dynamical statistical models for implicitly represented shapes. We show how these can be integrated into a segmentation process in a Bayesian framework for image sequence segmentation. Experiments demonstrate that such shape priors with memory can drastically improve the segmentation of image sequences.

Keywords

Autoregressive Model Active Contour Statistical Shape Signed Distance Function Deformable Object 
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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Daniel Cremers
    • 1
  • Gareth Funka-Lea
    • 1
  1. 1.Department of Imaging and VisualizationSiemens Corporate ResearchPrinceton

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