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Dynamical Statistical Shape Priors for Level Set Based Sequence Segmentation

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Variational, Geometric, and Level Set Methods in Computer Vision (VLSM 2005)

Part of the book series: Lecture Notes in Computer Science ((LNIP,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.

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Authors and Affiliations

  1. Department of Imaging and Visualization, Siemens Corporate Research, Princeton, NJ

    Daniel Cremers & Gareth Funka-Lea

Authors
  1. Daniel Cremers
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  2. Gareth Funka-Lea
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Editor information

Editors and Affiliations

  1. MAS - Ecole Centrale Paris, Grande Voie des Vignes, 92295, Chatenay-Malabry, France

    Nikos Paragios

  2. I.N.R.I.A, 2004 route des lucioles,, 06902, Sophia-Antipolis, France

    Olivier Faugeras

  3. Department of Mathematics, UCLA,  

    Tony Chan

  4. Image and Pattern Analysis Group, Heidelberg Collaboratory for Image Processing, University of Heidelberg, Germany

    Christoph Schnörr

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© 2005 Springer-Verlag Berlin Heidelberg

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Cremers, D., Funka-Lea, G. (2005). Dynamical Statistical Shape Priors for Level Set Based Sequence Segmentation. In: Paragios, N., Faugeras, O., Chan, T., Schnörr, C. (eds) Variational, Geometric, and Level Set Methods in Computer Vision. VLSM 2005. Lecture Notes in Computer Science, vol 3752. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11567646_18

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  • DOI: https://doi.org/10.1007/11567646_18

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-29348-4

  • Online ISBN: 978-3-540-32109-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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