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One-Shot Integral Invariant Shape Priors for Variational Segmentation

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Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR 2005)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 3757))

Abstract

We match shapes, even under severe deformations, via a smooth re-parametrization of their integral invariant signatures. These robust signatures and correspondences are the foundation of a shape energy functional for variational image segmentation. Integral invariant shape templates do not require registration and allow for significant deformations of the contour, such as the articulation of the object’s parts. This enables generalization to multiple instances of a shape from a single template, instead of requiring several templates for searching or training. This paper motivates and presents the energy functional, derives the gradient descent direction to optimize the functional, and demonstrates the method, coupled with a data term, on real image data where the object’s parts are articulated.

UCRL-CONF-212393. This work was performed under the auspices of the U. S. Department of Energy by Univesity of California Lawrence Livermore National Laboratory under contract No. W-7405-Eng-48. Supported by NSF IIS-0208197, AFOSR F49620-03-1-0095, ONR N00014-03-1-0850.

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Author information

Authors and Affiliations

  1. Lawrence Livermore National Laboratory,  

    Siddharth Manay

  2. Siemens Corporate Research,  

    Daniel Cremers

  3. Georgia Institute of Technology,  

    Anthony Yezzi

  4. University of California at Los Angeles,  

    Stefano Soatto

Authors
  1. Siddharth Manay
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  2. Daniel Cremers
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  3. Anthony Yezzi
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  4. Stefano Soatto
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Editor information

Editors and Affiliations

  1. Department of CISE, University of Florida, Gainesville, FL, USA

    Anand Rangarajan

  2. University of Florida, 32611, Gainesville, FL, USA

    Baba Vemuri

  3. Department of Statistics, University of California, Los Angeles

    Alan L. Yuille

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© 2005 Springer-Verlag Berlin Heidelberg

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Manay, S., Cremers, D., Yezzi, A., Soatto, S. (2005). One-Shot Integral Invariant Shape Priors for Variational Segmentation. In: Rangarajan, A., Vemuri, B., Yuille, A.L. (eds) Energy Minimization Methods in Computer Vision and Pattern Recognition. EMMCVPR 2005. Lecture Notes in Computer Science, vol 3757. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11585978_27

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  • DOI: https://doi.org/10.1007/11585978_27

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-30287-2

  • Online ISBN: 978-3-540-32098-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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