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Enforcing local context into shape statistics

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Abstract

The paper presents a variational framework to compute first and second order statistics of an ensemble of shapes undergoing deformations. Geometrically “meaningful” correspondence between shapes is established via a kernel descriptor that characterizes local shape properties. Such a descriptor allows retaining geometric features such as high-curvature structures in the average shape, unlike conventional methods where the average shape is usually smoothed out by generic regularization terms. The obtained shape statistics are integrated into segmentation as a prior knowledge. The effectiveness of the method is demonstrated through experimental results with synthetic and real images.

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Authors and Affiliations

  1. School of Computer Science and Engineering, Chung-Ang University, Seoul, Korea

    Byung-Woo Hong

  2. Computer Science Department, University of California, Los Angeles, CA, USA

    Stefano Soatto

  3. Department of Mathematics, University of California, Los Angeles, CA, USA

    Luminita A. Vese

Authors
  1. Byung-Woo Hong
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  2. Stefano Soatto
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  3. Luminita A. Vese
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Corresponding author

Correspondence to Byung-Woo Hong.

Additional information

Communicated by Lixin Shen and Yuesheng Xu.

For any questions regarding this manuscript, please contact the corresponding author Byung-Woo Hong.

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Hong, BW., Soatto, S. & Vese, L.A. Enforcing local context into shape statistics. Adv Comput Math 31, 185–213 (2009). https://doi.org/10.1007/s10444-008-9104-5

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  • DOI: https://doi.org/10.1007/s10444-008-9104-5

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