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Multigrid Convergence and Surface Area Estimation

  • David Coeurjolly
  • Frédéric Flin
  • Olivier Teytaud
  • Laure Tougne
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2616)

Abstract

Surface area of discrete objects is an important feature for model-based image analysis. In this article, we present a theoretical framework in order to prove multigrid convergence of surface area estimators based on discrete normal vector field integration. The paper details an algorithm which is optimal in time and multigrid convergent to estimate the surface area and a very efficient algorithm based on a local but adaptive computation.

Keywords

Normal Vector Gradient Vector Snow Sample Discrete Surface Discrete Curve 
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 2003

Authors and Affiliations

  • David Coeurjolly
    • 1
  • Frédéric Flin
    • 2
  • Olivier Teytaud
    • 3
  • Laure Tougne
    • 1
  1. 1.Laboratoire ERICBron CedexFrance
  2. 2.Centre d’Etudes de la NeigeDomaine UniversitaireSaint Martin d’Hères Cedex
  3. 3.ArtelysIssy-les-Moulineaux Cedex

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