Maximal Digital Straight Segments and Convergence of Discrete Geometric Estimators

  • François de Vieilleville
  • Jacques-Olivier Lachaud
  • Fabien Feschet
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3540)

Abstract

Discrete geometric estimators approach geometric quantities on digitized shapes without any knowledge of the continuous shape. A classical yet difficult problem is to show that an estimator asymptotically converges toward the true geometric quantity as the resolution increases. We study here the convergence of local estimators based on Digital Straight Segment (DSS) recognition. It is closely linked to the asymptotic growth of maximal DSS, for which we show bounds both about their number and sizes. These results not only give better insights about digitized curves but indicate that curvature estimators based on local DSS recognition are not likely to converge. We indeed invalidate an hypothesis which was essential in the only known convergence theorem of a discrete curvature estimator. The proof involves results from arithmetic properties of digital lines, digital convexity, combinatorics, continued fractions and random polytopes.

Keywords

Asymptotic Convergence Curvature Estimator Maximal Segment Left Factor Supporting Edge 
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

  • François de Vieilleville
    • 1
  • Jacques-Olivier Lachaud
    • 1
  • Fabien Feschet
    • 2
  1. 1.LaBRIUniv. Bordeaux 1TalenceFrance
  2. 2.LLAIC1IUT Clermont-FerrandAubière CedexFrance

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