Robust and Convergent Curvature and Normal Estimators with Digital Integral Invariants

  • Jacques-Olivier Lachaud
  • David Coeurjolly
  • Jérémy Levallois
Chapter
Part of the Lecture Notes in Mathematics book series (LNM, volume 2184)

Abstract

We present, in details, a generic tool to estimate differential geometric quantities on digital shapes, which are subsets of \(\mathbb{Z}^{d}\). This tool, called digital integral invariant, simply places a ball at the point of interest, and then examines the intersection of this ball with input data to infer local geometric information. Just counting the number of input points within the intersection provides curvature estimation in 2D and mean curvature estimation in 3D. The covariance matrix of the same point set allows to recover principal curvatures, principal directions and normal direction estimates in 3D. We show the multigrid convergence of all these estimators, which means that their estimations tend toward the exact geometric quantities on—smooth enough—Euclidean shapes digitized with finer and finer gridsteps. During the course of the chapter, we establish several multigrid convergence results of digital volume and moments estimators in arbitrary dimensions. Afterwards, we show how to set automatically the radius parameter while keeping multigrid convergence properties. Our estimators are then demonstrated to be accurate in practice, with extensive comparisons with state-of-the-art methods. To conclude the exposition, we discuss their robustness to perturbations and noise in input data and we show how such estimators can detect features using scale-space arguments.

Keywords

Curvature estimation Digital geometry Digital moments Integral invariants Multigrid convergence Normal estimation 

Notes

Acknowledgements

This work has been mainly funded by DigitalSnow ANR-11-BS02-009, KIDICO ANR-2010-BLAN-0205 and PRIMES Labex ANR-11-LABX-0063/ANR-11-IDEX-0007 research grants.

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Copyright information

© The Author(s) 2017

Authors and Affiliations

  • Jacques-Olivier Lachaud
    • 1
    • 2
  • David Coeurjolly
    • 3
  • Jérémy Levallois
    • 1
    • 4
  1. 1.CNRS, LAMA, UMR 5127, Université Savoie Mont BlancLe Bourget-du-lacFrance
  2. 2.CNRS, LJK, UMR 5224, Université Grenoble-AlpesSaint-Martin-d’HéresFrance
  3. 3.CNRS, Université de Lyon, LIRIS, UMR 5205, Université de LyonLyonFrance
  4. 4.CNRS, INSA-Lyon, LIRIS, UMR 5205, Université de LyonLyonFrance

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