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Communication Dans Un Congrès Année : 2014

Parameter-free and Multigrid Convergent Digital Curvature Estimators

Résumé

In many geometry processing applications, the estimation of differential geometric quantities such as curvature or normal vector field is an essential step. Focusing on multigrid convergent estimators, most of them require a user specified parameter to define the scale at which the analysis is performed (size of a convolution kernel, size of local patches for polynomial fitting, etc). In a previous work, we have proposed a new class of estimators on digital shape boundaries based on Integral Invariants. In this paper, we propose new variants of these estimators which are parameter-free and ensure multigrid convergence in 2D. As far as we know, these are the first parameter-free multigrid convergent curvature estimators.
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Dates et versions

hal-01118476 , version 1 (19-02-2015)

Identifiants

Citer

Jérémy Levallois, David Coeurjolly, Jacques-Olivier Lachaud. Parameter-free and Multigrid Convergent Digital Curvature Estimators. 18th International Conference on Discrete Geometry for Computer Imagery (DGCI 2014), Sep 2014, Siena, Italy. pp.162-175, ⟨10.1007/978-3-319-09955-2_14⟩. ⟨hal-01118476⟩
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