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Parameter-free and multigrid convergent digital curvature estimators. (English) Zbl 1417.68241

Barcucci, Elena (ed.) et al., Discrete geometry for computer imagery. 18th IAPR international conference, DGCI 2014, Siena, Italy, September 10–12, 2014. Proceedings. Berlin: Springer. Lect. Notes Comput. Sci. 8668, 162-175 (2014).
Summary: 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.
For the entire collection see [Zbl 1295.68017].

MSC:

68U05 Computer graphics; computational geometry (digital and algorithmic aspects)
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