An algorithm for Mean Curvature Motion
By Antonin Chambolle. 2004
157 citations recorded by the source corpus.
Subfields: Applied Mathematics, Computational Mechanics
View Paper: https://www.semanticscholar.org/paper/a8f932ec32734c9b9fddf1843a62ee77063dd549
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Applied Mathematics
- Matteo Novaga
- V. Caselles
- Antonin Chambolle
- Giovanni Bellettini
- Thomas Pock
- Felix Otto
- Tim Laux
- Marcello Ponsiglione
In Computational Mechanics
- Thomas Pock
- Antonin Chambolle
- Jean-François Aujol
- Tim Laux
- Felix Otto
- Gilles Aubert
- V. Caselles
- Massimiliano Morini
Citations in the corpus
- A Variational Approach to Removing Multiplicative Noise (2008, background): “Jn\ uiVu2J 77- JQ\ u2j Adding these two inequalities and using the fact that J(u\ A u2) + J(u\ V u2) < J(ui) + J{u2) [12, 23], we get (4.”
- Convergence of the thresholding scheme for multi-phase mean-curvature flow (2016, methodology): “Although the time discretization scheme in [2, 24] seems rather academic from a computational point of view, it has been adapted for numerical simulations by Chambolle in [8].”
- Crystalline Mean Curvature Flow of Convex Sets (2006, methodology): “The advantage of the method of [24] is that it provides uniqueness of each minimizer at each discrete step, which in general is not the case in the flat φ-curvature flow procedure; this is essentially the reason for which the corresponding flow is, in general, also a flat φ-curvature flow.”
- Nonlocal Curvature Flows (2014, methodology): “We extend the approach of [12] (see also [14]) to our general framework.”
- The anisotropic total variation flow (2005, unknown): “Let us also notice that our problem is closely related to motion under Anisotropic Mean Curvature Flow since, as shown by A. Chambolle ([ 28 ]), in case f does not depend on the space variable, if we take the f 0 -distance to a given set E as”
- An introduction to continuous optimization for imaging (2016, methodology): “In fact a minimizer of (7.23) is obtained by minimizing (7.1), with u = w being the input image, and then thresholding the solution u at the 0 level (Chambolle 2004a, 2005).”
- An Algorithm for Total Variation Minimization and Applications (2004, unknown): “This result holds in fact in any dimension. The proof will be given in [ 4 ].”