Shapes and Diffeomorphisms
By Laurent Younes. 2010
494 citations recorded by the source corpus.
Subfields: Computer Vision and Pattern Recognition
View Paper: https://www.semanticscholar.org/paper/163047737ca73fdb3e5147eaee64329abe04ed1d
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Vision and Pattern Recognition
- Anuj Srivastava
- Facundo M'emoli
- Alain Trouv'e
- Emmanuel Trélat
- Sylvain Arguillère
- Sebastian N. Kurtek
- Eric Klassen
- Arnaud Doucet
Citations in the corpus
- Shape deformation analysis from the optimal control viewpoint (2014, methodology): “The most common model is when γ is a Gaussian function but other families of kernels can be used as well [39, 42].”
- Gromov–Wasserstein Distances and the Metric Approach to Object Matching (2011, methodology): “We should refer the reader to the recent book by Younes [112].”
- Augmented Neural ODEs (2019, background): “This statement is a consequence of the flow of an ODE being a homeomorphism, i.e. a continuous bijection whose inverse is also continuous; see, e.g., (Younes, 2010).”
- Geometry of Diffeomorphism Groups, Complete integrability and Geometric statistics (2013, unknown): “In [28] a one-dimensional version of (3.”
- Statistical Shape Analysis (2014, background): “PalaeoMath: Part 17 - Shape Theory 7 L. Younes (2010).”