The Gudhi Library: Simplicial Complexes and Persistent Homology
By Clément Maria, Jean-Daniel Boissonnat, Marc Glisse, Mariette Yvinec. International Congress on Mathematical Software, 2014
296 citations recorded by the source corpus.
Subfields: Computational Theory and Mathematics, Mathematical Physics, Radiology, Nuclear Medicine and Imaging
View Paper: https://www.semanticscholar.org/paper/5dbdfc3a7e9858494470209165baeaa06b84a3e0
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In Computational Theory and Mathematics
- Heather A. Harrington
- Frédéric Chazal
- Bertrand Michel
- Nina Otter
- Ulrike Tillmann
- M. A. Porter
- Peter Grindrod
In Mathematical Physics
- Frédéric Chazal
- Heather A. Harrington
- Nina Otter
- Ulrike Tillmann
- M. A. Porter
- Peter Grindrod
- Bertrand Michel
In Radiology, Nuclear Medicine and Imaging
Citations in the corpus
- A roadmap for the computation of persistent homology (2015, methodology): “A version of this approach was used in [23], in which the local structure of natural images was studied by selecting 3 × 3 patches of pixels of the images.”
- An Introduction to Topological Data Analysis: Fundamental and Practical Aspects for Data Scientists (2017, methodology): “6 tda for data sciences with the GUDHI library In this section we illustrate TDA methods with the Python library Gudhi6 (Maria et al., 2014) together with popular libraries as numpy (Walt et al., 2011), scikit-learn (Pedregosa et al., 2011), pandas (McKinney et al., 2010).”