Persistent Homology Computation Using Combinatorial Map Simplification

Guillaume Damiand 1 Rocio Gonzalez-Diaz 2
1 M2DisCo - Geometry Processing and Constrained Optimization
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
Abstract : We propose an algorithm for persistence homology computation of orientable 2-dimensional (2D) manifolds with or without boundary (meshes) represented by 2D combinatorial maps. Having as an input a real function h on the vertices of the mesh, we first compute persistent homology of filtrations obtained by adding cells incident to each vertex of the mesh, The cells to add are controlled by both the function h and a parameter δ. The parameter δ is used to control the number of cells added to each level of the filtration. Bigger δ produces less levels in the filtration and consequently more cells in each level. We then simplify each level (cluster) by merging faces of the same cluster. Our experiments demonstrate that our method allows fast computation of persistent ho-mology of big meshes and it is persistent-homology aware in the sense that persistent homology does not change in the simplification process when fixing δ.
Complete list of metadatas

Cited literature [18 references]  Display  Hide  Download

https://hal.archives-ouvertes.fr/hal-02002963
Contributor : Guillaume Damiand <>
Submitted on : Friday, February 1, 2019 - 10:00:32 AM
Last modification on : Wednesday, February 13, 2019 - 2:38:16 PM
Long-term archiving on : Thursday, May 2, 2019 - 2:13:28 PM

File

persistence-homology.pdf
Files produced by the author(s)

Identifiers

Citation

Guillaume Damiand, Rocio Gonzalez-Diaz. Persistent Homology Computation Using Combinatorial Map Simplification. International Workshop on Computational Topology in Image Context, Jan 2019, Malaga, Spain. pp.26-39, ⟨10.1007/978-3-030-10828-1_3⟩. ⟨hal-02002963⟩

Share

Metrics

Record views

30

Files downloads

71