Cubical Ripser: Software for computing persistent homology of image and volume data
About
We introduce Cubical Ripser for computing persistent homology of image and volume data (more precisely, weighted cubical complexes). To our best knowledge, Cubical Ripser is currently the fastest and the most memory-efficient program for computing persistent homology of weighted cubical complexes. We demonstrate our software with an example of image analysis in which persistent homology and convolutional neural networks are successfully combined. Our open-source implementation is available online.
Shizuo Kaji, Takeki Sudo, Kazushi Ahara• 2020
Related benchmarks
| Task | Dataset | Result | Rank | |
|---|---|---|---|---|
| V-filtration computation | Synthetic 2D medium (316 x 316) | Execution Time (s)0.04 | 5 | |
| V-filtration computation | Lena 256 x 256 | Execution Time (s)0.02 | 5 | |
| V-filtration computation | Synthetic 2D large 1024 x 1024 | Execution Time (s)0.34 | 5 | |
| V-filtration computation | DIV2K 1024 x 1024 | Execution Time (s)0.27 | 5 | |
| V-filtration computation | Synthetic 3D medium (46^3) | Execution Time (s)0.31 | 3 | |
| V-filtration computation | Fuel 64^3 | Execution Time (s)0.28 | 3 | |
| V-filtration computation | Synthetic 3D large (128^3) | Time (s)8.29 | 3 | |
| V-filtration computation | Bonsai 128^3 | Time (s)2.94 | 3 | |
| V-filtration computation | Aneurism 128^3 | Time (s)2.62 | 3 |
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