Papers › Cubical Ripser: Software for computing persistent homology of image and volume data
Cubical Ripser: Software for computing persistent homology of image and volume data
Shizuo Kaji, Takeki Sudo, Kazushi Ahara
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.
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