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Cubical Ripser: Software for Computing Persistent Homology of Image and Volume Data
(2020)
Shizuo Kaji, Takeki Sudo, Kazushi Ahara
Abstract
We introduce Cubical Ripser for computing persistent homology of image and volume data. To our best knowledge, Cubical Ripser is currently the fastest and the most memory-eļ¬cient program for computing persistent homology of image and volume data. 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 at [14].