Cubical Ripser: Software for computing persistent homology of image and volume data

05/23/2020

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by   Shizuo Kaji, et al.

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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-efficient 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 online.