Cubical Ripser: Software for computing persistent homology of image and volume data
This software addresses the need for efficient topological data analysis in fields like computer vision and medical imaging, though it appears incremental as an optimization of existing methods.
The authors tackled the problem of computing persistent homology for image and volume data by introducing Cubical Ripser, which they claim is the fastest and most memory-efficient software for this task, as demonstrated through an example combining it with convolutional neural networks for image analysis.
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.