  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.
