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

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arxiv 2005.12692 v2 pith:KJDL7APR submitted 2020-05-23 cs.CV math.AT

Shizuo Kaji , Takeki Sudo , Kazushi Ahara This is my paper · ORCID
classification cs.CVmath.AT
keywords cubicalhomologypersistentcomputingimageripsercomplexesdata
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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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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Topology Optimization in Medical Image Segmentation with Fast Euler Characteristic

    eess.IV 2025-07 conditional novelty 5.0 of 10

    A fast Euler-characteristic-based violation map guides a refinement network that improves the topological correctness of medical image segmentations.

  2. Topological Data Analysis and Topological Deep Learning Beyond Persistent Homology -- A Review

    math.HO 2025-07 conditional novelty 3.0 of 10

    A survey organizing recent TDA and TDL methods beyond persistent homology and connecting them to data structures and vectorization.

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