REVIEW 5 cited by
Cubical Ripser: Software for computing persistent homology of image and volume data
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Cubical Ripser: Software for computing persistent homology of image and volume data
read the original abstract
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.
Forward citations
Cited by 5 Pith papers
-
The 3D Structure and Kinematics of the Local Disk-Halo Interface: Intermediate-velocity Clouds are the Minority of High-altitude Clouds in the Solar Neighborhood
3D dust mapping shows IVCs are only 18% of high-altitude clouds near the Sun, with strong north-south asymmetry and most structures at low radial velocity.
-
Quantifying displacement: an urban expansion consequence via persistent homology
Persistent homology on spatio-temporal cubical complexes from address records quantifies urban population displacement and identifies affected neighborhoods and years in a Madrid case study.
-
From Frames to Features: Scalable Zigzag Persistence for Binary Video
A graph encoding of connected-component dynamics enables direct extraction of H0 and H1 zigzag barcodes for binary video, bypassing cubical complexes and achieving linear-time scaling via Dey-Hou decomposition.
-
Topology-Informed Neural Networks for Flood Detection in Optical and Synthetic Aperture Radar Imagery
Topological descriptors from imagery provide independent flood signals and complement neural networks for more robust detection.
-
Topology-Informed Neural Networks for Flood Detection in Optical and Synthetic Aperture Radar Imagery
Topological descriptors extracted from images provide independent flood signals and improve neural network performance when combined with standard CNN and vision transformer backbones on the SEN12-FLOOD dataset.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.