Morse theory and persistent homology for topological analysis of 3D images of complex materials
Olaf Delgado‐Friedrichs, Vanessa Robins, Adrian P. Sheppard
Abstract
Olaf Delgado‐Friedrichs, Vanessa Robins, Adrian P. Sheppard
Abstract
We develop topologically accurate and compatible definitions for the skeleton and watershed segmentation of a 3D digital object that are computed by a single algorithm. These definitions are based on a discrete gradient vector field derived from a signed distance transform. This gradient vector field is amenable to topological analysis and simplification via For-man's discrete Morse theory and provides a filtration that can be used as input to persistent homology algorithms. Efficient implementations allow us to process large-scale x-ray micro-CT data of rock cores and other materials.
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We develop topologically accurate and compatible definitions for the skeleton and watershed segmentation of a 3D digital object that are computed by a single algorithm. These definitions are based on a discrete gradient vector field derived from a signed distance transform. This gradient vector field is amenable to topological analysis and simplification via For-man's discrete Morse theory and provides a filtration that can be used as input to persistent homology algorithms. Efficient implementations allow us to process large-scale x-ray micro-CT data of rock cores and other materials.
Key concepts: Persistent homology, Discrete Morse theory, Morse theory, Topological data analysis, Topology (electrical circuits), Signed distance function, Computer science, Topological skeleton