A finite sample of directions and filtration values of the persistent homology transform can be extended to the whole sphere with interleaving-distance error at most twice the sampling radius plus the height sampling error.
An Invitation to the Euler Characteristic Transform
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abstract
The Euler characteristic transform (ECT) is a simple to define yet powerful representation of shape. The idea is to encode an embedded shape using sub-level sets of a a function defined based on a given direction, and then returning the Euler characteristics of these sublevel sets. Because the ECT has been shown to be injective on the space of embedded simplicial complexes, it has been used for applications spanning a range of disciplines, including plant morphology and protein structural analysis. In this survey article, we present a comprehensive overview of the Euler characteristic transform, highlighting the main idea on a simple leaf example, and surveying its its key concepts, theoretical foundations, and available applications.
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Kan Approximations of the Persistent Homology Transform
A finite sample of directions and filtration values of the persistent homology transform can be extended to the whole sphere with interleaving-distance error at most twice the sampling radius plus the height sampling error.