How Many Directions Determine a Shape and other Sufficiency Results for Two Topological Transforms
By J. Curry, Sayan Mukherjee, Katharine Turner. Transactions of the American Mathematical Society. Series B, 2018
95 citations recorded by the source corpus.
In this paper we consider two topological transforms that are popular in applied topology: the Persistent Homology Transform and the Euler Characteristic Transform. Both of these transforms are of interest for their mathematical properties as well as their applications to science and engineering, because they provide a way of summarizing shapes in a topological, yet quantitative, way. Both transforms take a shape, viewed as a tame subset M M of R d \mathbb { R}^d , and associates to each direction v ∈ S d − 1 v\in S^{d-1} a shape summary obtained by scanning M M in the direction v v . These shape summaries are either persistence diagrams or piecewise constant integer-valued functions called Euler curves. By using an inversion theorem of Schapira, we show that both transforms are injective on the space of shapes, i.e. each shape has a unique transform. Moreover, we prove that these transforms determine continuous maps from the sphere to the space of persistence diagrams, equipped with any Wasserstein p p -distance, or the space of Euler curves, equipped with certain L p L^p norms. By making use of a stratified space structure on the sphere, induced by hyperplane divisions, we prove additional uniqueness results in terms of distributions on the space of Euler curves. Finally, our main result proves that any shape in a certain uncountable space of PL embedded shapes with plausible geometric bounds can be uniquely determined using only finitely many directions.
Subfields: Biophysics, Computational Theory and Mathematics, Mathematical Physics
View Paper: https://www.semanticscholar.org/paper/0c2129d5862fd39cfb7416d54e99f4349f977a3f
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Citations in the corpus
- An Introduction to Topological Data Analysis: Fundamental and Practical Aspects for Data Scientists (2017, methodology): “The persistence homology transform introduced in the studies by Curry et al. (2018), Turner et al. (2014b) to study shapes in R takes a different path by looking at the persistence homology of the sublevel set filtration induced by the projection of the considered shape in each direction in R .”