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An Invitation to the Euler Characteristic Transform

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arxiv 2310.10395 v1 pith:JNVHY76K submitted 2023-10-16 cs.CG

classification cs.CG
keywords eulercharacteristictransformapplicationsbeenembeddedideasets
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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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Cited by 3 Pith papers

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

  1. Kan Approximations of the Persistent Homology Transform

    math.AT 2025-07 conditional novelty 7.0 of 10

    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.

  2. On the Stability of the Euler Characteristic Transform for a Perturbed Embedding

    cs.CG 2025-06 conditional novelty 4.0 of 10

    For two embeddings of the same finite simplicial complex, the integrated distance between their Euler Characteristic Transforms is bounded by a constant times the total vertex displacement, and SELECT obeys an analogo...

  3. Latent Manifold Reconstruction and Representation with Topological and Geometrical Regularization

    cs.LG 2025-05 conditional novelty 4.0 of 10

    The paper integrates a manifold fitting layer with topological and geometric regularizers in an autoencoder, reporting improved manifold reconstruction and dimensionality reduction on noisy point clouds.

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