A Function Space Perspective on Stochastic Shape Evolution

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Modelling randomness in shape data, for example, the evolution of shapes of organisms in biology, requires stochastic models of shapes. This paper presents a new stochastic shape model based on a description of shapes as functions in a Sobolev space. Using an explicit orthonormal basis as a reference frame for the noise, the model is independent of the parameterisation of the mesh. We define the stochastic model, explore its properties, and illustrate examples of stochastic shape evolutions using the resulting numerical framework.

Original languageEnglish
Title of host publicationImage Analysis - 23rd Scandinavian Conference, SCIA 2023, Proceedings
EditorsRikke Gade, Michael Felsberg, Joni-Kristian Kämäräinen
Number of pages15
PublisherSpringer
Publication date2023
Pages278-292
ISBN (Print)9783031314377
DOIs
Publication statusPublished - 2023
Event23nd Scandinavian Conference on Image Analysis, SCIA 2023 - Lapland, Finland
Duration: 18 Apr 202321 Apr 2023

Conference

Conference23nd Scandinavian Conference on Image Analysis, SCIA 2023
LandFinland
ByLapland
Periode18/04/202321/04/2023
SeriesLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13886 LNCS
ISSN0302-9743

Bibliographical note

Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

    Research areas

  • 3D mesh processing, diffusions, shape space

Links

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