A function space perspective on stochastic shape evolution
Computer Vision and Pattern Recognition
2023-02-13 v1 Probability
Abstract
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.
Cite
@article{arxiv.2302.05382,
title = {A function space perspective on stochastic shape evolution},
author = {Elizabeth Baker and Thomas Besnier and Stefan Sommer},
journal= {arXiv preprint arXiv:2302.05382},
year = {2023}
}