A Geometric Framework for Stochastic Shape Analysis

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A Geometric Framework for Stochastic Shape Analysis. / Arnaudon, Alexis; Holm, Darryl D.; Sommer, Stefan.

I: Foundations of Computational Mathematics, Bind 19, Nr. 3, 2019, s. 653–701.

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningfagfællebedømt

Harvard

Arnaudon, A, Holm, DD & Sommer, S 2019, 'A Geometric Framework for Stochastic Shape Analysis', Foundations of Computational Mathematics, bind 19, nr. 3, s. 653–701. https://doi.org/10.1007/s10208-018-9394-z

APA

Arnaudon, A., Holm, D. D., & Sommer, S. (2019). A Geometric Framework for Stochastic Shape Analysis. Foundations of Computational Mathematics, 19(3), 653–701. https://doi.org/10.1007/s10208-018-9394-z

Vancouver

Arnaudon A, Holm DD, Sommer S. A Geometric Framework for Stochastic Shape Analysis. Foundations of Computational Mathematics. 2019;19(3):653–701. https://doi.org/10.1007/s10208-018-9394-z

Author

Arnaudon, Alexis ; Holm, Darryl D. ; Sommer, Stefan. / A Geometric Framework for Stochastic Shape Analysis. I: Foundations of Computational Mathematics. 2019 ; Bind 19, Nr. 3. s. 653–701.

Bibtex

@article{64101c7e2080457b86ef823682b98b97,
title = "A Geometric Framework for Stochastic Shape Analysis",
abstract = "We introduce a stochastic model of diffeomorphisms, whose action on a variety of data types descends to stochastic evolution of shapes, images and landmarks. The stochasticity is introduced in the vector field which transports the data in the large deformation diffeomorphic metric mapping framework for shape analysis and image registration. The stochasticity thereby models errors or uncertainties of the flow in following the prescribed deformation velocity. The approach is illustrated in the example of finite-dimensional landmark manifolds, whose stochastic evolution is studied both via the Fokker–Planck equation and by numerical simulations. We derive two approaches for inferring parameters of the stochastic model from landmark configurations observed at discrete time points. The first of the two approaches matches moments of the Fokker–Planck equation to sample moments of the data, while the second approach employs an expectation-maximization based algorithm using a Monte Carlo bridge sampling scheme to optimise the data likelihood. We derive and numerically test the ability of the two approaches to infer the spatial correlation length of the underlying noise.",
keywords = "Shape analysis, Stochastic flows of diffeomorphisms, Stochastic geometric mechanics, Stochastic landmark dynamics",
author = "Alexis Arnaudon and Holm, {Darryl D.} and Stefan Sommer",
year = "2019",
doi = "10.1007/s10208-018-9394-z",
language = "English",
volume = "19",
pages = "653–701",
journal = "Foundations of Computational Mathematics",
issn = "1615-3375",
publisher = "Springer",
number = "3",

}

RIS

TY - JOUR

T1 - A Geometric Framework for Stochastic Shape Analysis

AU - Arnaudon, Alexis

AU - Holm, Darryl D.

AU - Sommer, Stefan

PY - 2019

Y1 - 2019

N2 - We introduce a stochastic model of diffeomorphisms, whose action on a variety of data types descends to stochastic evolution of shapes, images and landmarks. The stochasticity is introduced in the vector field which transports the data in the large deformation diffeomorphic metric mapping framework for shape analysis and image registration. The stochasticity thereby models errors or uncertainties of the flow in following the prescribed deformation velocity. The approach is illustrated in the example of finite-dimensional landmark manifolds, whose stochastic evolution is studied both via the Fokker–Planck equation and by numerical simulations. We derive two approaches for inferring parameters of the stochastic model from landmark configurations observed at discrete time points. The first of the two approaches matches moments of the Fokker–Planck equation to sample moments of the data, while the second approach employs an expectation-maximization based algorithm using a Monte Carlo bridge sampling scheme to optimise the data likelihood. We derive and numerically test the ability of the two approaches to infer the spatial correlation length of the underlying noise.

AB - We introduce a stochastic model of diffeomorphisms, whose action on a variety of data types descends to stochastic evolution of shapes, images and landmarks. The stochasticity is introduced in the vector field which transports the data in the large deformation diffeomorphic metric mapping framework for shape analysis and image registration. The stochasticity thereby models errors or uncertainties of the flow in following the prescribed deformation velocity. The approach is illustrated in the example of finite-dimensional landmark manifolds, whose stochastic evolution is studied both via the Fokker–Planck equation and by numerical simulations. We derive two approaches for inferring parameters of the stochastic model from landmark configurations observed at discrete time points. The first of the two approaches matches moments of the Fokker–Planck equation to sample moments of the data, while the second approach employs an expectation-maximization based algorithm using a Monte Carlo bridge sampling scheme to optimise the data likelihood. We derive and numerically test the ability of the two approaches to infer the spatial correlation length of the underlying noise.

KW - Shape analysis

KW - Stochastic flows of diffeomorphisms

KW - Stochastic geometric mechanics

KW - Stochastic landmark dynamics

UR - http://www.scopus.com/inward/record.url?scp=85048455538&partnerID=8YFLogxK

U2 - 10.1007/s10208-018-9394-z

DO - 10.1007/s10208-018-9394-z

M3 - Journal article

VL - 19

SP - 653

EP - 701

JO - Foundations of Computational Mathematics

JF - Foundations of Computational Mathematics

SN - 1615-3375

IS - 3

ER -

ID: 203942592