Image Comparison and Scaling via Nonlinear Elasticity

John M. Ball; Christopher L. Horner

doi:10.1007/978-3-031-31975-4_43

Image Comparison and Scaling via Nonlinear Elasticity

John M. Ball^*, Christopher L. Horner

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

A nonlinear elasticity model for comparing images is formulated and analyzed, in which optimal transformations between images are sought as minimizers of an integral functional. The existence of minimizers in a suitable class of homeomorphisms between image domains is established under natural hypotheses. We investigate whether for linearly related images the minimization algorithm delivers the linear transformation as the unique minimizer.

Original language	English
Title of host publication	Scale Space and Variational Methods in Computer Vision. SSVM 2023
Editors	Luca Calatroni, Marco Donatelli, Serena Morigi, Marco Prato, Matteo Santacesaria
Publisher	Springer
Pages	565-574
Number of pages	10
ISBN (Electronic)	9783031319754
ISBN (Print)	9783031319747
DOIs	https://doi.org/10.1007/978-3-031-31975-4_43
Publication status	Published - 10 May 2023
Event	9th International Conference on Scale Space and Variational Methods in Computer Vision 2023 - Santa Margherita di Pula, Italy Duration: 21 May 2023 → 25 May 2023

Publication series

Name	Lecture Notes in Computer Science
Volume	14009
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	9th International Conference on Scale Space and Variational Methods in Computer Vision 2023
Abbreviated title	SSVM 2023
Country/Territory	Italy
City	Santa Margherita di Pula
Period	21/05/23 → 25/05/23

Keywords

image registration
Nonlinear elasticity
scaling

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-031-31975-4_43

https://arxiv.org/pdf/2303.10103.pdfLicence: CC BY

Cite this

@inproceedings{71b3df840ad34c71b868cac28b3022d6,

title = "Image Comparison and Scaling via Nonlinear Elasticity",

abstract = "A nonlinear elasticity model for comparing images is formulated and analyzed, in which optimal transformations between images are sought as minimizers of an integral functional. The existence of minimizers in a suitable class of homeomorphisms between image domains is established under natural hypotheses. We investigate whether for linearly related images the minimization algorithm delivers the linear transformation as the unique minimizer.",

keywords = "image registration, Nonlinear elasticity, scaling",

author = "Ball, {John M.} and Horner, {Christopher L.}",

year = "2023",

month = may,

day = "10",

doi = "10.1007/978-3-031-31975-4_43",

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editor = "Luca Calatroni and Marco Donatelli and Serena Morigi and Marco Prato and Matteo Santacesaria",

booktitle = "Scale Space and Variational Methods in Computer Vision. SSVM 2023",

note = "9th International Conference on Scale Space and Variational Methods in Computer Vision 2023, SSVM 2023 ; Conference date: 21-05-2023 Through 25-05-2023",

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Ball, JM & Horner, CL 2023, Image Comparison and Scaling via Nonlinear Elasticity. in L Calatroni, M Donatelli, S Morigi, M Prato & M Santacesaria (eds), Scale Space and Variational Methods in Computer Vision. SSVM 2023. Lecture Notes in Computer Science, vol. 14009, Springer, pp. 565-574, 9th International Conference on Scale Space and Variational Methods in Computer Vision 2023, Santa Margherita di Pula, Italy, 21/05/23. https://doi.org/10.1007/978-3-031-31975-4_43

Image Comparison and Scaling via Nonlinear Elasticity. / Ball, John M.; Horner, Christopher L.
Scale Space and Variational Methods in Computer Vision. SSVM 2023. ed. / Luca Calatroni; Marco Donatelli; Serena Morigi; Marco Prato; Matteo Santacesaria. Springer, 2023. p. 565-574 (Lecture Notes in Computer Science; Vol. 14009).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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