A nonlinear elasticity model in computer vision
John M. Ball, Christopher L. Horner

TL;DR
This paper introduces a nonlinear elasticity model for image comparison, proving the existence of optimal transformations under certain conditions and exploring properties of minimisers for affine image relations.
Contribution
It establishes existence results for minimisers of a nonlinear elasticity functional in image analysis and proposes a new model involving second derivatives of transformations.
Findings
Existence of minimisers is proven under natural conditions.
Affine mappings are minimisers for certain functional classes.
A new model with second derivatives guarantees properties for all affine image pairs.
Abstract
The purpose of this paper is to analyze a nonlinear elasticity model introduced by the authors for comparing two images, regarded as bounded open subsets of together with associated vector-valued intensity maps. Optimal transformations between the images are sought as minimisers of an integral functional among orientation-preserving homeomorphisms. The existence of minimisers is proved under natural coercivity and polyconvexity conditions, assuming only that the intensity functions are bounded measurable. Variants of the existence theorem are also proved, first under the constraint that finite sets of landmark points in the two images are mapped one to the other, and second when one image is to be compared to an unknown part of another. The question is studied as to whether for images related by an affine mapping the unique minimiser is given by that affine mapping. For a…
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Taxonomy
TopicsImage Processing Techniques and Applications · Medical Image Segmentation Techniques · Advanced Vision and Imaging
