A nonlinear elasticity model in computer vision

The purpose of this paper is to analyze a nonlinear elasticity model previously introduced by the authors for comparing two images, regarded as bounded open subsets of $\R^n$ 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 a linear mapping the unique minimizer is given by that linear mapping. For a natural class of functional integrands an example is given guaranteeing that this property holds for pairs of images in which the second is a scaling of the first by a constant factor. However for the property to hold for arbitrary pairs of linearly related images it is shown that the integrand has to depend on the gradient of the transformation as a convex function of its determinant alone. This suggests a new model in which the integrand depends also on second derivatives of the transformation, and an example is given for which both existence of minimizers is assured and the above property holds for all pairs of linearly related images.

Image comparison and scaling via nonlinear elasticity

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.