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How to deform and shake images?

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In recent years, several methods and frameworks have been developed to deform images in the aim of solving problems such as shape analysis or image registration. The application and usefulness of these methods is now well established as well as their mathematical foundation. Nevertheless, because various uncertainties remain at all stages of the image analysis procedure (from data capture to intrinsic variability within a dataset), practical extensions of these deterministic methods should be available. In this talk, I will walk you through one of them, by starting from the geometrical formulation of the theory of large deformation matching, then implementing a particular type of stochastic deformation to preserve the original geometrical structure to end with the description of practical methods for the estimation of the unknown noise parameters of the model. I will illustrate this theory with numerical solutions for a discrete representation of images, where the method of moments can easily be implemented to solve this inverse problem of estimating the noise parameters. This is joint work with Stefan Sommer and Darryl Holm.

This talk is part of the Isaac Newton Institute Seminar Series series.

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