Efficient computation of low-rank Gaussian process models for surface and image registration

Dölz, Jürgen and Gerig, Thomas and Harbrecht , Helmut and Lüthi, Marcel and Vetter, Thomas. (2017) Efficient computation of low-rank Gaussian process models for surface and image registration. Preprints Fachbereich Mathematik, 2017 (01).

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Official URL: https://edoc.unibas.ch/69940/

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Gaussian Process Morphable Models (GPMMs) are a unifying approach to non-rigid surface and image registration, where a deformation prior is defined using a Gaussian process. By a simple exchange of the covariance function we can formulate a wide variety of different deformation priors, such as spline-based models, free-form deformations or statistical shape and deformation models. How well the method works in practical applications depends crucially on how well a low-rank approximation of the Gaussian process can be computed. In this article we propose the use of the pivoted Cholesky decomposition for this task. This method makes it possible to efficiently compute a low-rank approximation for very large point sets, such as given by 3D meshes or 3D image grids, with a rigorously controlled approximation error. Compared to the current state of the art, which is based on the Nystro ̈m method, the approximation error is controllable and can be specified by a user-defined threshold. Further we propose a computationally more efficient and greedy alternative to currently used Karhunen-Loève expansion. This makes it possible to compute more accurate model approximations at the same computational costs. Detailed experiments from the registration of high quality human face scans and medical CT images containing the forearm with Ulna and Radius demonstrate the efficiency of the method and the computational advantages over the Nyström method.
Faculties and Departments:05 Faculty of Science > Departement Mathematik und Informatik > Mathematik > Computational Mathematics (Harbrecht)
12 Special Collections > Preprints Fachbereich Mathematik
UniBasel Contributors:Dölz, Jürgen and Gerig, Thomas and Harbrecht, Helmut and Lüthi, Marcel and Vetter, Thomas
Item Type:Preprint
Publisher:Universität Basel
edoc DOI:
Last Modified:17 Apr 2019 20:52
Deposited On:28 Mar 2019 09:51

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