1 code implementation • 18 Mar 2022 • Dennis Madsen, Jonathan Aellen, Andreas Morel-Forster, Thomas Vetter, Marcel Lüthi
Furthermore, we show how existing algorithms in the GiNGR framework can perform probabilistic registration to obtain a distribution of different registrations instead of a single best registration.
no code implementations • 8 Dec 2021 • Jean-Rassaire Fouefack, Bhushan Borotikar, Marcel Lüthi, Tania S. Douglas, Valérie Burdin, Tinashe E. M. Mutsvangwa
A deformation field-based metric is adapted in the method for modelling shape and intensity feature variation as well as for comparing rigid transformations (pose).
2 code implementations • ECCV 2020 • Dennis Madsen, Andreas Morel-Forster, Patrick Kahr, Dana Rahbani, Thomas Vetter, Marcel Lüthi
Furthermore, in a reconstruction task, we show how to estimate the posterior distribution of missing data without assuming a fixed point-to-point correspondence.
2 code implementations • 25 Sep 2017 • Thomas Gerig, Andreas Morel-Forster, Clemens Blumer, Bernhard Egger, Marcel Lüthi, Sandro Schönborn, Thomas Vetter
Non-rigid registration of faces is significant for many applications in computer vision, such as the construction of 3D Morphable face models (3DMMs).
no code implementations • 23 Mar 2016 • Marcel Lüthi, Christoph Jud, Thomas Gerig, Thomas Vetter
However, while for SSMs the shape variation is restricted to the span of the example data, with GPMMs we can define the shape variation using any Gaussian process.