Peter Gemmar — accepted AI-conference papers

2016

Linear Shape Deformation Models With Local Support Using Graph-Based Structured Matrix Factorisation

Florian Bernard, Peter Gemmar, Frank Hertel, Jorge Goncalves, Johan Thunberg

CVPR 2016spotlight

Representing 3D shape deformations by high-dimensional linear models has many applications in computer vision and medical imaging. Commonly, using Principal Components Analysis a low-dimensional subspace of the high-dimensional shape space is determined. However, the resulting factors (the most domi…

Cited by 19 PDF ↗Scholar ↗

2015

A Solution for Multi-Alignment by Transformation Synchronisation

Florian Bernard, Johan Thunberg, Peter Gemmar, Frank Hertel, Andreas Husch, Jorge Goncalves

CVPR 2015poster

The alignment of a set of objects by means of transformations plays an important role in computer vision. Whilst the case for only two objects can be solved globally, when multiple objects are considered usually iterative methods are used. In practice the iterative methods perform well if the relati…

Cited by 70 Source ↗PDF ↗Scholar ↗