no code implementations • 2 Apr 2014 • Jörg H. Kappes, Bjoern Andres, Fred A. Hamprecht, Christoph Schnörr, Sebastian Nowozin, Dhruv Batra, Sungwoong Kim, Bernhard X. Kausler, Thorben Kröger, Jan Lellmann, Nikos Komodakis, Bogdan Savchynskyy, Carsten Rother
However, on new and challenging types of models our findings disagree and suggest that polyhedral methods and integer programming solvers are competitive in terms of runtime and solution quality over a large range of model types.
no code implementations • 1 Jul 2014 • Jan Lellmann, Dirk A. Lorenz, Carola Schönlieb, Tuomo Valkonen
We propose the use of the Kantorovich-Rubinstein norm from optimal transport in imaging problems.
no code implementations • 3 Jul 2014 • Frank Lenzen, Jan Lellmann, Florian Becker, Christoph Schnörr
In the present paper we prove uniqueness for a larger class of problems and in particular independent of the image size.
2 code implementations • CVPR 2016 • Thomas Möllenhoff, Emanuel Laude, Michael Moeller, Jan Lellmann, Daniel Cremers
We propose a novel spatially continuous framework for convex relaxations based on functional lifting.
1 code implementation • 7 Apr 2016 • Emanuel Laude, Thomas Möllenhoff, Michael Moeller, Jan Lellmann, Daniel Cremers
Convex relaxations of nonconvex multilabel problems have been demonstrated to produce superior (provably optimal or near-optimal) solutions to a variety of classical computer vision problems.
no code implementations • 24 Jan 2017 • Juheon Lee, David Coomes, Carola-Bibiane Schonlieb, Xiaohao Cai, Jan Lellmann, Michele Dalponte, Yadvinder Malhi, Nathalie Butt, Mike Morecroft
Here we develop a 3D tree delineation method which uses graph cut to delineate trees from the full 3D LiDAR point cloud, and also makes use of any optical imagery available (hyperspectral imagery in our case).
no code implementations • 3 Aug 2017 • Yury Korolev, Jan Lellmann
In this approach, errors in the data and in the forward models are described using order intervals.
no code implementations • 27 Apr 2018 • Lars König, Jan Rühaak, Alexander Derksen, Jan Lellmann
Based on this analysis, we derive equivalent matrix-free closed-form expressions for derivative computations, eliminating the need for storing intermediate results and the costs of sparse matrix arithmetic.
1 code implementation • 27 Jul 2018 • Veronica Corona, Jan Lellmann, Peter Nestor, Carola-Bibiane Schoenlieb, Julio Acosta-Cabronero
Thalamic alterations are relevant to many neurological disorders including Alzheimer's disease, Parkinson's disease and multiple sclerosis.
1 code implementation • 12 Dec 2018 • Simone Parisotto, Jan Lellmann, Simon Masnou, Carola-Bibiane Schönlieb
We introduce a new class of higher-order total directional variation regularizers.
Numerical Analysis 47A52, 49M30, 49N45, 65J22, 94A08
no code implementations • 17 Dec 2018 • Daniel Budelmann, Lars König, Nils Papenberg, Jan Lellmann
We present a highly parallel method for accurate and efficient variational deformable 3D image registration on a consumer-grade graphics processing unit (GPU).
1 code implementation • 10 Jan 2020 • Roland Haase, Stefan Heldmann, Jan Lellmann
Low-rank and sparse decompositions and robust PCA (RPCA) are highly successful techniques in image processing and have recently found use in groupwise image registration.
no code implementations • 6 Jul 2020 • Thomas Vogt, Roland Haase, Danielle Bednarski, Jan Lellmann
By modifying the continuity equation, the approach can also be extended to models with higher-order regularization.
no code implementations • CVPR 2022 • Natacha Kuete Meli, Florian Mannel, Jan Lellmann
We propose an iterative method for estimating rigid transformations from point sets using adiabatic quantum computation.
1 code implementation • 15 Aug 2023 • Willem Diepeveen, Carlos Esteve-Yagüe, Jan Lellmann, Ozan Öktem, Carola-Bibiane Schönlieb
First, it comes with a rich structure to account for a wide range of geometries that can be modelled after an energy landscape.