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Daniel Tenbrinck
Daniel Tenbrinck
Department of Data Science, FAU Erlangen- Nürnberg
Email verificata su fau.de - Home page
Titolo
Citata da
Citata da
Anno
On the p-Laplacian and ∞-Laplacian on Graphs with Applications in Image and Data Processing
A Elmoataz, M Toutain, D Tenbrinck
SIAM Journal on Imaging Sciences 8 (4), 2412-2451, 2015
1202015
On the p-Laplacian and ∞-Laplacian on Graphs with Applications in Image and Data Processing
A Elmoataz, M Toutain, D Tenbrinck
SIAM Journal on Imaging Sciences 8 (4), 2412-2451, 2015
1202015
A variational framework for region-based segmentation incorporating physical noise models
A Sawatzky, D Tenbrinck, X Jiang, M Burger
Journal of Mathematical Imaging and Vision 47 (3), 179-209, 2013
532013
CLIP: Cheap Lipschitz Training of Neural Networks
L Bungert, R Raab, T Roith, L Schwinn, D Tenbrinck
International Conference on Scale Space and Variational Methods in Computer …, 2021
302021
CLIP: Cheap Lipschitz training of neural networks
L Bungert, R Raab, T Roith, L Schwinn, D Tenbrinck
International Conference on Scale Space and Variational Methods in Computer …, 2021
302021
Histogram-based optical flow for motion estimation in ultrasound imaging
D Tenbrinck, S Schmid, X Jiang, K Schäfers, J Stypmann
Journal of mathematical imaging and vision 47 (1), 138-150, 2013
262013
A Graph Framework for Manifold-valued Data
R Bergmann, D Tenbrinck
SIAM Journal on Imaging Sciences 11 (1), 325-360, 2018
252018
Fenchel duality theory and a primal-dual algorithm on Riemannian manifolds
R Bergmann, R Herzog, MS Louzeiro, D Tenbrinck, J Vidal-Núñez
Foundations of Computational Mathematics, 1-40, 2021
242021
Image segmentation with arbitrary noise models by solving minimal surface problems
D Tenbrinck, X Jiang
Pattern Recognition 48 (11), 3293-3309, 2015
192015
A Bregman learning framework for sparse neural networks
L Bungert, T Roith, D Tenbrinck, M Burger
The Journal of Machine Learning Research 23 (1), 8673-8715, 2022
142022
Impact of Physical Noise Modeling on Image Segmentation in Echocardiography.
D Tenbrinck, A Sawatzky, X Jiang, M Burger, W Haffner, P Willems, ...
VCBM, 33-40, 2012
132012
Computing nonlinear eigenfunctions via gradient flow extinction
L Bungert, M Burger, D Tenbrinck
Scale Space and Variational Methods in Computer Vision: 7th International …, 2019
122019
Identifying untrustworthy predictions in neural networks by geometric gradient analysis
L Schwinn, A Nguyen, R Raab, L Bungert, D Tenbrinck, D Zanca, ...
Uncertainty in Artificial Intelligence, 854-864, 2021
102021
Discriminant analysis based level set segmentation for ultrasound imaging
D Tenbrinck, X Jiang
International Conference on Computer Analysis of Images and Patterns, 144-151, 2013
82013
Biomedical imaging: a computer vision perspective
X Jiang, M Dawood, F Gigengack, B Risse, S Schmid, D Tenbrinck, ...
Computer Analysis of Images and Patterns: 15th International Conference …, 2013
82013
Using migrating cells as probes to illuminate features in live embryonic tissues
S Gross-Thebing, L Truszkowski, D Tenbrinck, H Sánchez-Iranzo, ...
Science Advances 6 (49), eabc5546, 2020
72020
Variational Graph Methods for Efficient Point Cloud Sparsification
D Tenbrinck, F Gaede, M Burger
arXiv preprint arXiv:1903.02858, 2019
72019
Solving minimal surface problems on surfaces and point clouds
D Tenbrinck, F Lozes, A Elmoataz
Scale Space and Variational Methods in Computer Vision: 5th International …, 2015
72015
Automatic classification of left ventricular wall segments in small animal ultrasound imaging
K Ungru, D Tenbrinck, X Jiang, J Stypmann
Computer methods and programs in biomedicine 117 (1), 2-12, 2014
72014
Dynamically sampled nonlocal gradients for stronger adversarial attacks
L Schwinn, A Nguyen, R Raab, D Zanca, BM Eskofier, D Tenbrinck, ...
2021 International Joint Conference on Neural Networks (IJCNN), 1-8, 2021
62021
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Articoli 1–20