Daniel Tenbrinck

ORCID

Publication Activity (10 Years)

Years Active: 2010-2024
Publications (10 Years): 26

Top Venues

Universität Münster (PHD)

Education History

Universität Münster

PHD

Publications

Ariane Fazeny, Daniel Tenbrinck, Kseniia Lukin, Martin Burger
Hypergraph p-Laplacians and Scale Spaces. J. Math. Imaging Vis. 66 (4) (2024)
Farid Bozorgnia, Leon Bungert, Daniel Tenbrinck
The Infinity Laplacian Eigenvalue Problem: Reformulation and a Numerical Scheme. J. Sci. Comput. 98 (2) (2024)
Samira Kabri, Tim Roith, Daniel Tenbrinck, Martin Burger
Resolution-Invariant Image Classification Based on Fourier Neural Operators. SSVM (2023)
Ariane Fazeny, Daniel Tenbrinck, Martin Burger
Hypergraph p-Laplacians, Scale Spaces, and Information Flow in Networks. CoRR (2023)
Samira Kabri, Tim Roith, Daniel Tenbrinck, Martin Burger
Resolution-Invariant Image Classification based on Fourier Neural Operators. CoRR (2023)
Ariane Fazeny, Daniel Tenbrinck, Martin Burger
Hypergraph p-Laplacians, Scale Spaces, and Information Flow in Networks. SSVM (2023)
Leon Bungert, Tim Roith, Daniel Tenbrinck, Martin Burger
A Bregman Learning Framework for Sparse Neural Networks. J. Mach. Learn. Res. 23 (2022)
Leo Schwinn, An Nguyen, René Raab, Leon Bungert, Daniel Tenbrinck, Dario Zanca, Martin Burger, Bjoern M. Eskofier
Identifying Untrustworthy Predictions in Neural Networks by Geometric Gradient Analysis. CoRR (2021)
Leo Schwinn, An Nguyen, René Raab, Dario Zanca, Bjoern M. Eskofier, Daniel Tenbrinck, Martin Burger
Dynamically Sampled Nonlocal Gradients for Stronger Adversarial Attacks. IJCNN (2021)
Leon Bungert, René Raab, Tim Roith, Leo Schwinn, Daniel Tenbrinck
CLIP: Cheap Lipschitz Training of Neural Networks. SSVM (2021)
Leo Schwinn, An Nguyen, René Raab, Leon Bungert, Daniel Tenbrinck, Dario Zanca, Martin Burger, Björn M. Eskofier
Identifying untrustworthy predictions in neural networks by geometric gradient analysis. UAI (2021)
Leon Bungert, René Raab, Tim Roith, Leo Schwinn, Daniel Tenbrinck
CLIP: Cheap Lipschitz Training of Neural Networks. CoRR (2021)
Leon Bungert, Tim Roith, Daniel Tenbrinck, Martin Burger
A Bregman Learning Framework for Sparse Neural Networks. CoRR (2021)
Ronny Bergmann, Roland Herzog, Mauricio S. Louzeiro, Daniel Tenbrinck, José Vidal-Núñez
Fenchel Duality Theory and a Primal-Dual Algorithm on Riemannian Manifolds. Found. Comput. Math. 21 (6) (2021)
Leon Bungert, Tim Roith, Daniel Tenbrinck, Martin Burger
Neural Architecture Search via Bregman Iterations. CoRR (2021)
Leo Schwinn, Daniel Tenbrinck, An Nguyen, René Raab, Martin Burger, Bjoern M. Eskofier
Sampled Nonlocal Gradients for Stronger Adversarial Attacks. CoRR (2020)
Farid Bozorgnia, Leon Bungert, Daniel Tenbrinck
The Infinity Laplacian eigenvalue problem: reformulation and a numerical scheme. CoRR (2020)
Leon Bungert, Martin Burger, Daniel Tenbrinck
Computing Nonlinear Eigenfunctions via Gradient Flow Extinction. CoRR (2019)
Ronny Bergmann, Roland Herzog, Daniel Tenbrinck, José Vidal-Núñez
Fenchel Duality for Convex Optimization and a Primal Dual Algorithm on Riemannian Manifolds. CoRR (2019)
Leon Bungert, Martin Burger, Daniel Tenbrinck
Computing Nonlinear Eigenfunctions via Gradient Flow Extinction. SSVM (2019)
Daniel Tenbrinck, Fjedor Gaede, Martin Burger
Variational Graph Methods for Efficient Point Cloud Sparsification. CoRR (2019)
Ronny Bergmann, Daniel Tenbrinck
A Graph Framework for Manifold-Valued Data. SIAM J. Imaging Sci. 11 (1) (2018)
Ronny Bergmann, Daniel Tenbrinck
Nonlocal Inpainting of Manifold-Valued Data on Finite Weighted Graphs. GSI (2017)
Abderrahim Elmoataz, Matthieu Toutain, Daniel Tenbrinck
On the p-Laplacian and ∞-Laplacian on Graphs with Applications in Image and Data Processing. SIAM J. Imaging Sci. 8 (4) (2015)
Daniel Tenbrinck, Xiaoyi Jiang
Image segmentation with arbitrary noise models by solving minimal surface problems. Pattern Recognit. 48 (11) (2015)
Daniel Tenbrinck, François Lozes, Abderrahim Elmoataz
Solving Minimal Surface Problems on Surfaces and Point Clouds. SSVM (2015)
Kathrin Ungru, Daniel Tenbrinck, Xiaoyi Jiang, Jörg Stypmann
Automatic classification of left ventricular wall segments in small animal ultrasound imaging. Comput. Methods Programs Biomed. 117 (1) (2014)
Sebastian Suhr, Daniel Tenbrinck, Martin Burger, Jan Modersitzki
Registration of Noisy Images via Maximum A-Posteriori Estimation. WBIR (2014)
Xiaoyi Jiang, Mohammad Dawood, Fabian Gigengack, Benjamin Risse, Sönke Schmid, Daniel Tenbrinck, Klaus P. Schäfers
Biomedical Imaging: A Computer Vision Perspective. CAIP (1) (2013)
Daniel Tenbrinck, Sönke Schmid, Xiaoyi Jiang, Klaus P. Schäfers, Jörg Stypmann
Histogram-Based Optical Flow for Motion Estimation in Ultrasound Imaging. J. Math. Imaging Vis. 47 (1-2) (2013)
Xiaoyi Jiang, Daniel Tenbrinck
Region Based Contour Detection by Dynamic Programming. CAIP (2) (2013)
Daniel Tenbrinck, Xiaoyi Jiang
Discriminant Analysis Based Level Set Segmentation for Ultrasound Imaging. CAIP (2) (2013)
Alex Sawatzky, Daniel Tenbrinck, Xiaoyi Jiang, Martin Burger
A Variational Framework for Region-Based Segmentation Incorporating Physical Noise Models. J. Math. Imaging Vis. 47 (3) (2013)
Daniel Tenbrinck, Alex Sawatzky, Xiaoyi Jiang, Martin Burger, Wladimir Haffner, Patrick Willems, Matthias Paul, Jörg Stypmann
Impact of Physical Noise Modeling on Image Segmentation in Echocardiography. VCBM (2012)
Sönke Schmid, Daniel Tenbrinck, Xiaoyi Jiang, Klaus P. Schäfers, Klaus Tiemann, Jörg Stypmann
Histogram-Based Optical Flow for Functional Imaging in Echocardiography. CAIP (1) (2011)
Daniel Tenbrinck, Mohammad Dawood, Fabian Gigengack, Michael Fieseler, Xiaoyi Jiang, Klaus Schiifers
Motion correction in positron emission tomography considering partial volume effects in optical flow estimation. ISBI (2010)

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Publication Activity (10 Years)

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Daniel Tenbrinck