Motion correction in positron emission tomography considering partial volume effects in optical flow estimation. - Daniel Tenbrinck; Mohammad Dawood; Fabian Gigengack; Michael Fieseler; Xiaoyi Jiang 001; Klaus Schiifers (ISBI, 2010)

Histogram-Based Optical Flow for Functional Imaging in Echocardiography. - Sönke Schmid; Daniel Tenbrinck; Xiaoyi Jiang 001; Klaus P. Schäfers; Klaus Tiemann; Jörg Stypmann (CAIP (1), 2011)

Impact of Physical Noise Modeling on Image Segmentation in Echocardiography. - Daniel Tenbrinck; Alex Sawatzky; Xiaoyi Jiang 001; Martin Burger 001; Wladimir Haffner; Patrick Willems; Matthias Paul; Jörg Stypmann (VCBM, 2012)

Variational Methods for Medical Ultrasound Imaging. - Daniel Tenbrinck (2013)

Biomedical Imaging: A Computer Vision Perspective. - Xiaoyi Jiang 001; Mohammad Dawood; Fabian Gigengack; Benjamin Risse; Sönke Schmid; Daniel Tenbrinck; Klaus P. Schäfers (CAIP (1), 2013)

Region Based Contour Detection by Dynamic Programming. - Xiaoyi Jiang 001; Daniel Tenbrinck (CAIP (2), 2013)

Discriminant Analysis Based Level Set Segmentation for Ultrasound Imaging. - Daniel Tenbrinck; Xiaoyi Jiang 001 (CAIP (2), 2013)

A Variational Framework for Region-Based Segmentation Incorporating Physical Noise Models. - Alex Sawatzky; Daniel Tenbrinck; Xiaoyi Jiang 001; Martin Burger 001 (J. Math. Imaging Vis., 2013)

Histogram-Based Optical Flow for Motion Estimation in Ultrasound Imaging. - Daniel Tenbrinck; Sönke Schmid; Xiaoyi Jiang 001; Klaus P. Schäfers; Jörg Stypmann (J. Math. Imaging Vis., 2013)

Registration of Noisy Images via Maximum A-Posteriori Estimation. - Sebastian Suhr; Daniel Tenbrinck; Martin Burger 001; Jan Modersitzki (WBIR, 2014)

Automatic classification of left ventricular wall segments in small animal ultrasound imaging. - Kathrin Ungru; Daniel Tenbrinck; Xiaoyi Jiang 001; Jörg Stypmann (Comput. Methods Programs Biomed., 2014)

Solving Minimal Surface Problems on Surfaces and Point Clouds. - Daniel Tenbrinck; François Lozes; Abderrahim Elmoataz (SSVM, 2015)

Image segmentation with arbitrary noise models by solving minimal surface problems. - Daniel Tenbrinck; Xiaoyi Jiang 001 (Pattern Recognit., 2015)

On the p-Laplacian and ∞-Laplacian on Graphs with Applications in Image and Data Processing. - Abderrahim Elmoataz; Matthieu Toutain; Daniel Tenbrinck (SIAM J. Imaging Sci., 2015)

Nonlocal Inpainting of Manifold-Valued Data on Finite Weighted Graphs. - Ronny Bergmann; Daniel Tenbrinck (GSI, 2017)

A Graph Framework for Manifold-Valued Data. - Ronny Bergmann; Daniel Tenbrinck (SIAM J. Imaging Sci., 2018)

Computing Nonlinear Eigenfunctions via Gradient Flow Extinction. - Leon Bungert; Martin Burger 001; Daniel Tenbrinck (SSVM, 2019)

Variational Graph Methods for Efficient Point Cloud Sparsification. - Daniel Tenbrinck; Fjedor Gaede; Martin Burger 001 (CoRR, 2019)

Fenchel Duality for Convex Optimization and a Primal Dual Algorithm on Riemannian Manifolds. - Ronny Bergmann; Roland Herzog; Daniel Tenbrinck; José Vidal-Núñez (CoRR, 2019)

Sampled Nonlocal Gradients for Stronger Adversarial Attacks. - Leo Schwinn; Daniel Tenbrinck; An Nguyen; René Raab; Martin Burger 001; Bjoern M. Eskofier (CoRR, 2020)

Dynamically Sampled Nonlocal Gradients for Stronger Adversarial Attacks. - Leo Schwinn; An Nguyen; René Raab; Dario Zanca; Bjoern M. Eskofier; Daniel Tenbrinck; Martin Burger 001 (IJCNN, 2021)

CLIP: Cheap Lipschitz Training of Neural Networks. - Leon Bungert; René Raab; Tim Roith; Leo Schwinn; Daniel Tenbrinck (SSVM, 2021)

Identifying untrustworthy predictions in neural networks by geometric gradient analysis. - Leo Schwinn; An Nguyen; René Raab; Leon Bungert; Daniel Tenbrinck; Dario Zanca; Martin Burger 001; Björn M. Eskofier (UAI, 2021)

Neural Architecture Search via Bregman Iterations. - Leon Bungert; Tim Roith; Daniel Tenbrinck; Martin Burger 001 (CoRR, 2021)

Fenchel Duality Theory and a Primal-Dual Algorithm on Riemannian Manifolds. - Ronny Bergmann; Roland Herzog; Maurício Silva Louzeiro; Daniel Tenbrinck; José Vidal-Núñez (Found. Comput. Math., 2021)

A Bregman Learning Framework for Sparse Neural Networks. - Leon Bungert; Tim Roith; Daniel Tenbrinck; Martin Burger 001 (J. Mach. Learn. Res., 2022)

Hypergraph p-Laplacians, Scale Spaces, and Information Flow in Networks. - Ariane Fazeny; Daniel Tenbrinck; Martin Burger 001 (SSVM, 2023)

Resolution-Invariant Image Classification Based on Fourier Neural Operators. - Samira Kabri; Tim Roith; Daniel Tenbrinck; Martin Burger 001 (SSVM, 2023)

The Infinity Laplacian Eigenvalue Problem: Reformulation and a Numerical Scheme. - Farid Bozorgnia; Leon Bungert; Daniel Tenbrinck (J. Sci. Comput., 2024)

Hypergraph p-Laplacians and Scale Spaces. - Ariane Fazeny; Daniel Tenbrinck; Kseniia Lukin; Martin Burger 001 (J. Math. Imaging Vis., 2024)

You Are What You Say: Exploiting Linguistic Content for VoicePrivacy Attacks. - Ünal Ege Gaznepoglu; Anna Leschanowsky; Ahmad Aloradi; Prachi Singh; Daniel Tenbrinck; Emanuël A. P. Habets; Nils Peters (INTERSPEECH, 2025)

On MAP estimates and source conditions for drift identification in SDEs. - Daniel Tenbrinck; Nikolas Uesseler; Philipp Wacker; Benedikt Wirth (CoRR, 2025)