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Department Mathematik

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  1. Startseite
  2. Angewandte Mathematik (Modellierung und Numerik)
  3. Mitarbeitende
  4. Dr. Daniel Tenbrinck

Dr. Daniel Tenbrinck

Bereichsnavigation: Angewandte Mathematik (Modellierung und Numerik)
  • Arbeitsgruppen
  • Forschung
  • Lehre
  • Mitarbeitende
    • Alice Lieu, PhD
    • Alicja Kerschbaum
    • Apratim Bhattacharya
    • Astrid Bigott
    • Cornelia Weber
    • Doris Schneider
    • Dr. Alexander Prechtel
    • Dr. Antonio Esposito
    • Dr. Daniel Tenbrinck
    • Dr. habil. Nicolae Suciu
    • Dr. habil. Raphael Schulz
    • Dr. Lea Föcke
    • Dr. Nadja Ray
    • Dr. Philipp Wacker
    • Dr. Stefan Metzger (AG Grün)
    • Jonas Knoch
    • Lorenz Klein (AG Grün)
    • Lorenz Kuger
    • Oliver Sieber (AG Grün)
    • Patrick Weiß (AG Grün)
    • PD Dr. Maria Neuss-Radu
    • Prof. Dr. Günther Grün
    • Prof. Dr. Manuel Friedrich
    • Prof. Dr. Martin Burger
    • Prof. Dr. Peter Knabner
    • Prof. Dr. Serge Kräutle
    • Prof. Dr. Wilhelm Merz
    • Sebastian Czop
    • Simon Zech
    • Stephan Gärttner
    • Tim Roith
    • Tobias Elbinger
  • Veranstaltungen

Dr. Daniel Tenbrinck

Prof. Dr. Daniel Tenbrinck, Akad. Rat

Dr. Daniel Tenbrinck
Akademischer Rat

Department of Data Science (DDS)
Professur im Themenfeld Data Science

Raum: Raum 04.376
Cauerstr. 11
91058 Erlangen
  • Telefon: +49 9131 85-67233
  • Faxnummer: +49 9131 85-67225
  • E-Mail: daniel.tenbrinck@fau.de

Lebenslauf

  • Grundwehrdienst bei der Luftwaffe, Budel (Niederlande), 2004-2005.
  • Studium in Informatik mit Nebenfach Mathematik an der Westfälischen Wilhelms-Universität (WWU) Münster, 2005-2009, Diplom 2009.
  • Doktor der Naturwissenschaften in Informatik an der WWU Münster, 2013.
  • Wissenschaftlicher Mitarbeiter und Postdoc im SFB 656 „Molekulare Bildgebung“ an der WWU Münster, 2009-2013.
  • Postdoc an der École Nationale Ecole Nationale Supérieure d’Ingénieurs de Caen (ENSICAEN), Frankreich, 2014.
  • Postdoc am Institut für Angewandte Mathematik, Prof. Burger, WWU Münster, 2014-2018.
  • Postdoc am Lehrstuhl für Angewandte Mathematik, Prof. Burger, FAU Erlangen-Nürnberg, 2018-2019.
  • Akademischer Rat auf Zeit am Lehrstuhl für Angewandte Mathematik, Prof. Burger, FAU Erlangen-Nürnberg, seit 2019.

Publikationen

  • Bozorgnia F., Bungert L., Tenbrinck D.:
    The Infinity Laplacian Eigenvalue Problem: Reformulation and a Numerical Scheme
    In: Journal of Scientific Computing 98 (2024), Art.Nr.: 40
    ISSN: 0885-7474
    DOI: 10.1007/s10915-023-02425-w
    URL: https://link.springer.com/article/10.1007/s10915-023-02425-w
    BibTeX: Download
  • Fazeny A., Tenbrinck D., Lukin K., Burger M.:
    Hypergraph p-Laplacians and Scale Spaces
    In: Journal of Mathematical Imaging and Vision (2024)
    ISSN: 0924-9907
    DOI: 10.1007/s10851-024-01183-0
    URL: https://link.springer.com/article/10.1007/s10851-024-01183-0
    BibTeX: Download

  • Fazeny A., Tenbrinck D., Burger M.:
    Hypergraph p-Laplacians, Scale Spaces, and Information Flow in Networks
    International Conference on Scale Space and Variational Methods in Computer Vision (Santa Margherita di Pula, 21-05-2023 - 25-05-2023)
    In: SSVM 2023: Scale Space and Variational Methods in Computer Vision 2023
    DOI: 10.1007/978-3-031-31975-4_52
    BibTeX: Download
  • Kabri S., Roith T., Tenbrinck D., Burger M.:
    Resolution-Invariant Image Classification Based on Fourier Neural Operators
    International Conference on Scale Space and Variational Methods in Computer Vision (Santa Margherita di Pula, 23-05-2023 - 25-05-2023)
    In: Scale Space and Variational Methods in Computer Vision 2023
    DOI: 10.1007/978-3-031-31975-4_18
    BibTeX: Download

  • Bungert L., Roith T., Tenbrinck D., Burger M.:
    A Bregman Learning Framework for Sparse Neural Networks
    In: Journal of Machine Learning Research (2022)
    ISSN: 1532-4435
    Open Access: https://www.jmlr.org/papers/v23/21-0545.html
    BibTeX: Download

  • Bergmann R., Herzog R., Silva Louzeiro M., Tenbrinck D., Vidal-Núñez J.:
    Fenchel Duality Theory and a Primal-Dual Algorithm on Riemannian Manifolds
    In: Foundations of Computational Mathematics (2021)
    ISSN: 1615-3375
    DOI: 10.1007/s10208-020-09486-5
    BibTeX: Download
  • Bungert L., Raab R., Roith T., Schwinn L., Tenbrinck D.:
    CLIP: Cheap Lipschitz Training of Neural Networks
    International Conference on Scale Space and Variational Methods in Computer Vision
    In: Abderrahim Elmoataz, Jalal Fadili, Yvain Quéau, Julien Rabin, Loïc Simon (Hrsg.): SSVM 2021: Scale Space and Variational Methods in Computer Vision, Cham: 2021
    DOI: 10.1007/978-3-030-75549-2_25
    URL: https://arxiv.org/abs/2103.12531
    BibTeX: Download
  • Schwinn L., Nguyen A., Raab R., Bungert L., Tenbrinck D., Zanca D., Burger M., Eskofier B.:
    Identifying untrustworthy predictions in neural networks by geometric gradient analysis
    Conference on Uncertainty in Artificial Intelligence (UAI) (Online, 27-07-2021 - 30-07-2021)
    URL: https://arxiv.org/abs/2102.12196
    BibTeX: Download
  • Schwinn L., Nguyen A., Raab R., Zanca D., Eskofier B., Tenbrinck D., Burger M.:
    Dynamically Sampled Nonlocal Gradients for Stronger Adversarial Attacks
    International Joint Conference on Neural Networks (IJCNN) (Online, 18-07-2021 - 22-07-2021)
    DOI: 10.1109/ijcnn52387.2021.9534190
    BibTeX: Download

  • Gross-Thebing S., Truszkowski L., Tenbrinck D., Sanchez-Iranzo H., Camelo C., Westerich KJ., Singh A., Maier P., Prengel J., Lange P., Huewel J., Gaede F., Sasse R., Vos BE., Betz T., Matis M., Prevedel R., Luschnig S., Diz-Munoz A., Burger M., Raz E.:
    Using migrating cells as probes to illuminate features in live embryonic tissues
    In: Science Advances 6 (2020)
    ISSN: 2375-2548
    DOI: 10.1126/sciadv.abc5546
    BibTeX: Download

  • Bungert L., Burger M., Tenbrinck D.:
    Computing Nonlinear Eigenfunctions via Gradient Flow Extinction
    SSVM 2019 (Hofgeismar, 30-06-2019 - 04-07-2019)
    DOI: 10.1007/978-3-030-22368-7_23
    URL: https://arxiv.org/abs/1902.10414
    BibTeX: Download

  • Bergmann R., Tenbrinck D.:
    A Graph Framework for Manifold-valued Data
    In: Siam Journal on Imaging Sciences 11 (2018)
    ISSN: 1936-4954
    DOI: 10.1137/17M1118567
    BibTeX: Download

  • Bergmann R., Tenbrinck D.:
    Nonlocal Inpainting of Manifold-Valued Data on Finite Weighted Graphs
    International Conference on Geometric Science of Information (Mines ParisTech, Paris, 07-11-2017 - 09-11-2017)
    URL: https://arxiv.org/abs/1704.06424
    BibTeX: Download

  • Tenbrinck D., Jiang X.:
    Image segmentation with physical noise models
    In: Ayman El-Baz, Xiaoyi Jiang, Jasjit S. Suri (Hrsg.): Biomedical Image Segmentation Advances and Trends, CRC Press, 2016, S. 461-484
    BibTeX: Download

  • Elmoataz A., Toutain M., Tenbrinck D.:
    On the p-Laplacian and ∞-Laplacian on Graphs with Applications in Image and Data Processing
    In: Siam Journal on Imaging Sciences 8 (2015), S. 2412-2451
    ISSN: 1936-4954
    DOI: 10.1137/15M1022793
    BibTeX: Download
  • Tenbrinck D., Jiang X.:
    Image Segmentation with Arbitrary Noise Models by Solving Minimal Surface Problems
    In: Pattern Recognition 48 (2015), S. 3293-3309
    ISSN: 0031-3203
    DOI: 10.1016/j.patcog.2015.01.006
    BibTeX: Download
  • Tenbrinck D., Lozes F., Elmoataz A.:
    Solving Minimal Surface Problems on Surfaces and Point Clouds
    International Conference on Scale Space and Variational Methods in Computer Vision (Bordeaux)
    BibTeX: Download

  • Burger M., Modersitzki J., Tenbrinck D.:
    Mathematical methods in biomedical imaging
    In: GAMM-Mitteilungen 37 (2014), S. 154-183
    ISSN: 0936-7195
    DOI: 10.1002/gamm.201410008
    BibTeX: Download
  • Law Y., Tenbrinck D., Jiang X., Kuhlen T.:
    Software Phantom with Realistic Speckle Modeling for Validation of Image Analysis Methods in Echocardiography
    SPIE Medical Imaging 2014: Ultrasonic Imaging and Tomography
    BibTeX: Download
  • Suhr S., Tenbrinck D., Burger M., Modersitzki J.:
    Registration of noisy images via maximum a-posteriori estimation
    In: Lecture Notes in Computer Science 8545 LNCS (2014), S. 231-240
    ISSN: 0302-9743
    DOI: 10.1007/978-3-319-08554-8_24
    BibTeX: Download
  • Ungru K., Tenbrinck D., Jiang X., Stypmann J.:
    Automatic Classification of Left Ventricular Wall Segments in Small Animal Ultrasound Imaging
    In: Computer Methods and Programs in Biomedicine 117 (2014), S. 2-12
    ISSN: 0169-2607
    DOI: 10.1016/j.cmpb.2014.06.015
    BibTeX: Download

  • Jiang X., Dawood M., Gigengack F., Risse B., Schmid S., Tenbrinck D., Schäfers KP.:
    Biomedical Imaging: A Computer Vision Perspective
    International Conference on Computer Analysis of Images and Patterns (York, 27-08-2013 - 29-08-2013)
    BibTeX: Download
  • Sawatzky A., Tenbrinck D., Jiang X., Burger M.:
    A Variational Framework for Region-Based Segmentation Incorporating Physical Noise Models
    In: Journal of Mathematical Imaging and Vision 47 (2013), S. 179-209
    ISSN: 0924-9907
    DOI: 10.1007/s10851-013-0419-6
    BibTeX: Download
  • Tenbrinck D.:
    Variational Methods for Medical Ultrasound Imaging (Dissertation, 2013)
    URL: https://www.datascience.nat.fau.eu/files/2023/11/dissertation_tenbrinck.pdf
    BibTeX: Download
  • Tenbrinck D., Jiang X.:
    Discriminant Analysis Based Level Set Segmentation for Ultrasound Imaging
    International Conference on Computer Analysis of Images and Patterns (York, 27-08-2013 - 29-08-2013)
    BibTeX: Download
  • Tenbrinck D., Schmid S., Jiang X., Schaefers K., Stypmann J.:
    Histogram-based Optical Flow for Motion Estimation in Ultrasound Imaging
    In: Journal of Mathematical Imaging and Vision 47 (2013), S. 138-150
    ISSN: 0924-9907
    DOI: 10.1007/s10851-012-0398-z
    BibTeX: Download
  • Tenbrinck D., Ungru K., Jiang X., Stypmann J.:
    Regional Classification of Left Ventricular Wall in Small Animal Ultrasound Imaging
    International Conference on Biomedical Informatics and Technology
    BibTeX: Download

  • Tenbrinck D., Sawatzky A., Jiang X., Burger M., Haffner W., Willems P., Paul M., Stypmann J.:
    Impact of physical noise modeling on image segmentation in echocardiography
    3rd Eurographics Workshop on VisualComputing in Biology and Medicine, EG VCBM 2012 (Norrkoping, swe)
    DOI: 10.2312/VCBM/VCBM12/033-040
    BibTeX: Download

  • Schmid S, Tenbrinck D, Jiang X, Schäfers K, Tiemann K, Stypmann J:
    Histogram-Based Optical Flow for Functional Imaging in Echocardiography
    International Conference on Computer Analysis of Images and Patterns (Sevilla, 29-08-2011 - 31-08-2011)
    BibTeX: Download

Lehrveranstaltungen

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Department Mathematik

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91058 Erlangen
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