Publications

Joachim Weickert, Sven Grewenig, Christopher Schroers, und Andrés Bruhn. Cyclic Schemes for PDE-Based Image Analysis. International journal of computer vision, (118)3:275-299, Springer, 2016. [PUMA: ubs_10005 ubs_20008 ubs_30086 ubs_40127 unibibliografie wos]

Hui Men, Hanhe Lin, Vlad Hosu, Daniel Maurer, Andrés Bruhn, und Dietmar Saupe. Visual Quality Assessment for Motion Compensated Frame Interpolation. 2019 Eleventh International Conference on Quality of Multimedia Experience (QoMEX), IEEE, Piscataway, NJ, 2019. [PUMA: ubs_10005 ubs_20008 ubs_30086 ubs_40127 unibibliografie wos]

Andrés Bruhn, Atsushi Imiya, Ales Leonardis, und Tomás Pajdla. Vision for autonomous vehicles and probes. Dagstuhl reports, 5, 11:36-61, Schloss Dagstuhl, Wadern, 2015. [PUMA: hp ubs_10005 ubs_20008 ubs_30086 ubs_40127 unibibliografie]

Daniel Maurer, Yong Chul Ju, Michael Breuß, und Andrés Bruhn. An efficient linearisation approach for variational perspective shape from shading. In Juergen Gall, Peter V. Gehler, und Bastian Leibe (Hrsg.), Pattern recognition, 9358:249-261, Springer, Cham, 2015. [PUMA: hp ubs_10005 ubs_20008 ubs_30086 ubs_40127 unibibliografie]

Yong Chul Ju, Andrés Bruhn, und Michael Breuß. Variational perspective shape from shading. In Jean-François Aujol, Mila Nikolova, und Nicolas Papadakis (Hrsg.), Scale space and variational methods in computer vision : 5th International Conference, SSVM 2015, 9087:538-550, Springer, Cham, 2015. [PUMA: hp ubs_10005 ubs_20008 ubs_30086 ubs_40127 unibibliografie]

Soon-Seo Park, Youngjae Min, Jung-Su Ha, Doo-Hyun Cho, und Han-Lim Choi. A Distributed ADMM Approach to Non-Myopic Path Planning for Multi-Target Tracking. IEEE Access, (7):163589-163603, IEEE, 2019. [PUMA: oa ubs_10005 ubs_20008 ubs_30086 ubs_40127 unibibliografie wos]

Hui Men, Vlad Hosu, Hanhe Lin, Andrés Bruhn, und Dietmar Saupe. Visual Quality Assessment for Interpolated Slow-Motion Videos Based on a Novel Database. 2020 Twelfth International Conference on Quality of Multimedia Experience (QoMEX), 1-6, IEEE, 2020. [PUMA: ubs_10005 ubs_20008 ubs_30086 ubs_40127 unibibliografie wos]

Jenny Schmalfuß, Erik Scheurer, Heng Zhao, Nikolaos Karantzas, Andrés Bruhn, und Demetrio Labate. Handwriting Inpainting Dataset. 2022. [PUMA: darus mult ubs_10005 ubs_20008 ubs_30082 ubs_30086 ubs_40116 ubs_40127 unibibliografie]

Lukas Mehl, Cedric Beschle, Andrea Barth, und Andrés Bruhn. Replication Data for: An Anisotropic Selection Scheme for Variational Optical Flow Methods with Order-Adaptive Regularisation. 2022. [PUMA: darus mult ubs_10005 ubs_10008 ubs_10018 ubs_20008 ubs_20013 ubs_20024 ubs_30086 ubs_30123 ubs_30200 ubs_40127 ubs_40192 unibibliografie]

Tim Krake, Andrés Bruhn, Bernhard Eberhardt, und Daniel Weiskopf. Efficient and Robust Background Modeling with Dynamic Mode Decomposition. Journal of mathematical imaging and vision, (64)4:364-378, Springer, 2022. [PUMA: mult oa ubs_10005 ubs_10017 ubs_20008 ubs_30086 ubs_40127 ubs_40132 unibibliografie wos]

Markus Philipp, Neal Bacher, Stefan Saur, Franziska Mathis-Ullrich, und Andrés Bruhn. From Chairs To Brains : Customizing Optical Flow For Surgical Activity Localization. 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI), IEEE, Piscataway, 2022. [PUMA: ubs_10005 ubs_20008 ubs_30086 ubs_40127 unibibliografie wos]

Jenny Schmalfuß, Erik Scheurer, Heng Zhao, Nikolaos Karantzas, Andrés Bruhn, und Demetrio Labate. Blind Image Inpainting with Sparse Directional Filter Dictionaries for Lightweight CNNs. Journal of mathematical imaging and vision, Springer, 2022. [PUMA: mult ubs_10005 ubs_20008 ubs_30082 ubs_30086 ubs_40127 ubs_40427 unibibliografie wos]

Lukas Mehl, Jenny Schmalfuss, Azin Jahedi, Yaroslava Nalivayko, und Andrés Bruhn. Spring: A High-Resolution High-Detail Dataset and Benchmark for Scene Flow, Optical Flow and Stereo. 2023. [PUMA: darus mult ubs_10005 ubs_10018 ubs_20008 ubs_20024 ubs_30086 ubs_30200 ubs_40127 unibibliografie]

Jenny Schmalfuss, Cedric Riethmüller, Mirco Altenbernd, Kilian Weishaupt, und Dominik Göddeke. Partitioned coupling vs. monolithic block-preconditioning approaches for solving Stokes-Darcy systems. In E. Oñate, M. Papadrakakis, und B. Schrefler (Hrsg.), Presentations and Plenary videos to 9th edition of the International Conference on Computational Methods for Coupled Problems in Science and Engineering (COUPLED PROBLEMS 2021), Scipedia, S.L., Barcelona, 2021. [PUMA: mult oa sent ubs_10002 ubs_10005 ubs_10008 ubs_10021 ubs_20002 ubs_20008 ubs_20013 ubs_20019 ubs_30028 ubs_30086 ubs_30123 ubs_30165 ubs_40127 ubs_40195 unibibliografie]

Jenny Schmalfuß, Philipp Scholze, und Andrés Bruhn. A Perturbation-Constrained Adversarial Attack for Evaluating the Robustness of Optical Flow. In Shai Avidan, Gabriel Brostow, Moustapha Cissé, Giovanni Maria Farinella, und Tal Hassner (Hrsg.), Computer Vision – ECCV 2022, (22)13682:183-200, Springer, Cham, 2022. [PUMA: ubs_10005 ubs_20008 ubs_30086 ubs_40127 unibibliografie wos]

Jenny Schmalfuß, Lukas Mehl, und Andrés Bruhn. Distracting Downpour - Adversarial Weather Attacks for Motion Estimation (Replication Data). 2023. [PUMA: darus ubs_10005 ubs_20008 ubs_30086 ubs_40127 unibibliografie]

Azin Jahedi, Lukas Mehl, Marc Rivinius, und Andrés Bruhn. Multi-Scale Raft : Combining Hierarchical Concepts for Learning-Based Optical Flow Estimation. 2022 IEEE International Conference on Image Processing (ICIP), 1236-1240, IEEE, 2023. [PUMA: mult ubs_10005 ubs_20007 ubs_20008 ubs_30086 ubs_30180 ubs_40127 ubs_40301 unibibliografie wos]

Lukas Mehl, Azin Jahedi, Jenny Schmalfuß, und Andrés Bruhn. M-FUSE : Multi-frame Fusion for Scene Flow Estimation. 2023 IEEE Winter Conference on Applications of Computer Vision, 2019-2028, IEEE, Piscataway, NJ, 2023. [PUMA: ubs_10005 ubs_20008 ubs_30086 ubs_40127 unibibliografie wos]

Lukas Mehl, Jenny Schmalfuß, Azin Jahedi, Yaroslava Nalivayko, und Andrés Bruhn. Spring : A High-Resolution High-Detail Dataset and Benchmark for Scene Flow, Optical Flow and Stereo. 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 4981-4991, IEEE, 2023. [PUMA: ubs_10005 ubs_20008 ubs_30086 ubs_40127 unibibliografie wos]

Azin Jahedi, Maximilian Luz, Marc Rivinius, Lukas Mehl, und Andrés Bruhn. MS-RAFT plus: High Resolution Multi-Scale RAFT. International journal of computer vision, Springer, 2023. [PUMA: f2023 hybrid oa oafonds transform ubs_10005 ubs_20007 ubs_20008 ubs_30086 ubs_30180 ubs_40127 ubs_40301 unibibliografie wos]