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Author Title Type [ Year(Desc)]
2018
Shekhovtsov, A, Swoboda, P and Savchynskyy, B (2018). Maximum Persistency via Iterative Relaxed Inference in Graphical Models. IEEE Transactions on Pattern Analysis and Machine Intelligence. 40 1668–1682. http://www.icg.tugraz.at/
Kiechle, M, Storath, M, Weinmann, A and Kleinsteuber, M (2018). Model-based learning of local image features for unsupervised texture segmentation. IEEE Transactions on Image Processing. 27 1994-2007
Tourani, S, Shekhovtsov, A, Rother, C and Savchynskyy, B (2018). MPLP++: Fast, Parallel Dual Block-Coordinate Ascent for Dense Graphical Models. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 11208 LNCS 264–281
Beier, T (2018). Multicut Algorithms for Neurite Segmentation. Heidelberg University
Wolf, S, Pape, C, Bailoni, A, Rahaman, N, Kreshuk, A, Köthe, U and Hamprecht, F A (2018). The Mutex Watershed: Efficient, Parameter-Free Image Partitioning. ECCV. Proceedings. Springer. 571-587
Wolf, S, Pape, C, Bailoni, A, Rahaman, N, Kreshuk, A, Köthe, U and Hamprecht, F A (2018). The Mutex Watershed: Efficient, Parameter-Free Image Partitioning. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 11208 LNCS 571–587. http://arxiv.org/abs/1904.12654
Lang, S and Ommer, B (2018). Reconstructing Histories: Analyzing Exhibition Photographs with Computational Methods. Arts, Computational Aesthetics. 7, 64PDF icon arts-07-00064.pdf (4.6 MB)
Lang, S and Ommer, B (2018). Reflecting on How Artworks Are Processed and Analyzed by Computer Vision. European Conference on Computer Vision (ECCV - VISART). Springer
Vianello, A, Ackermann, J, Diebold, M and Jähne, B (2018). Robust Hough transform based 3D reconstruction from circular light fields. Conference on Computer Vision and Pattern Recognition (CVPR)
Kostrykin, L, Schnörr, C and Rohr, K (2018). Segmentation of Cell Nuclei Using Intensity-Based Model Fitting and Sequential Convex Programming. Proc. ISBI
Kawetzki, D (2018). Semantic Segmentation Of Urban Scenes Using Deep Learning. Heidelberg University
Rahaman, N, Arpit, D, Baratin, A, Draxler, F, Lin, M, Hamprecht, F A, Bengio, Y and Courville, A (2018). On the spectral bias of deep neural networks. arXiv preprint arXiv:1806.08734
Sanakoyeu, A, Kotovenko, D, Lang, S and Ommer, B (2018). A Style-Aware Content Loss for Real-time HD Style Transfer. Proceedings of the European Conference on Computer Vision (ECCV) (Oral)
(2018). A Supplementary Material Cereals-Cost-Effective Region-Based Active Learning For Semantic Segmentation
Bredies, K, Holler, M, Storath, M and Weinmann, A (2018). Total Generalized Variation for Manifold-valued Data. SIAM Journal on Imaging Sciences. 11 1785 - 1848
Esser, P, Haux, J, Milbich, T and Ommer, B (2018). Towards Learning a Realistic Rendering of Human Behavior. European Conference on Computer Vision (ECCV - HBUGEN)
Schilling, H, Diebold, M, Rother, C and Jähne, B (2018). Trust your Model: Light Field Depth Estimation with inline Occlusion Handling. CVPR. ProceedingsPDF icon Technical Report (5.46 MB)
Schilling, H, Diebold, M, Rother, C and Jähne, B (2018). Trust your Model: Light Field Depth Estimation with Inline Occlusion Handling. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 4530–4538
Zern, A, Zisler, M, Aström, F, Petra, S and Schnörr, C (2018). Unsupervised Label Learning on Manifolds by Spatially Regularized Geometric Assignment. GCPR. Proceedings. 698-713PDF icon Technical Report (5.23 MB)
Zern, A, Zisler, M, Aström, F, Petra, S and Schnörr, C (2018). Unsupervised Label Learning on Manifolds by Spatially Regularized Geometric Assignment. GCPR
Esser, P, Sutter, E and Ommer, B (2018). A Variational U-Net for Conditional Appearance and Shape Generation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (short Oral). https://compvis.github.io/vunet/
Roth, N (2018). Visualization Of Near-Surface Flow Patterns For Air-Water Gas Transfer. Institut für Umweltphysik, Universität Heidelberg, Germany
Blum, O, Brattoli, B and Ommer, B (2018). X-GAN: Improving Generative Adversarial Networks with ConveX Combinations. German Conference on Pattern Recognition (GCPR) (Oral). Stuttgart, GermanyPDF icon Article (6.65 MB)PDF icon Supplementary material (7.96 MB)PDF icon Oral slides (14.96 MB)
2019
Jähne, (2019). Air-Sea Gas Exchange. Encyclopedia of Ocean Sciences. Academic Press. 6 1–13
Krall, K E, Smith, A W, Takagaki, N and Jähne, B (2019). Air–sea gas exchange at wind speeds up to 85 m/s. Ocean Science. 15 1783-–1799
Schnörr, (2019). Assignment Flows. Variational Methods for Nonlinear Geometric Data and Applications. Springer
Haußmann, M, Gerwinn, S and Kandemir, M (2019). Bayesian Prior Networks with PAC Training. arXiv preprint arXiv:1906.00816
Kruse, J, Ardizzone, L, Rother, C and Köthe, U (2019). Benchmarking Invertible Architectures On Inverse Problems
Kamann, C and Rother, C (2019). Benchmarking the Robustness of Semantic Segmentation Models. http://arxiv.org/abs/1908.05005
Bendinger, A L, Debus, C, Glowa, C, Karger, C P, Peter, J and Storath, M (2019). Bolus arrival time estimation in dynamic contrast-enhanced magnetic resonance imaging of small animals based on spline models, in press. Physics in Medicine and Biology. 64
Kleesiek, J, Morshuis, J Nikolas, Isensee, F, Deike-Hofmann, K, Paech, D, Kickingereder, P, Köthe, U, Rother, C, Forsting, M, Wick, W, Bendszus, M, Schlemmer, H Peter and Radbruch, A (2019). Can Virtual Contrast Enhancement in Brain MRI Replace Gadolinium?: A Feasibility Study. Investigative Radiology. 54 653–660
Mackowiak, R, Lenz, P, Ghori, O, Diego, F, Lange, O and Rother, C (2019). CEREALS - Cost-Effective REgion-based Active Learning for Semantic Segmentation. British Machine Vision Conference 2018, BMVC 2018
Kotovenko, D, Sanakoyeu, A, Lang, S and Ommer, B (2019). Content and Style Disentanglement for Artistic Style Transfer. Proceedings of the Intl. Conf. on Computer Vision (ICCV)
Savarino, F and Schnörr, C (2019). Continuous-Domain Assignment Flows. preprint: arXiv. https://arxiv.org/abs/1910.07287
Lu, G -hung, Tsai, W -ting and Jähne, B (2019). Decomposing infrared images of wind waves for quantitative separation into characteristic flow processes. IEEE Transactions on Geoscience and Remote Sensing. 57 8304–8316

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