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Schnörr, (2007). Signal and Image Approximation with Level-Set Constraints. Computing. 81 137-160

Technical Report (506.8 KB)

Milbich, T, Roth, K, Brattoli, B and Ommer, B (2020). Sharing Matters for Generalization in Deep Metric Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI). https://arxiv.org/abs/2004.05582

Cremers, D, Kohlberger, T and Schnörr, C (2003). Shape Statistics in Kernel Space for Variational Image Segmentation. Pattern Recognition. 36 1929–1943

Cremers, D, Kohlberger, T and Schnörr, C (2003). Shape Statistics in Kernel Space for Variational Image Segmentation. Pattern Recognition. 36 1929--1943

Technical Report (1.67 MB)

Didden, E - M, Thorarinsdottir, T L, Lenkoski, A and Schnörr, C (2015). Shape from Texture using Locally Scaled Point Processes. Image Anal. Stereol. 34 161-170

Maco, B, Cantoni, M, Holtmaat, A, Kreshuk, A, Hamprecht, F A and Knott, G W (2014). Semiautomated Correlative 3D Electron Microscopy of In Vivo Imaged Axons and Dendrites. Nature Protocols. 9 1354-1366

Technical Report (2.01 MB)

Hullin, M, Klein, R, Schultz, T, Yao, A, Li, W, Hosseini Jafari, O and Rother, C (2017). Semantic-Aware Image Smoothing. Vision, Modeling, and Visualization. https://hci.iwr.uni-heidelberg.de/vislearn/wp-content/uploads/2014/08/paper1024_CRC.pdf

Zisler, M, Zern, A, Petra, S and Schnörr, C (2019). Self-Assignment Flows for Unsupervised Data Labeling on Graphs. preprint: arXiv. https://arxiv.org/abs/1911.03472

Staudacher, M, Hamprecht, F A and Görlitz, L (2009). Self Adjustment of Scanning Electron Microscopes / Selbstadaptivität von Rasterelektronenmikroskopen. Patent, Patent Number WO2009062781A1

Technical Report (46.64 KB)

Jähne, (1998). Sehen, was man sonst nicht sieht. Ruperto Carola. 32--36. http://www.uni-heidelberg.de/uni/presse/RuCa3_98/jaehne.htm

Ommer, B, Mader, T and Buhmann, J M (2009). Seeing the Objects Behind the Dots: Recognition in Videos from a Moving Camera. International Journal of Computer Vision. Springer. 83 57--71

Technical Report (9.61 MB)

Berger, J, Lenzen, F, Becker, F, Neufeld, A and Schnörr, C (2017). {Second-Order Recursive Filtering on the Rigid-Motion Lie Group SE(3) Based on Nonlinear Observations. J. Math. Imag. Vision. 58 102–129

Andres, B, Köthe, U, Kröger, T and Hamprecht, F A (2010). Runtime-Flexible Multi-dimensional Views and Arrays for C++98 and C++0x. ArXiv e-prints. http://arxiv.org/abs/1008.2909v1

Technical Report (415.54 KB)

He, X, Wang, H, Zhang, F, Wang, G and Zhou, K (2014). Robust Simulation of Small-Scale Thin Features in SPH-based Free Surface Flows. Life.Kunzhou.Net. 1 1–8. http://doi.acm.org/10.1145/XXXXXXX.YYYYYYY http://life.kunzhou.net/2013/SPHsurfacetension.pdf

König, T, Menze, B H, Kirchner, M, Monigatti, F, Parker, K C, Patterson, T, Steen, J J, Hamprecht, F A and Steen, H (2008). Robust Prediction of the MASCOT Score for an Improved Quality Assessment in Mass Spectrometric Proteomics. Journal of Proteome Research. 7 3708-3717

Technical Report (1.16 MB)

Breitenreicher, D and Schnörr, C (2010). Robust 3D object registration without explicit correspondence using geometric integration. Machine Vision and Applications. 21 601-611. http://www.springerlink.com/content/g20710062l014241/

Breitenreicher, D and Schnörr, C (2010). Robust 3D object registration without explicit correspondence using geometric integration. Machine Vision and Applications. 21 601-611. http://www.springerlink.com/content/g20710062l014241/

Technical Report (1.65 MB)

Álvarez, J M, Gevers, T, Diego, F and López, A M (2012). Road Geometry Classification by Adaptive Shape Models. IEEE Transactions on Intelligent Transportation Systems (ITS). 99 1-10

Wenig, M, Kuhl, S, Beirle, S, Bucsela, E, Jähne, B, Platt, U, Gleason, J and Wagner, T (2004). Retrieval and analysis of stratospheric NO$_2$ from the Global Ozone Monitoring Experiment. J. Geophys. Res. 109 D04315, 1--11

Bhowmik, A, Gumhold, S, Rother, C and Brachmann, E (2019). Reinforced Feature Points: Optimizing Feature Detection and Description for a High-Level Task. http://arxiv.org/abs/1912.00623

Lellmann, J and Schnörr, C (2011). Regularizers for Vector-Valued Data and Labeling Problems in Image Processing. Control Systems and Computers. 2 43–54

Lang, S and Ommer, B (2018). Reconstructing Histories: Analyzing Exhibition Photographs with Computational Methods. Arts, Computational Aesthetics. 7, 64

arts-07-00064.pdf (4.6 MB)

Schmidt, M, Jehle, M and Jähne, B (2008). Range flow estimation based on photonic mixing device data. Int. J. Intelligent Systems Technologies and Applications. 5 380--392

Spies, H, Jähne, B and Barron, J L (2002). Range flow estimation.. Computer Vision and Image Understanding. 85 209--231

Leue, C, Wenig, M, Jähne, B and Platt, U (1998). Quantitative observation of biomass-burning plumes from GOME. ESA Publications EOQ. 58 33--35. http://esapub.esrin.esa.it/eoq/eoq58

Schmund, D, Stitt, M, Jähne, B and Schurr, U (1998). Quantitative analysis of the local rates of growth of dicot leaves at a high temporal and spatial resolution, using image sequence analysis. Plant Journal. 16 505--514

Leue, C, Wenig, M, Wagner, T, Klimm, O, Platt, U and Jähne, B (2001). Quantitative analysis of NO$_x$ emissions from Global Ozone Monitoring Experiment satellite image sequences. J. Geophys. Res. 106 5493--5505

Lou, X, Fiaschi, L, Köthe, U and Hamprecht, F A (2012). Quality Classification of Microscopic Imagery with Weakly Supervised Learning. MICCAI-MLMI. Proceedings. 176-183

Technical Report (4.15 MB)

Sieg, S, Stutz, B, Schmidt, T, Hamprecht, F A and Maier, W F (2006). A QCAR-approach to materials modelling. Journal of Molecular Modeling. 12 611-619

Technical Report (343.11 KB)

Distributions, L (2014). Proof of Lemma 2 Proof of Lemma 3 Proof of Theorem 4 Proof of Lemma 10. Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics. 9–11

Görlitz, L, Menze, B H, Kelm, B Michael and Hamprecht, F A (2009). Processing Spectral Data. Surface and Interface Analysis. 41 636-644

Technical Report (4.17 MB)

Rathke, F, Schmidt, S and Schnörr, C (2014). Probabilistic Intra-Retinal Layer Segmentation in 3-D OCT Images Using Global Shape Regularization. Medical Image Analysis. 18 781-794

Technical Report (4.07 MB)

Rathke, F, Schmidt, S and Schnörr, C (2014). Probabilistic Intra-Retinal Layer Segmentation in 3-D OCT Images Using Global Shape Regularization. Medical Image Analysis. 18 781-794

Rathke, F, Schmidt, S and Schnörr, C (2014). Probabilistic Intra-Retinal Layer Segmentation in 3-D OCT Images Using Global Shape Regularization. Med. Image Anal. 18 781–794

Kolmogorov, V, Criminisi, A, Blake, A, Cross, G and Rother, C (2006). Probabilistic fusion of stereo with color and contrast for bilayer segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence. 28 1480–1492. http://research.microsoft.com/vision/cambridge

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