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Publications

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Journal Article
C. Schnörr, “Signal and Image Approximation with Level-Set Constraints”, Computing, vol. 81, pp. 137-160, 2007.PDF icon Technical Report (506.8 KB)
T. Milbich, Roth, K., Brattoli, B., and Ommer, B., “Sharing Matters for Generalization in Deep Metric Learning”, IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2020.
D. Cremers, Kohlberger, T., and Schnörr, C., “Shape Statistics in Kernel Space for Variational Image Segmentation”, Pattern Recognition, vol. 36, pp. 1929–1943, 2003.
D. Cremers, Kohlberger, T., and Schnörr, C., “Shape Statistics in Kernel Space for Variational Image Segmentation”, Pattern Recognition, vol. 36, p. 1929--1943, 2003.PDF icon Technical Report (1.67 MB)
E. - M. Didden, Thorarinsdottir, T. L., Lenkoski, A., and Schnörr, C., “Shape from Texture using Locally Scaled Point Processes”, Image Anal. Stereol., vol. 34, pp. 161-170, 2015.
B. Maco, Cantoni, M., Holtmaat, A., Kreshuk, A., Hamprecht, F. A., and Knott, G. W., “Semiautomated Correlative 3D Electron Microscopy of In Vivo Imaged Axons and Dendrites”, Nature Protocols, vol. 9, pp. 1354-1366, 2014.PDF icon Technical Report (2.01 MB)
M. Hullin, Klein, R., Schultz, T., Yao, A., Li, W., Hosseini Jafari, O., and Rother, C., “Semantic-Aware Image Smoothing”, Vision, Modeling, and Visualization, 2017.
M. Zisler, Zern, A., Petra, S., and Schnörr, C., “Self-Assignment Flows for Unsupervised Data Labeling on Graphs”, preprint: arXiv, 2019.
M. Staudacher, Hamprecht, F. A., and Görlitz, L., “Self Adjustment of Scanning Electron Microscopes / Selbstadaptivität von Rasterelektronenmikroskopen”, Patent, Patent Number WO2009062781A1, 2009.PDF icon Technical Report (46.64 KB)
B. Jähne, “Sehen, was man sonst nicht sieht”, Ruperto Carola, p. 32--36, 1998.
B. Ommer, Mader, T., and Buhmann, J. M., “Seeing the Objects Behind the Dots: Recognition in Videos from a Moving Camera”, International Journal of Computer Vision, vol. 83, p. 57--71, 2009.PDF icon Technical Report (9.61 MB)
J. Berger, Lenzen, F., Becker, F., Neufeld, A., and Schnörr, C., “{Second-Order Recursive Filtering on the Rigid-Motion Lie Group SE(3) Based on Nonlinear Observations”, J. Math. Imag. Vision, vol. 58, pp. 102–129, 2017.
B. Andres, Köthe, U., Kröger, T., and Hamprecht, F. A., “Runtime-Flexible Multi-dimensional Views and Arrays for C++98 and C++0x”, ArXiv e-prints, 2010.PDF icon Technical Report (415.54 KB)
X. He, Wang, H., Zhang, F., Wang, G., and Zhou, K., “Robust Simulation of Small-Scale Thin Features in SPH-based Free Surface Flows”, Life.Kunzhou.Net, vol. 1, pp. 1–8, 2014.
T. König, Menze, B. H., Kirchner, M., Monigatti, F., Parker, K. C., Patterson, T., Steen, J. J., Hamprecht, F. A., and Steen, H., “Robust Prediction of the MASCOT Score for an Improved Quality Assessment in Mass Spectrometric Proteomics”, Journal of Proteome Research, vol. 7, pp. 3708-3717, 2008.PDF icon Technical Report (1.16 MB)
D. Breitenreicher and Schnörr, C., “Robust 3D object registration without explicit correspondence using geometric integration”, Machine Vision and Applications, vol. 21, pp. 601-611, 2010.
D. Breitenreicher and Schnörr, C., “Robust 3D object registration without explicit correspondence using geometric integration”, Machine Vision and Applications, vol. 21, pp. 601-611, 2010.PDF icon Technical Report (1.65 MB)
J. M. Álvarez, Gevers, T., Diego, F., and López, A. M., “Road Geometry Classification by Adaptive Shape Models”, IEEE Transactions on Intelligent Transportation Systems (ITS), vol. 99, pp. 1-10, 2012.
M. Wenig, Kuhl, S., Beirle, S., Bucsela, E., Jähne, B., Platt, U., Gleason, J., and Wagner, T., “Retrieval and analysis of stratospheric NO$_2$ from the Global Ozone Monitoring Experiment”, J. Geophys. Res., vol. 109, p. D04315, 1--11, 2004.
A. Bhowmik, Gumhold, S., Rother, C., and Brachmann, E., “Reinforced Feature Points: Optimizing Feature Detection and Description for a High-Level Task”, 2019.
J. Lellmann and Schnörr, C., “Regularizers for Vector-Valued Data and Labeling Problems in Image Processing”, Control Systems and Computers, vol. 2, pp. 43–54, 2011.
S. Lang and Ommer, B., “Reconstructing Histories: Analyzing Exhibition Photographs with Computational Methods”, Arts, Computational Aesthetics, vol. 7, 64, no. 64, 2018.PDF icon arts-07-00064.pdf (4.6 MB)
M. Schmidt, Jehle, M., and Jähne, B., “Range flow estimation based on photonic mixing device data”, Int. J. Intelligent Systems Technologies and Applications, vol. 5, p. 380--392, 2008.
H. Spies, Jähne, B., and Barron, J. L., “Range flow estimation.”, Computer Vision and Image Understanding, vol. 85, p. 209--231, 2002.
C. Leue, Wenig, M., Jähne, B., and Platt, U., “Quantitative observation of biomass-burning plumes from GOME”, ESA Publications EOQ, vol. 58, p. 33--35, 1998.
D. Schmund, Stitt, M., Jähne, B., and Schurr, U., “Quantitative analysis of the local rates of growth of dicot leaves at a high temporal and spatial resolution, using image sequence analysis”, Plant Journal, vol. 16, p. 505--514, 1998.
C. Leue, Wenig, M., Wagner, T., Klimm, O., Platt, U., and Jähne, B., “Quantitative analysis of NO$_x$ emissions from Global Ozone Monitoring Experiment satellite image sequences”, J. Geophys. Res., vol. 106, p. 5493--5505, 2001.
X. Lou, Fiaschi, L., Köthe, U., and Hamprecht, F. A., “Quality Classification of Microscopic Imagery with Weakly Supervised Learning”, MICCAI-MLMI. Proceedings, pp. 176-183, 2012.PDF icon Technical Report (4.15 MB)
S. Sieg, Stutz, B., Schmidt, T., Hamprecht, F. A., and Maier, W. F., “A QCAR-approach to materials modelling”, Journal of Molecular Modeling, vol. 12, pp. 611-619, 2006.PDF icon Technical Report (343.11 KB)
L. Distributions, “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, pp. 9–11, 2014.
L. Görlitz, Menze, B. H., Kelm, B. Michael, and Hamprecht, F. A., “Processing Spectral Data”, Surface and Interface Analysis, vol. 41, pp. 636-644, 2009.PDF icon Technical Report (4.17 MB)
F. Rathke, Schmidt, S., and Schnörr, C., “Probabilistic Intra-Retinal Layer Segmentation in 3-D OCT Images Using Global Shape Regularization”, Medical Image Analysis, vol. 18, pp. 781-794, 2014.PDF icon Technical Report (4.07 MB)
F. Rathke, Schmidt, S., and Schnörr, C., “Probabilistic Intra-Retinal Layer Segmentation in 3-D OCT Images Using Global Shape Regularization”, Medical Image Analysis, vol. 18, pp. 781-794, 2014.
F. Rathke, Schmidt, S., and Schnörr, C., “Probabilistic Intra-Retinal Layer Segmentation in 3-D OCT Images Using Global Shape Regularization”, Med. Image Anal., vol. 18, pp. 781–794, 2014.
V. Kolmogorov, Criminisi, A., Blake, A., Cross, G., and Rother, C., “Probabilistic fusion of stereo with color and contrast for bilayer segmentation”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, pp. 1480–1492, 2006.

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