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Journal Article

F. A. Hamprecht, Jost, D., Rüttimann, M., Calamai, F., and Kowalski, J. J., “Preliminary results on the prediction of countershock success with fibrillation power”, Resuscitation, vol. 50, pp. 297-299, 2001.

M. Jäger and Hamprecht, F. A., “Principal Component Imagery for the Quality Monitoring of Dynamic Laser Welding Processes”, IEEE Transactions on Industrial Electronics, vol. 56:4, pp. 1307-1313, 2008.

B. Jähne, “Prinzipien und Verfahren zur Aufnahme spektraler Bilddaten - Vereinfachte Bildanalyse”, QZ, vol. 53, p. 45--48, 2008.

S. Weber, Schüle, T., and Schnörr, C., “Prior Learning and Convex-Concave Regularization of Binary Tomography”, Electr. Notes in Discr. Math., vol. 20, pp. 313-327, 2005.

M. Geese, Ruhnau, P., and Jähne, B., “PRNU and DSNU Maximum Likelihood Estimation Using Sensor Statistics”, tm --- Technisches Messen, vol. 80, p. 321--328, 2013.

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.

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.

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”, Medical Image Analysis, vol. 18, pp. 781-794, 2014.

Technical Report (4.07 MB)

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.

Technical Report (4.17 MB)

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.

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.

Technical Report (343.11 KB)

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.

Technical Report (4.15 MB)

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.

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., Jähne, B., and Platt, U., “Quantitative observation of biomass-burning plumes from GOME”, ESA Publications EOQ, vol. 58, p. 33--35, 1998.

H. Spies, Jähne, B., and Barron, J. L., “Range flow estimation.”, Computer Vision and Image Understanding, vol. 85, p. 209--231, 2002.

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.

S. Lang and Ommer, B., “Reconstructing Histories: Analyzing Exhibition Photographs with Computational Methods”, Arts, Computational Aesthetics, vol. 7, 64, no. 64, 2018.

arts-07-00064.pdf (4.6 MB)

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.

A. Bhowmik, Gumhold, S., Rother, C., and Brachmann, E., “Reinforced Feature Points: Optimizing Feature Detection and Description for a High-Level Task”, 2019.

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.

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.

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.

Technical Report (1.65 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.

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.

Technical Report (1.16 MB)

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.

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.

Technical Report (415.54 KB)

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. 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.

Technical Report (9.61 MB)

B. Jähne, “Sehen, was man sonst nicht sieht”, Ruperto Carola, p. 32--36, 1998.

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

Technical Report (46.64 KB)

M. Zisler, Zern, A., Petra, S., and Schnörr, C., “Self-Assignment Flows for Unsupervised Data Labeling on Graphs”, preprint: arXiv, 2019.

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.

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.

Technical Report (2.01 MB)

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