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Journal Article
B. H. Menze, Lichy, M. P., Bachert, P., Kelm, B. Michael, Schlemmer, H. P., and Hamprecht, F. A., “Optimal Classification of Long Echo Time in vivo Magnetic Resonance Spectra in the Detection of Recurrent Brain Tumor”, NMR in Biomedicine, vol. 19, pp. 599-609, 2006.PDF icon Technical Report (289.77 KB)
B. Jähne, “Optik, Photonik und Bildverarbeitung --- eine spannende Reise”, Optik & Photonik, vol. 7, p. 2--3, 2012.
P. Ruhnau and Schnörr, C., “Optical Stokes Flow Estimation: An Imaging-Based Control Approach”, Exp. in Fluids, vol. 42, pp. 61–78, 2007.
P. Ruhnau and Schnörr, C., “Optical Stokes Flow Estimation: An Imaging-Based Control Approach”, Exp.~in Fluids, vol. 42, p. 61--78, 2007.PDF icon Technical Report (1.54 MB)
F. Friedl, Krah, N., and Jähne, B., “Optical sensing of oxygen using a modified Stern-Volmer equation for high laser irradiance”, Sensors and Actuators B: Chemical, vol. 206, p. 336--342, 2015.
F. Friedl, Krah, N., and Jähne, B., “Optical sensing of oxygen using a modified Stern-Volmer equation for high laser irradiance”, Sensors and Actuators B: Chemical, vol. 206, pp. 336–342, 2015.
C. S. Garbe, Roetmann, K., Beushausen, V., and Jähne, B., “An optical flow MTV based technique for measuring microfluidic flow in the presence of diffusion and Taylor dispersion”, Exp. Fluids, vol. 44, p. 439--450, 2008.
M. Wenig, Jähne, B., and Platt, U., “Operator representation as a new differential optical absorption spectroscopy formalism”, Appl. Optics, vol. 44, pp. 3246-3253, 2005.
B. Andres, Beier, T., and Kappes, J. H., “OpenGM: A C++ Library for Discrete Graphical Models”, ArXiv e-prints, 2012.
E. - A. Horvát, Hanselmann, M., Hamprecht, F. A., and Zweig, K. A., “One plus one makes three (for social networks)”, PLoS ONE, vol. 4,7, 2012.
B. Jähne, “The ocean in the lab: measurements with light and shadow”, Ruperto Carola Forschungsmagazin Heidelberg University, vol. 7, pp. 52–59, 2015.
F. O. Kaster, Merkel, B., Nix, O., and Hamprecht, F. A., “An object-oriented library for systematic training and comparison of classifiers for computer-assisted tumor diagnosis from MRSI measurements”, Computer Science - Research and Development, vol. 26, pp. 65-85, 2011.PDF icon Technical Report (808.16 KB)
V. Ulman, Maška, M., Magnusson, K. E. G., Ronneberger, O., Haubold, C., Harder, N., Matula, P., Matula, P., Svoboda, D., Radojevic, M., Smal, I., Rohr, K., Jaldén, J., Blau, H. M., Dzyubachyk, O., Lelieveldt, B., Xiao, P., Li, Y., Cho, S. - Y., Dufour, A., Olivo-Marin, J. C., Reyes-Aldasoro, C. C., Solis-Lemus, J. A., Bensch, R., Brox, T., Stegmaier, J., Mikut, R., Wolf, S., Hamprecht, F. A., Esteves, T., Quelhas, P., Demirel, Ö., Malström, L., Jug, F., Tomančák, P., Meijering, E., Muñoz-Barrutia, A., Kozubek, M., and Ortiz-de-Solorzano, C., “An Objective Comparison of Cell Tracking Algorithms”, Nature Methods, vol. 14, no. 12, pp. 1141-1152, 2017.PDF icon Technical Report (4.24 MB)
M. Jehle and Jähne, B., “A novel method for three-dimensional three-component analysis of flow close to free water surfaces”, Exp. Fluids, vol. 44, p. 469--480, 2008.
P. Bell, Schlecht, J., and Ommer, B., “Nonverbal Communication in Medieval Illustrations Revisited by Computer Vision and Art History”, Visual Resources Journal, Special Issue on Digital Art History, vol. 29, p. 26--37, 2013.
J. Restle, Hissmann, M., and Hamprecht, F. A., “Nonparametric Smoothing of Height maps using ``Confidence'' values”, Optical Engineering, vol. 43, pp. 866-871, 2004.PDF icon Technical Report (1.07 MB)
C. Schnörr and Sprengel, R., “A Nonlinear Regularization Approach to Early Vision”, Biol. Cybernetics, vol. 72, pp. 141–149, 1994.
M. Zisler, Kappes, J. H., Schnörr, C., Petra, S., and Schnörr, C., “Non-Binary Discrete Tomography by Continuous Non-Convex Optimization”, IEEE Comp. Imaging, vol. 2, pp. 335-347, 2016.
B. Jähne and Schwarzbauer, M., “Noise equalisation and quasi loss-less image data compression – or how many bits needs an image sensor?”, tm – Technisches Messen, vol. 83, pp. 16–24, 2016.
B. Y. Renard, Kirchner, M., Steen, H., Steen, J. A. J., and Hamprecht, F. A., “NITPICK: Peak Identification for Mass Spectrometry Data”, BMC Bioinformatics, vol. 9, p. 355, 2008.PDF icon Technical Report (643.89 KB)
N. Krasowki, Beier, T., Knott, G. W., Köthe, U., Hamprecht, F. A., and Kreshuk, A., “Neuron Segmentation with High-Level Biological Priors”, IEEE Transactions on Medical Imaging, vol. 37, no. 4, 2017.
S. Trittler and Hamprecht, F. A., “Near-Optimum Sampling Design and an Efficient Algorithm for Single Tone Frequency Estimation”, Digital Signal Processing, vol. 19, pp. 628-639, 2009.PDF icon Technical Report (511.23 KB)
B. Goldlücke, Strekalovskiy, E., and Cremers, D., “The Natural Vectorial Total Variation which Arises from Geometric Measure Theory”, SIAM Journal on Imaging Sciences, vol. 5, pp. 537-563, 2012.
B. Goldlücke, Strekalovskiy, E., and Cremers, D., “The natural vectorial total variation which arises from geometric measure theory”, SIAM Journal on Imaging Sciences, 2012.
M. Heiler and Schnörr, C., “Natural Image Statistics for Natural Image Segmentation”, Int. J. Comp. Vision, vol. 63, pp. 5–19, 2005.
S. Wolf, Bailoni, A., Pape, C., Rahaman, N., Kreshuk, A., Köthe, U., and Hamprecht, F. A., “The Mutex Watershed and its Objective: Efficient, Parameter-Free Graph Partitioning”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 43, pp. 3724-3738, 2020.PDF icon Technical Report (2.58 MB)
B. H. Menze, Petrich, W., and Hamprecht, F. A., “Multivariate feature selection and hierarchical classification for infrared spectroscopy: serum-based detection of bovine spongiform encephalopathy”, Analytical and Bioanalytical Chemistry, vol. 387, pp. 1801-1807, 2007.PDF icon Technical Report (283.47 KB)
M. Kandemir, Klami, A., Gonen, M., Vetek, A., and Kaski, S., “Multi-task and multi-view learning of user state”, Neurocomputing, vol. 139, pp. 97-106, 2014.
D. Cremers, Sochen, N., and Schnörr, C., “Multiphase Dynamic Labeling for Variational Recognition-Driven Image Segmentation”, ijcv, vol. 66, pp. 67-81, 2006.
B. H. Menze and Hamprecht, F. A., “Multimodal Medical Image Analysis: from Visualization to Disease Modeling”, Zeitschrift für Med. Physik, vol. 1, pp. 1-2, 2010.PDF icon Technical Report (481.58 KB)
A. Bruhn, Weickert, J., Kohlberger, T., and Schnörr, C., “A Multigrid Platform for Real-Time Motion Computation with Discontinuity-Preserving Variational Methods”, Int.~J.~Computer Vision, vol. 70, pp. 257-277, 2006.PDF icon Technical Report (447.65 KB)
A. Bruhn, Weickert, J., Kohlberger, T., and Schnörr, C., “A Multigrid Platform for Real-Time Motion Computation with Discontinuity-Preserving Variational Methods”, Int. J. Computer Vision, vol. 70, pp. 257-277, 2006.
B. Jähne, Brocke, M., Eisele, H., Hader, S., Hamprecht, F. A., Happold, W., Raisch, F., and Restle, J., “Multidimensionale Bildverarbeitung in der Produktion”, QZ, vol. 47, p. 1154--1159, 2002.
J. Hendrik Kappes, Swoboda, P., Savchynskyy, B., Hazan, T., and Schnörr, C., “Multicuts and Perturb & MAP for Probabilistic Graph Clustering”, Journal of Mathematical Imaging and Vision, vol. 56, pp. 221–237, 2016.
J. H. Kappes, Swoboda, P., Savchynskyy, B., Hazan, T., and Schnörr, C., “Multicuts and Perturb & MAP for Probabilistic Graph Clustering”, J. Math. Imag. Vision, vol. 56, pp. 221–237, 2016.

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