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Author Title [ Type(Desc)] Year
Journal Article
B. Jähne, Brocke, M., Eisele, H., Hader, S., Hamprecht, F. A., Happold, W., Raisch, F., and Restle, J., “Für Anspruchsvolle - Multidimensionale Bildverarbeitung in der Produktion”, Qualität und Zuverlässigkeit, vol. 47, pp. 1154-1159, 2002.
V. Lempitsky, Rother, C., Roth, S., and Blake, A., “Fusion moves for markov random field optimization”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, pp. 1392–1405, 2010.
V. Lempitsky, Rother, C., Roth, S., and Blake, A., “Fusion moves for markov random field optimization”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, pp. 1392–1405, 2010.
A. Blake, Criminisi, A., Cross, G., Kolmogorov, V., and Rother, C., “Fusion of stereo, colour and contrast”, Springer Tracts in Advanced Robotics, vol. 28, 2007.
J. Röder, Tolosana-Delgado, R., and Hamprecht, F. A., “Gaussian process classification: singly versus doubly stochastic models, and new computational schemes”, Stochastic Environmental Research & Risk Assessment, vol. 25 (7), pp. 865-879, 2011.PDF icon Technical Report (672.68 KB)
A. Nicola, Petra, S., Popa, C., and Schnörr, C., “A general extending and constraining procedure for linear iterative methods”, Int. J. Comp. Math., 2011.
A. Nicola, Petra, S., Popa, C., and Schnörr, C., “A general extending and constraining procedure for linear iterative methods”, Int.~J.~Comp.~Math., 2011.PDF icon Technical Report (633.79 KB)
J. Schmähling and Hamprecht, F. A., “Generalizing the Abbott-Firestone curve by two new surface descriptors”, Wear, vol. 262, pp. 1360-1371, 2007.PDF icon Technical Report (877.34 KB)
F. A. Hamprecht, Scott, W. R. P., and van Gunsteren, W. F., “Generation of pseudo-native protein structures for threading”, Proteins, vol. 28, pp. 522-529, 1997.
J. C. Rubio, Eigenstetter, A., and Ommer, B., “Generative Regularization with Latent Topics for Discriminative Object Recognition”, Pattern Recognition, vol. 48, p. 3871--3880, 2015.PDF icon Technical Report (5.49 MB)
F. Aström and Schnörr, C., “A Geometric Approach for Color Image Regularization”, Comp. Vision Image Understanding, vol. 165, pp. 43–59, 2017.
A. Zeilmann, Savarino, F., Petra, S., and Schnörr, C., “Geometric Numerical Integration of the Assignment Flow”, Inverse Problems, vol. 36, p. 034004 (33pp), 2020.
A. Zeilmann, Savarino, F., Petra, S., and Schnörr, C., “Geometric Numerical Integration of the Assignment Flow”, Inverse Problems, 2019.
A. Zeilmann, Savarino, F., Petra, S., and Schnörr, C., “Geometric Numerical Integration of the Assignment Flow”, preprint: arXiv, 2018.
B. Savchynskyy and Schmidt, S., “Getting Feasible Variable Estimates From Infeasible Ones: MRF Local Polytope Study”, arXiv:1210.4081, 2012.
O. J. Woodford, “A Global Perspective on MAP Inference for Low-Level Vision Supplementary material to ICCV submission \# 1536”, Optimization, 2009.
S. Wanner, Straehle, C. N., and Goldlücke, B., “Globally Consistent Multi-Label Assignment on the Ray Space of 4D Light Fields”, CVPR 2013. Proceedings, pp. 1011-1018, 2013.
B. Schmitzer and Schnörr, C., “Globally Optimal Joint Image Segmentation and Shape Matching based on Wasserstein Modes”, J. Math. Imag. Vision, vol. 52, pp. 436–458, 2015.
B. Schmitzer and Schnörr, C., “Globally Optimal Joint Image Segmentation and Shape Matching based on Wasserstein Modes”, J.~Math.~Imag.~Vision, vol. 52, p. 436--458, 2015.PDF icon Technical Report (1.97 MB)
L. Kostrykin, Schnörr, C., and Rohr, K., “Globally Optimal Segmentation of Cell Nuclei in Fluoroscence Microscopy Images using Shape and Intensity Information”, Medical Image Analysis, 2019.
J. Heers, Schnörr, C., and Stiehl, H. S., “Globally–Convergent Iterative Numerical Schemes for Non–Linear Variational Image Smoothing and Segmentation on a Multi–Processor Machine”, IEEE Trans. Image Proc., vol. 10, pp. 852–864, 2001.
C. Leue, Wenig, M., Jähne, B., and Platt, U., “GOME mißt atmosphärische Stickoxide. Globale Biomassenverbrennung und Industrieemissionen”, Physik in unserer Zeit, vol. 29, p. 179, 1998.
M. Schiegg, Hanslovsky, P., Haubold, C., Köthe, U., Hufnagel, L., and Hamprecht, F. A., “Graphical Model for Joint Segmentation and Tracking of Multiple Dividing Cell”, Bioinformatics, vol. 31, no. 6, pp. 948-956, 2015.PDF icon Technical Report (534.29 KB)
L. Ardizzone, Lüth, C., Kruse, J., Rother, C., and Köthe, U., “Guided Image Generation with Conditional Invertible Neural Networks”, 2019.
L. Ardizzone, Lüth, C., Kruse, J., Rother, C., and Köthe, U., “Guided Image Generation with Conditional Invertible Neural Networks”, 2019.
B. Jähne, “Gut beleuchtet ist halb gemessen”, QZ, vol. 44, p. 1283--1288, 1999.
B. Jähne, Wais, T., Memery, L., Caulliez, G., Merlivat, L., Münnich, K. O., and Coantic, M., “He and Rn gas exchange experiments in the large wind-wave facility of IMST”, J. Geophys. Res., vol. 90, p. 11,989--11,998, 1985.
R. Lindner, Lou, X., Reinstein, J., Shoeman, R. L., Hamprecht, F. A., and Winkler, A., “Hexicon 2: Automated Processing of Hydrogen-Deuterium Exchange Mass Spectrometry Data with Improved Deuteration Distribution Estimation”, Journal of The American Society for Mass Spectrometry, vol. 25, pp. 1018-1028, 2014.PDF icon Technical Report (2.1 MB)
A. Bruhn, Jakob, T., Fischer, M., Weickert, J., Brüning, U., and Schnörr, C., “High performance cluster computing with 3-D nonlinear diffusion filters”, Real-Time Imaging, vol. 10, pp. 41–51, 2004.
R. Nair and Kondermann, D., “High Precision TOF-guided Depth from Stereo for Room Scanning”, CVMP, Proceedings., 2011.
C. Kräuter, Trofimova, D., Kiefhaber, D., Krah, N., and Jähne, B., “High resolution 2-D fluorescence imaging of the mass boundary layer thickness at free water surfaces”, J. Europ. Opt. Soc. Rap. Public., vol. 9, p. 14016, 2014.
J. Kappes, Speth, M., Reinelt, G., and Schnörr, C., “Higher-order Segmentation via Multicuts”, Comp. Vision Image Understanding, vol. 143, pp. 104–119, 2016.
D. Kiefhaber, Reith, S., Rocholz, R., and Jähne, B., “High-speed imaging of short wind waves by shape from refraction”, J. Europ. Opt. Soc. Rap. Public., vol. 9, p. 14015, 2014.
A. Donath and Kondermann, D., “How Good is Crowdsourcing for Optical Flow Ground Truth Generation?”, submitted to CVPR, 2013.
B. Andres, Köthe, U., Kröger, T., and Hamprecht, F. A., “How to Extract the Geometry and Topology from Very Large 3D Segmentations”, ArXiv e-prints, 2010.PDF icon Technical Report (1.44 MB)

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