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Author Title [ Type(Desc)] Year
Journal Article
Jähne, B, Brocke, M, Eisele, H, Hader, S, Hamprecht, F A, Happold, W, Raisch, F and Restle, J (2002). Für Anspruchsvolle - Multidimensionale Bildverarbeitung in der Produktion. Qualität und Zuverlässigkeit. 47 1154-1159
Lempitsky, V, Rother, C, Roth, S and Blake, A (2010). Fusion moves for markov random field optimization. IEEE Transactions on Pattern Analysis and Machine Intelligence. 32 1392–1405
Lempitsky, V, Rother, C, Roth, S and Blake, A (2010). Fusion moves for markov random field optimization. IEEE Transactions on Pattern Analysis and Machine Intelligence. 32 1392–1405
Blake, A, Criminisi, A, Cross, G, Kolmogorov, V and Rother, C (2007). Fusion of stereo, colour and contrast. Springer Tracts in Advanced Robotics. 28. www.research.microsoft.com/vision/cambridge
Röder, J, Tolosana-Delgado, R and Hamprecht, F A (2011). Gaussian process classification: singly versus doubly stochastic models, and new computational schemes. Stochastic Environmental Research & Risk Assessment. 25 (7) 865-879PDF icon Technical Report (672.68 KB)
Nicola, A, Petra, S, Popa, C and Schnörr, C (2011). A general extending and constraining procedure for linear iterative methods. Int. J. Comp. Math. http://dx.doi.org/10.1080/00207160.2011.634002
Nicola, A, Petra, S, Popa, C and Schnörr, C (2011). A general extending and constraining procedure for linear iterative methods. Int.~J.~Comp.~Math. http://dx.doi.org/10.1080/00207160.2011.634002PDF icon Technical Report (633.79 KB)
Schmähling, J and Hamprecht, F A (2007). Generalizing the Abbott-Firestone curve by two new surface descriptors. Wear. 262 1360-1371PDF icon Technical Report (877.34 KB)
Hamprecht, F A, Scott, W R P and van Gunsteren, W F (1997). Generation of pseudo-native protein structures for threading. Proteins. 28 522-529
Rubio, J C, Eigenstetter, A and Ommer, B (2015). Generative Regularization with Latent Topics for Discriminative Object Recognition. Pattern Recognition. Elsevier. 48 3871--3880PDF icon Technical Report (5.49 MB)
Aström, F and Schnörr, C (2017). A Geometric Approach for Color Image Regularization. Comp. Vision Image Understanding. 165 43–59. https://doi.org/10.1016/j.cviu.2017.10.013
Zeilmann, A, Savarino, F, Petra, S and Schnörr, C (2020). Geometric Numerical Integration of the Assignment Flow. Inverse Problems. 36 034004 (33pp)
Zeilmann, A, Savarino, F, Petra, S and Schnörr, C (2019). Geometric Numerical Integration of the Assignment Flow. Inverse Problems. https://doi.org/10.1088/1361-6420/ab2772
Zeilmann, A, Savarino, F, Petra, S and Schnörr, C (2018). Geometric Numerical Integration of the Assignment Flow. preprint: arXiv. https://arxiv.org/abs/1810.06970
Savchynskyy, B and Schmidt, S (2012). Getting Feasible Variable Estimates From Infeasible Ones: MRF Local Polytope Study. arXiv:1210.4081
Woodford, O J (2009). A Global Perspective on MAP Inference for Low-Level Vision Supplementary material to ICCV submission \# 1536. Optimization
Wanner, S, Straehle, C N and Goldlücke, B (2013). Globally Consistent Multi-Label Assignment on the Ray Space of 4D Light Fields. CVPR 2013. Proceedings. 1011-1018
Schmitzer, B and Schnörr, C (2015). Globally Optimal Joint Image Segmentation and Shape Matching based on Wasserstein Modes. J. Math. Imag. Vision. 52 436–458. http://link.springer.com/article/10.1007/s10851-014-0546-8
Schmitzer, B and Schnörr, C (2015). Globally Optimal Joint Image Segmentation and Shape Matching based on Wasserstein Modes. J.~Math.~Imag.~Vision. 52 436--458. http://link.springer.com/article/10.1007/s10851-014-0546-8PDF icon Technical Report (1.97 MB)
Kostrykin, L, Schnörr, C and Rohr, K (2019). Globally Optimal Segmentation of Cell Nuclei in Fluoroscence Microscopy Images using Shape and Intensity Information. Medical Image Analysis. https://doi.org/10.1016/j.media.2019.101536
Heers, J, Schnörr, C and Stiehl, H S (2001). Globally–Convergent Iterative Numerical Schemes for Non–Linear Variational Image Smoothing and Segmentation on a Multi–Processor Machine. IEEE Trans. Image Proc. 10 852–864
Leue, C, Wenig, M, Jähne, B and Platt, U (1998). GOME mißt atmosphärische Stickoxide. Globale Biomassenverbrennung und Industrieemissionen. Physik in unserer Zeit. 29 179
Schiegg, M, Hanslovsky, P, Haubold, C, Köthe, U, Hufnagel, L and Hamprecht, F A (2015). Graphical Model for Joint Segmentation and Tracking of Multiple Dividing Cell. Bioinformatics. 31 948-956. http://bioinformatics.oxfordjournals.org/content/early/2014/11/17/bioinformatics.btu764.full.pdf?keytype=ref&ijkey=mTXWsiFrci7R8tcPDF icon Technical Report (534.29 KB)
Ardizzone, L, Lüth, C, Kruse, J, Rother, C and Köthe, U (2019). Guided Image Generation with Conditional Invertible Neural Networks. http://arxiv.org/abs/1907.02392
Ardizzone, L, Lüth, C, Kruse, J, Rother, C and Köthe, U (2019). Guided Image Generation with Conditional Invertible Neural Networks. http://arxiv.org/abs/1907.02392
Jähne, (1999). Gut beleuchtet ist halb gemessen. QZ. 44 1283--1288. https://www.qz-online.de/qz-zeitschrift/archiv/artikel/art-der-lichtquelle-und-der-einstrahlbedingungen-sind-entscheidend-gut-beleuchtet-ist-halb-gemessen-345974.html
Jähne, B, Wais, T, Memery, L, Caulliez, G, Merlivat, L, Münnich, K O and Coantic, M (1985). He and Rn gas exchange experiments in the large wind-wave facility of IMST. J. Geophys. Res. 90 11,989--11,998
Lindner, R, Lou, X, Reinstein, J, Shoeman, R L, Hamprecht, F A and Winkler, A (2014). Hexicon 2: Automated Processing of Hydrogen-Deuterium Exchange Mass Spectrometry Data with Improved Deuteration Distribution Estimation. Journal of The American Society for Mass Spectrometry. 25 1018-1028PDF icon Technical Report (2.1 MB)
Bruhn, A, Jakob, T, Fischer, M, Weickert, J, Brüning, U and Schnörr, C (2004). High performance cluster computing with 3-D nonlinear diffusion filters. Real-Time Imaging. 10 41–51
Nair, R and Kondermann, D (2011). High Precision TOF-guided Depth from Stereo for Room Scanning. CVMP, Proceedings
Kräuter, C, Trofimova, D, Kiefhaber, D, Krah, N and Jähne, B (2014). High resolution 2-D fluorescence imaging of the mass boundary layer thickness at free water surfaces. J. Europ. Opt. Soc. Rap. Public. 9 14016
Kappes, J, Speth, M, Reinelt, G and Schnörr, C (2016). Higher-order Segmentation via Multicuts. Comp. Vision Image Understanding. 143 104–119
Kiefhaber, D, Reith, S, Rocholz, R and Jähne, B (2014). High-speed imaging of short wind waves by shape from refraction. J. Europ. Opt. Soc. Rap. Public. 9 14015
Donath, A and Kondermann, D (2013). How Good is Crowdsourcing for Optical Flow Ground Truth Generation?. submitted to CVPR
Andres, B, Köthe, U, Kröger, T and Hamprecht, F A (2010). How to Extract the Geometry and Topology from Very Large 3D Segmentations. ArXiv e-prints. http://arxiv.org/abs/1009.6215PDF icon Technical Report (1.44 MB)

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