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Lou, X and Hamprecht, F A (2012). Learning to Segment Dense Cell Nuclei with Shape Prior. CVPR 2012. Proceedings. 1012-1018

Technical Report (2.66 MB)

Sommer, C, Fiaschi, L, Hamprecht, F A and Gerlich, D (2012). Learning-based Mitotic Cell Detection in Histopathological Images. ICPR 2012. Proceedings. 2306-2309

Technical Report (1.96 MB)

Münsterer, T and Jähne, B (1998). LIF measurements of concentration profiles in the aqueous mass boundary layer. Exp. Fluids. 25 190--196

Peckar, W, Schnörr, C, Rohr, K, Stiehl, H –S and Spetzger, U (1998). Linear and Incremental Estimation of Elastic Deformations in Medical Registration Using Prescribed Displacements. Machine Graphics & Vision. 7 807–829

Rother, C and Carlsson, S (2002). Linear multi view reconstruction and camera recovery using a reference plane. International Journal of Computer Vision. 49 117–141

Rother, C (2003). Linear Multi-View Reconstruction for Translating Cameras. Nada.Kth.Se. http://www.nada.kth.se/ carstenr/papers/paper_ssab03.pdf

Weber, S, Schüle, T, Schnörr, C and Hornegger, J (2004). A Linear Programming Approach to Limited Angle 3D Reconstruction from DSA Projections. Methods of Information in Medicine. 43 320–326

Fehr, J (2010). Local Rotation Invariant Patch Descriptors for 3D Vector Fields. Pattern Recognition, International Conference on, Istanbul, Turkey, August 23-26, 2010. 1381-1384

Bruhn, A, Weickert, J and Schnörr, C (2005). Lucas/Kanade Meets Horn/Schunck: Combining Local and Global Optic Flow Methods. 61 211-231

Strouse, T M D (2016). Marijuana's Public Health Pros and Cons | For Better | US News. U.S. News and World Report. http://health.usnews.com/health-news/patient-advice/articles/2016-10-12/marijuanas-public-health-pros-and-cons

Kandemir, M, Hamprecht, F A, Wojek, C and Schmidt, U (2017). Maschinelles Lernen. Patent, Patent Number WO2017032775A1

Technical Report (317.04 KB)

Erb, W, Weinmann, A, Ahlborg, M, Brandt, C, Bringout, G, Buzug, T M, Frikel, J, Kaethner, C, Knopp, T, März, T, Möddel, M, Storath, M and Weber, A (2018). Mathematical Analysis of the 1D Model and Reconstruction Schemes for Magnetic Particle Imaging. Inverse Problems. 34

Shekhovtsov, A, Swoboda, P and Savchynskyy, B (2018). Maximum Persistency via Iterative Relaxed Inference in Graphical Models. IEEE Transactions on Pattern Analysis and Machine Intelligence. 40 1668–1682. http://www.icg.tugraz.at/

Stöhr, M, Roth, K and Jähne, B (2003). Measurement of 3D pore-scale flow in index-matched porous media. Exp. Fluids. 35 159--166

Nagel, L, Krall, K E and Jähne, B (2019). Measurement of air-sea gas transfer velocities in the Baltic Sea. Ocean Science. 15 235–247

Jähne, B, Heinz, G and Dietrich, W (1987). Measurement of the diffusion coefficients of sparingly soluble gases in water. J. Geophys. Res. 92 10,767--10,776

Jähne, B, Münnich, K O and Siegenthaler, U (1979). Measurements of gas exchange and momentum transfer in a circular wind-water tunnel. Tellus. 31 321--329

Mesarchaki, E, Kräuter, C, Krall, K Ellen, Bopp, M, Helleis, F, Williams, J and Jähne, B (2014). Measuring air-sea gas exchange velocities in a large scale annular wind-wave tank. Ocean Sci. Discuss. 11 1643--1689

Mesarchaki, E, Kräuter, C, Krall, K Ellen, Bopp, M, Helleis, F, Williams, J and Jähne, B (2015). Measuring air–sea gas-exchange velocities in a large-scale annular wind–wave tank. Ocean Sci. 11 121--138

Welk, M, Weickert, J, Becker, F, Schnörr, C, Feddern, C and Burgeth, B (2007). Median and related local filters for tensor-valued images. Signal Processing. 87 291-308

Technical Report (1007.29 KB)

Welk, M, Weickert, J, Becker, F, Schnörr, C, Feddern, C and Burgeth, B (2007). Median and related local filters for tensor-valued images. Signal Processing. 87 291-308

Bengio, Y, Deleu, T, Rahaman, N, Ke, R, Lachapelle, S, Bilaniuk, O, Goyal, A and Pal, C (2019). A Meta-Transfer Objective for Learning to Disentangle Causal Mechanisms. arXiv preprint arXiv:1901.10912

Technical Report (871.59 KB)

Staudacher, M, Hamprecht, F A and Görlitz, L (2008). Method for processing an intensity image of a microscope. Patent, Patent Number: WO2008034721A1

Technical Report (39.81 KB)

Kirchner, M, Steen, J A J, Hamprecht, F A and Steen, H (2010). MGFp: An Open Mascot Generic Format Parser Library Implementation. Journal of Proteome Research. 9 (5) 27622763

Technical Report (125.18 KB)

Zappa, C J, Asher, W E, Jessup, A T, Klinke, J and Long, S R (2004). Microbreaking and the enhancement of air-water transfer velocity. J. Geophys. Res. 109 C08S16

Roetmann, K, Schmunk, W, Garbe, C S and Beushausen, V (2008). Micro-flow analysis by molecular tagging velocimetry and planar raman-scattering. Exp. Fluids. 44 419--430

Pape, C, Remme, R, Wolny, A, Olberg, S, Wolf, S, Cerrone, L, Cortese, M, Klaus, S, Lucic, B, Ullrich, S, Anders-Össwein, M, Wolf, S, Cerikan, B, Neufeldt, C J, Ganter, M, Schnitzler, P, Merle, U, Lusic, M, Boulant, S, Stanifer, M, Bartenschlager, R, Hamprecht, F A, Kreshuk, A, Tischer, C, Kräusslich, H - G, Müller, B and Laketa, V (2021). Microscopy-based assay for semi-quantitative detection of SARS-CoV-2 specific antibodies in human sera. BioEssays. 43

Menze, B H, Kelm, B Michael, Weber, M - A, Bachert, P and Hamprecht, F A (2008). Mimicking the human expert: pattern recognition for an automated assessment of data quality in MRSI. Magnetic Resonance in Medicine. 59 1457-1466

Technical Report (1.45 MB)

Schäfer, H, Lenzen, F and Garbe, C S (2014). Model based scattering correction in time-of-flight cameras. Optics Express. 22 29835-29846

Kiechle, M, Storath, M, Weinmann, A and Kleinsteuber, M (2018). Model-based learning of local image features for unsupervised texture segmentation. IEEE Transactions on Image Processing. 27 1994-2007

Breitenreicher, D and Schnörr, C (2011). Model-Based Multiple Rigid Object Detection and Registration in Unstructured Range Data. Int. J. Comp. Vision. 92 32–52. http://www.springerlink.com/content/v266873267180602/

Breitenreicher, D and Schnörr, C (2011). Model-Based Multiple Rigid Object Detection and Registration in Unstructured Range Data. Int.~J.~Comp.~Vision. 92 32--52. http://www.springerlink.com/content/v266873267180602/

Technical Report (4.3 MB)

Schmitzer, B and Schnörr, C (2013). Modelling convex shape priors and matching based on the Gromov-Wasserstein distance. Journal of Mathematical Imaging and Vision. 46 143-159

Technical Report (957.78 KB)

Schmitzer, B and Schnörr, C (2013). Modelling convex shape priors and matching based on the Gromov-Wasserstein distance. Journal of Mathematical Imaging and Vision. 46 143-159

Gee, P J, Hamprecht, F A, Schuler, L D, van Gunsteren, W F, Duchardt, E, Schwalbe, H, Albert, M and Seebach, D (2002). A molecular dynamics simulation study of the conformational preferences of oligo-(3- hydroxyalcanoic acids) in chloroform solution. Helv. Chim. Acta. 85 618-632

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