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Conference Paper
S. Petra, Schröder, A., Wieneke, B., and Schnörr, C., “On Sparsity Maximization in Tomographic Particle Image Reconstruction”, in Pattern Recognition -- 30th DAGM Symposium, 2008, vol. 5096, p. 294--303.PDF icon Technical Report (1014.71 KB)
F. Diego and Hamprecht, F. A., “Sparse Space-Time Deconvolution for Calcium Image Analysis”, in NIPS. Proceedings, 2014, pp. 64-72.PDF icon Technical Report (5.27 MB)
M. Atif, Zimmermann, K., and Jähne, B., “A space-variant (3D) image simulation tool for computational cameras”, in International Conference on Computational Photography (ICCP) 2010, 2010.
F. Lenzen and Berger, J., “Solution-Driven Adaptive Total Variation Regularization”, in LNCS, 2015.PDF icon Technical Report (857.29 KB)
F. Lenzen and Berger, J., “Solution-Driven Adaptive Total Variation Regularization”, in LNCS, 2015.
B. Jähne and Herrmann, H., “Softwarekonzepte und Algorithmen für die 3D-Bildverarbeitung”, in 4. ABW Workshop, TA Esslingen 22.--23.01.1997, 1997.
B. Jähne, “Simultaneous measurements of solubilities and diffusion coeffcients of volatile species in liquids”, in 7th SOPRAN Annual Meeting, Bremen, Germany, 25-26 March 2014, 2014.
A. G. Herzog, Friedl, F., and Jähne, B., “Simultaneous high-resolution LIF measurements of dissolved gas concentration fields and measurements of wave slope at a wavy free water surface with wind-induced turbulence”, in 15h Int. Symp on Appl. Laser Techniques to Fluid Mechanics, Lisbon, Portugal, July 05--08, 2010, 2010.
M. Klar, Jehle, M., Detert, M., Jirka, G. H., Jähne, B., Köhler, H. - J., and Wenka, T., “Simultaneous 3D PTV and micro-pressure sensor equipment for flow analysis in subsurface gravel layer”, in Proc. of River Flow 2004, 2004, p. 703--712.
S. Meister, Nair, R., and Kondermann, D., “Simulation of Time-of-Flight Sensors using Global Illumination”, in Vision, Modeling and Visualization (VMV), 2013 International Workshop on. Proceedings, 2013, pp. 33-40.
B. Jähne, “Simulation der Austauschprozesse an der Ozeanoberfläche im Labor: das neue Heidelberger Aeolotron”, in Verhandlungen der Deutschen Physikalischen Gesellschaft, Spring Conference, Hamburg, 26.-30.03.2001, 2001.
A. Monroy, Bell, P., and Ommer, B., “Shaping Art with Art: Morphological Analysis for Investigating Artistic Reproductions”, in Proceedings of the European Conference on Computer Vision, Workshop on VISART, 2012, vol. 7583, p. 571--580.PDF icon Technical Report (7 MB)
B. Jähne, Waas, S., and Klinke, J., “Shape from shading techniques for short ocean wind waves”, in Imaging in Transport Processes, 1993, p. 269--281.
S. Gerloff, Hagemann, A., Schnörr, C., Tieck, S., Stiehl, H. S., Dombrowski, R., Dreyer, M., and Wiesendanger, R., “Semi–Automated Analysis of SXM Images”, in Proc. 9th Int. Conf. on Scanning Tunneling Microscopy/Spectroscopy and Related Techniques (STM'97), Hamburg, Germany, 1997.
L. Görlitz, Menze, B. H., Weber, M. - A., and Kelm, B. Michael, “Semi-Supervised Tumor Detection in MRSI With Discriminative Random Fields”, in Pattern Recognition, 2007, vol. 4713, pp. 224-233.PDF icon Technical Report (872.46 KB)
A. Drory, Haubold, C., Avidan, S., and Hamprecht, F. A., “Semi-Global Matching: A Principled Derivation in Terms of Message Passing”, in GCPR. Proceedings, 2014, pp. 43-53.PDF icon Technical Report (2.6 MB)
A. Zouhar, Rother, C., and Fuchs, S., “Semantic 3-D labeling of ear implants using a global parametric transition prior”, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2015, vol. 9350, pp. 177–184.
S. K. Mustikovela, Jampani, V., De Mello, S., Liu, S., Iqbal, U., Rother, C., and Kautz, J., “Self-Supervised Viewpoint Learning From Image Collections”, in CONSAC, 2020.PDF icon PDF (8.77 MB)
Ö. Sümer, Dencker, T., and Ommer, B., “Self-supervised Learning of Pose Embeddings from Spatiotemporal Relations in Videos”, in Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2017.PDF icon Paper (3.98 MB)PDF icon Supplementary Material (3.36 MB)
B. Andres, Hamprecht, F. A., and Garbe, C. S., “Selection of Local Optical Flow Models by Means of Residual Analysis”, in Pattern Recognition, 2007, vol. 4713, pp. 72-81.PDF icon Technical Report (229.64 KB)
B. Andres, Garbe, C. S., Schnörr, C., and Jähne, B., “Selection of local optical flow models by means of residual analysis”, in Proceedings of the 29th DAGM Symposium on Pattern Recognition, 2007, p. 72--81.
C. Leue, Geißler, P., Jähne, B., Jähne, B., Geißler, P., and Haußecker, H., “Segmentierung von Partikelbildern in der Strömungsvisualisierung”, in Proceedings of 18th DAGM-Symposium Mustererkennung, 1996, p. 118--129.
C. Schnörr, “Segmentation of Visual Motion by Minimizing Convex Non-Quadratic Functionals”, in 12th Int. Conf. on Pattern Recognition, Jerusalem, Israel, 1994.
B. Andres, Köthe, U., Helmstaedter, M., Denk, W., and Hamprecht, F. A., “Segmentation of SBFSEM Volume Data of Neural Tissue by Hierarchical Classification”, in Pattern Recognition. 30th DAGM Symposium Munich, Germany, June 10-13, 2008. Proceedings, 2008, vol. 5096, pp. 142-152.PDF icon Technical Report (1.21 MB)
P. Markowsky, Reith, S., Zuber, T. E., König, R., Rohr, K., and Schnörr, C., “Segmentation of cell structure using model-based set covering with iterative reweighting”, in Proc. ISBI, 2017.
L. Kostrykin, Schnörr, C., and Rohr, K., “Segmentation of Cell Nuclei Using Intensity-Based Model Fitting and Sequential Convex Programming”, in Proc. ISBI, 2018.
C. N. Straehle, Köthe, U., Briggman, K., Denk, W., and Hamprecht, F. A., “Seeded watershed cut uncertainty estimators for guided interactive segmentation”, in CVPR 2012. Proceedings, 2012, pp. 765 - 772.PDF icon Technical Report (2.84 MB)
J. Berger, Neufeld, A., Becker, F., Lenzen, F., and Schnörr, C., “Second Order Minimum Energy Filtering on SE(3) with Nonlinear Measurement Equations”, in Scale Space and Variational Methods in Computer Vision (SSVM 2015), 2015.PDF icon Technical Report (364.01 KB)
J. Berger, Neufeld, A., Becker, F., Lenzen, F., and Schnörr, C., “Second Order Minimum Energy Filtering on SE(3) with Nonlinear Measurement Equations”, in Scale Space and Variational Methods in Computer Vision (SSVM 2015), 2015.
K. E. Richter, Rocholz, R., and Jähne, B., “The Schmidt Number Dependency of Air-Sea Gas Exchange with Varying Surfactant Coverage”, in SOLAS Open Science Conference, Washington State, USA, 2012.
A. Mansfield, Gehler, P., Van Gool, L., and Rother, C., “Scene carving: Scene consistent image retargeting”, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2010, vol. 6311 LNCS, pp. 143–156.
M. Geese, Ruhnau, P., and Jähne, B., “Scene based maximum likelihood PRNU and DSNU non uniformity correction”, in Forum Bildverarbeitung, 2012, p. 71--82.
O. Friedrich, Weber, C., Both, M., von Wegner, F., Chamberlain, J. S., Garbe, C. S., and Fink, R. H. A., “Sarcomere Structure and Motor-Protein Function in an Animal Model of Duchenne Muscular Dystrophy (mdx mouse)”, in 87th Annual Meeting of the German Physiological Society, 2008.
N. Kirchgeßner, Spies, H., Scharr, H., and Schurr, U., “Root Growth Measurements in Object Coordinates”, in Proceedings of the 23th DAGM Symposium on Pattern Recognition, 2001.
N. Kirchgeßner, Spies, H., Scharr, H., and Schurr, U., “Root Growth Analysis in Physiological Coordinates”, in International Conference on Image Analysis and Processing (ICIAP'01), 2001.

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