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C. Götz, “Bildverarbeitungsalgorithmen in der Fluoreszenzmikroskopie”, IWR, Fakultät für Physik und Astronomie, Univ.\ Heidelberg, 1999.

J. Keuchel, Schnörr, C., Schellewald, C., and Cremers, D., “Binary Partitioning, Perceptual Grouping, and Restoration with Semidefinite Programming”, vol. 25, pp. 1364–1379, 2003.

S. Weber, Schnörr, C., Schüle, T., and Hornegger, J., “Binary Tomography by Iterating Linear Programs”, Geometric Properties from Incomplete Data. Springer, 2005.

S. Weber, Schüle, T., Hornegger, J., and Schnörr, C., “Binary Tomography by Iterating Linear Programs from Noisy Projections”, in Combinatorial Image Analysis, Proc. Int. Workshop on Combinatorial Image Analysis (IWCIA'04), 2004, vol. 3322, pp. 38–51.

S. Weber, Schüle, T., Schnörr, C., and Kuba, A., “Binary Tomography with Deblurring”, in Combinatorial Image Analysis, 2006, vol. 4040, pp. 375-388.

Technical Report (803.63 KB)

N. Ozlu, Monigatti, F., Renard, B. Y., Field, C. M., Steen, H., Mitchison, T. J., and Steen, J. J., “Binding partner switching on microtubules and aurora-B in the mitosis to cytokinesis transition”, Molecular & Cellular Proteomics, 2009.

X. Lou, “Biomedical Data Analysis with Prior Knowledge: Modeling and Learning”. University of Heidelberg, 2011.

M. C. Scholes, Matrai, P. A., Andreae, M. O., Smith, K. A., Manning, M. R., Artaxo, P., Barrie, L. A., Bates, T. S., Butler, J. H., Ciccioli, P., Cieslik, S. A., Delmas, R. J., and Dentener, F. J., “Biosphere-Atmosphere Interactions”, Atmospheric Chemistry in a Changing World, An Integration and Synthesis of a Decade of Tropospheric Chemistry Research. Springer, p. 19--71, 2003.

B. Jähne, “Blätter, Wind und Wellen. Unsichtbares wird sichtbar.”, computer art faszination, dot'99. dot-Verlag, p. 38--43, 1999.

A. L. Bendinger, Debus, C., Glowa, C., Karger, C. P., Peter, J., and Storath, M., “Bolus arrival time estimation in dynamic contrast-enhanced magnetic resonance imaging of small animals based on spline models, in press”, Physics in Medicine and Biology, vol. 64, no. 4, 2019.

T. Hörnlein, “Boosted Feature Generation for Classification Problems Involving High Numbers of Inputs and Classes”, vol. Dissertation. IWR, Fakultät für Mathematik und Informatik, Univ. Heidelberg, 2010.

T. Hörnlein, Jähne, B., and Süße, H., “Boosting shift-invariant features”, in Pattern Recognition, 2009, vol. 5748, p. 121--130.

T. Hodaň, Michel, F., Brachmann, E., Kehl, W., Buch, A. Glent, Kraft, D., Drost, B., Vidal, J., Ihrke, S., Zabulis, X., Sahin, C., Manhardt, F., Tombari, F., Kim, T. Kyun, Matas, J., and Rother, C., “BOP: Benchmark for 6D object pose estimation”, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2018, vol. 11214 LNCS, pp. 19–35.

A. Behl, Hosseini Jafari, O., Mustikovela, S. Karthik, Abu Alhaija, H., Rother, C., and Geiger, A., “Bounding Boxes, Segmentations and Object Coordinates: How Important is Recognition for 3D Scene Flow Estimation in Autonomous Driving Scenarios?”, in Proceedings of the IEEE International Conference on Computer Vision, 2017, vol. 2017-Octob, pp. 2593–2602.

A. Behl, Hosseini Jafari, O., Mustikovela, S. Karthik, Abu Alhaija, H., Rother, C., and Geiger, A., “Bounding Boxes, Segmentations and Object Coordinates: How Important is Recognition for 3D Scene Flow Estimation in Autonomous Driving Scenarios?”, in Proceedings of the IEEE International Conference on Computer Vision, 2017, vol. 2017-Octob, pp. 2593–2602.

V. Lempitsky, Blake, A., and Rother, C., “Branch-and-mincut: Global optimization for image segmentation with high-level priors”, Journal of Mathematical Imaging and Vision, vol. 44, pp. 315–329, 2012.

B. Jähne, “Bringing the ocean inside the lab, image processing in environmental sciences”. 2011.

S. Petra, Schnörr, C., Becker, F., and Lenzen, F., “B-SMART: Bregman-Based First-Order Algorithms for Non-Negative Compressed Sensing Problems”, in Proceedings of the 4th International Conference on Scale Space and Variational Methods in Computer Vision SSVM, 2013, pp. 110-124.

S. Petra, Schnörr, C., Becker, F., and Lenzen, F., “B-SMART: Bregman-Based First-Order Algorithms for Non-Negative Compressed Sensing Problems”, in Proceedings of the 4th International Conference on Scale Space and Variational Methods in Computer Vision (SSVM) 2013, 2013, vol. 7893, pp. 110-124.

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L. Flothow, “Bubble Characteristics from Breaking Waves in Fresh Water and Simulated Seawater”, Institut für Umweltphysik, Universität Heidelberg, Germany, 2017.

J. Heikkonen, Koikkalainen, P., and Schnörr, C., “Building Trajectories via Selforganization from Spatiotemporal Features”, in 12th Int. Conf. on Pattern Recognition, Jerusalem, Israel, 1994.

J. H. Kappes, Savchynskyy, B., and Schnörr, C., “A Bundle Approach To Efficient MAP-Inference by Lagrangian Relaxation”, in CVPR, 2012.

Technical Report (430.63 KB)

J. H. Kappes, Savchynskyy, B., and Schnörr, C., “A Bundle Approach To Efficient MAP-Inference by Lagrangian Relaxation”, in CVPR. Proceedings, 2012, pp. 1688-1695.

C

B. Jähne and Schultz, H., “Calibration and accuracy of optical slope measurements for short wind waves”, in Optics of the Air-Sea Interface: Theory and Measurements, 1992, vol. 1749, p. 222--233.

R. Rocholz, “Calibration of the 2010-CISG Setup at the Aeolotron”, Institute of Environmental Physics, University of Heidelberg, 2010.

S. Mersmann, Seitel, A., Erz, M., Jähne, B., Nickel, F., Mieth, M., Mehrabi, A., and Maier-Hein, L., “Calibration of time-of-flight cameras for accurate intraoperative surface reconstruction”, Med. Phys., vol. 40, p. 082701, 2013.

S. Karthik Mustikovela, Yang, M. Ying, and Rother, C., “Can ground truth label propagation from video help semantic segmentation?”, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2016, vol. 9915 LNCS, pp. 804–820.

C. M. Zechmann, Kelm, B. Michael, Zamecnik, P., Ikinger, U., Waldherr, R., Röll, S., Delorme, S., Hamprecht, F. A., and Bachert, P., “Can man still beat the machine? Automated vs. manual pattern recognition of 3D MRSI data of prostate cancer patients”, in Proceedings of the 16th ISMRM, 2006.

Technical Report (664.38 KB)

L. Maier-Hein, Mersmann, S., Kondermann, D., Bodenstedt, S., Sanchez, A., Stock, C., Kenngott, H., Eisenmann, M., and Speidel, S., “Can masses of non-experts train highly accurate image classifiers? A crowdsourcing approach to instrument segmentation in laparoscopic images”, in MICCAI, 2014.

J. Kleesiek, Morshuis, J. Nikolas, Isensee, F., Deike-Hofmann, K., Paech, D., Kickingereder, P., Köthe, U., Rother, C., Forsting, M., Wick, W., Bendszus, M., Schlemmer, H. Peter, and Radbruch, A., “Can Virtual Contrast Enhancement in Brain MRI Replace Gadolinium?: A Feasibility Study”, Investigative Radiology, vol. 54, pp. 653–660, 2019.

C. N. Straehle, Köthe, U., Knott, G. W., and Hamprecht, F. A., “Carving: Scalable Interactive Segmentation of Neural Volume Electron Microscopy Images”, in MICCAI 2011, Proceedings., 2011, vol. 6891, pp. 653-660.

Technical Report (1.69 MB)

J. Welbl, “Casting Random Forests as Artificial Neural Networks (and Profiting from It)”, in GCPR. Proceedings, 2014, pp. 765-771.

Technical Report (376.24 KB)

C. Zhang, Yarkony, J., and Hamprecht, F. A., “Cell detection and segmentation using correlation clustering”, in MICCAI. Proceedings, 2014, pp. 9-16.

Technical Report (8.06 MB)

M. Kandemir and Hamprecht, F. A., “Cell event detection in phase-contrast microscopy sequences from few annotations”, MICCAI. Proceedings, vol. LNCS 9351. Springer, pp. 316-323, 2015.

Technical Report (564.69 KB)

S. Wolf, “Cell Tracking With Graphical Model Using Higher Order Features On Track Segments”, University of Heidelberg, 2016.

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