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

D. Kiefhaber, Rocholz, R., Balschbach, G., Schaper, J., and Jähne, B., “Measurement of ocean wave statistics with the reflective stereo slope gauge”, in DPG Frühjahrstagung Dresden, Fachverband Umweltphysik, 2011.

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

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

D. Kiefhaber, Rocholz, R., Schaper, J., Balschbach, G., and Jähne, B., “Mean square slope measurements in the field with the reflective stereo slope gauge”, in EGU General Assembly, Vienna, 2011.

A. Kirillov, Schlesinger, D., Vetrov, D., Rother, C., and Savchynskyy, B., “M-best-diverse labelings for submodular energies and beyond”, in Advances in Neural Information Processing Systems, 2015, vol. 2015-Janua, pp. 613–621.

A. Eigenstetter, Yarlagadda, P., and Ommer, B., “Max-Margin Regularization for Reducing Accidentalness in Chamfer Matching”, in Proceedins of the Aian Conference on Computer Vision, 2012, p. 152--163.

Technical Report (7.31 MB)

A. Shekhovtsov, Swoboda, P., and Savchynskyy, B., “Maximum Persistency via Iterative Relaxed Inference in Graphical Models”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 40, pp. 1668–1682, 2018.

M. Welk, Becker, F., Schnörr, C., and Weickert, J., “Matrix-Valued Filters as Convex Programs”, in Scale-Space 2005, 2005, vol. 3459, pp. 204–216.

W. Erb, 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., “Mathematical Analysis of the 1D Model and Reconstruction Schemes for Magnetic Particle Imaging”, Inverse Problems, vol. 34, no. 5, 2018.

H. Spies, Haußecker, H., and Köhler, H. - J., “Material transport and structure changes at soil-water interfaces”, in Filters and Drainage in Geotechnical and Environmental Engineering, 2000, p. 91--97.

B. Balluff, Hanselmann, M., and Heeren, R. M. A., “Mass spectrometry imaging for the investigation of intratumor heterogeneity”, in Advances in Cancer Research, vol. 134, Elsevier, 2017, pp. 201-230.

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

Technical Report (317.04 KB)

T. M. D. Strouse, “Marijuana's Public Health Pros and Cons | For Better | US News”, U.S. News and World Report, 2016.

D. L. Richmond, Kainmueller, D., Yang, M. Y., Myers, E. W., and Rother, C., “Mapping auto-context decision forests to deep convnets for semantic segmentation”, in British Machine Vision Conference 2016, BMVC 2016, 2016, vol. 2016-Septe, pp. 144.1–144.12.

D. L. Richmond, Kainmueller, D., Yang, M. Y., Myers, E. W., and Rother, C., “Mapping auto-context decision forests to deep convnets for semantic segmentation”, in British Machine Vision Conference 2016, BMVC 2016, 2016, vol. 2016-Septe, pp. 144.1–144.12.

D. L. Richmond, Kainmueller, D., Yang, M. Y., Myers, E. W., and Rother, C., “Mapping auto-context decision forests to deep convnets for semantic segmentation”, in British Machine Vision Conference 2016, BMVC 2016, 2016, vol. 2016-Septe, pp. 144.1–144.12.

J. Hendrik Kappes, Beier, T., and Schnörr, C., “MAP-Inference on Large Scale Higher-Order Discrete Graphical Models by Fusion Moves”, in International Workshop on Graphical Models in Computer Vision, 2014.

J. H. Kappes, Beier, T., and Schnörr, C., “MAP-Inference on Large Scale Higher-Order Discrete Graphical Models by Fusion Moves”, in Computer Vision - {ECCV} 2014 Workshops - Zurich, Switzerland, September 6-7 and 12, 2014, Proceedings, Part {II}, 2014.

Technical Report (557.49 KB)

J. H. Kappes and Schnörr, C., “MAP-Inference for Highly-Connected Graphs with DC-Programming”, in Pattern Recognition -- 30th DAGM Symposium, 2008, vol. 5096, p. 1--10.

Technical Report (1.91 MB)

J. H. Kappes and Schnörr, C., “MAP-Inference for Highly-Connected Graphs with DC-Programming”, in Pattern Recognition – 30th DAGM Symposium, 2008, vol. 5096, pp. 1–10.

F. Aström, Hühnerbein, R., Savarino, F., Recknagel, J., and Schnörr, C., “MAP Image Labeling Using Wasserstein Messages and Geometric Assignment”, in Proc. SSVM, 2017, vol. 10302.

R. Rombach, Esser, P., and Ommer, B., “Making Sense of CNNs: Interpreting Deep Representations & Their Invariances with INNs”, IEEE European Conference on Computer Vision (ECCV). 2020.

B. H. Menze, Kelm, B. Michael, Heck, D., Lichy, M. P., and Hamprecht, F. A., “Machine-based rejection of low quality spectra and estimation of brain tumor probabilities from magnetic resonance spectroscopic images”, in Bildverarbeitung für die Medizin, 2006, pp. 31-36.

Technical Report (672.84 KB)

S. Peter, “Machine learning under test-time budget constraints”. Heidelberg University, 2019.

S. Wolf, “Machine Learning for Instance Segmentation”. Heidelberg University, 2020.

M. Lindner, “A Machine Learning Approach to Improve Digital Embryo Analysis”, University of Heidelberg, 2011.

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M. Bopp, “Luft- und wasserseitige Strömungsverhältnisse im ringförmigen Heidelberger Wind-Wellen-Kanal (Aeolotron)”, Institut für Umweltphysik, Universität Heidelberg, Germany, 2014.

M. Bopp, “Luft- und wasserseitige Strömungsverhältnisse im ringförmigen Heidelberger Wind-Wellen-Kanal (Aeolotron)”, Institut für Umweltphysik, Universität Heidelberg, Germany, 2014.

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

B. Brattoli, Büchler, U., Wahl, A. - S., Schwab, M. E., and Ommer, B., “LSTM Self-Supervision for Detailed Behavior Analysis”, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.

Article (8.75 MB)

P. Pinggera, Ramos, S., Gehrig, S., Franke, U., Rother, C., and Mester, R., “Lost and found: Detecting small road hazards for self-driving vehicles”, in IEEE International Conference on Intelligent Robots and Systems, 2016, vol. 2016-Novem, pp. 1099–1106.

J. Jancsary, Nowozin, S., and Rother, C., “Loss-specific training of non-parametric image restoration models: A new state of the art”, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2012, vol. 7578 LNCS, pp. 112–125.

J. Jancsary, Nowozin, S., and Rother, C., “Loss-specific training of non-parametric image restoration models: A new state of the art”, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2012, vol. 7578 LNCS, pp. 112–125.

R. Fitzenberger, “Lokale Transformationsmethoden zur Auswertung von Wellenneigungsbildern der Wasseroberfläche im Bereich kleinskaliger Oberflächenwellen”, Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ. Heidelberg, 1997.

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