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2015
N. Krah, Testa, M., Brons, S., Jäkel, O., Parodi, K., Voss, B., and Rinaldi, I., “An advanced image processing method to improve the spatial resolution of ion radiographies”, Physics in Medicine and Biology, vol. 60, p. 8525, 2015.
B. Jähne, “Air-sea gas exchange: from empiric wind speed relations to regimes and ranges”, in Frühjahrstagung der Deutschen Physikalischen Gesellschaft, Fachverband Umweltphysik, 2015, p. UP 16.3.
R. Nair, “Analysis and Modeling of Passive Stereo and Time-of-Flight Imaging”, vol. Dissertation. IWR, Univ. Heidelberg, 2015.
N. Gianniotis, Schnörr, C., Molkenthin, C., and Bora, S. S., “Approximate variational inference based on a finite sample of Gaussian latent variables”, Patt.~Anal.~Appl., 2015.PDF icon Technical Report (1.4 MB)
M. Kandemir, “Asymmetric transfer learning with deep Gaussian processes”, ICML. Proceedings. pp. 730-738, 2015.PDF icon Technical Report (570.95 KB)
A. Kreshuk, Walecki, R., Köthe, U., Gierthmühlen, M., Plachta, D., Genoud, C., Haastert-Talini, K., and Hamprecht, F. A., “Automated Tracing of Myelinated Axons and Detection of the Nodes of Ranvier in Serial Images of Peripheral Nerves”, Journal of Microscopy, vol. 259 (2), pp. 143-154, 2015.
2016
J. Kunz and Jähne, B., “Active thermography as a tool to investigate heat and gas transfer across the air-water interface”, in 13th Quantitative Infrared Thermographie Conference (QIRT 2016), Gdansk 4–8 July 2016, 2016.
C. Proß, “Analysis of the Fetch Dependency of the Slope of Wind-Water Waves”, Institut für Umweltphysik, Universität Heidelberg, Germany, 2016.
F. Aström, Petra, S., Schmitzer, B., and Schnörr, C., “The Assignment Manifold: A Smooth Model for Image Labeling”, in Proc. 2nd Int. Workshop on Differential Geometry in Computer Vision and Machine Learning (DIFF-CVML'16; oral presentation; Grenander best paper award), 2016.
N. Krasowski, “Automated Segmentation for Connectomics Utilizing Higher-Order Biological Priors”. University of Heidelberg, 2016.
C. Pape, “Automatic Segmentation of Neurites from Anisotropic EM-Imaging”, University of Heidelberg, 2016.
T. Prange, “Automatic Segmentation of Neurons in Electron Microscopy Data with Membrane Defects”, University of Heidelberg, 2016.
2017
M. Kandemir, Hamprecht, F. A., Wojek, C., and Schmidt, U., “Active machine learning for training an event classification”, Patent, Patent Number WO2017032775 A1, 2017.
J. Kunz, “Active Thermography as a Tool for the Estimation of Air-Water Transfer Velocities”, vol. Dissertation. Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ. Heidelberg, 2017.
O. Hosseini Jafari, Groth, O., Kirillov, A., Yang, M. Ying, and Rother, C., “Analyzing modular CNN architectures for joint depth prediction and semantic segmentation”, in Proceedings - IEEE International Conference on Robotics and Automation, 2017, pp. 4620–4627.
L. Gerhard Holtmann, “Aufbau eines aktiven Thermographiesystems zur Messung des Geschwindigkeitsgradienten in der windgetriebenen wasserseitigen viskosen Grenzschicht”, Institut für Umweltphysik, Universität Heidelberg, Germany, 2017.
H. Abu Alhaija, Mustikovela, S. Karthik, Mescheder, L., Geiger, A., and Rother, C., “Augmented reality meets deep learning for car instance segmentation in urban scenes”, in British Machine Vision Conference 2017, BMVC 2017, 2017.
2018
M. Bopp, “Air-Flow and Stress Partitioning over Wind Waves in a Linear Wind-Wave Facility”, vol. Dissertation. Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ. Heidelberg, Heidelberg, 2018.
S. Lang and Ommer, B., “Attesting Similarity: Supporting the Organization and Study of Art Image Collections with Computer Vision”, Digital Scholarship in the Humanities, Oxford University Press, vol. 33, no. 4, pp. 845-856, 2018.
H. Abu Alhaija, Mustikovela, S. Karthik, Mescheder, L., Geiger, A., and Rother, C., “Augmented Reality Meets Computer Vision”, International Journal of Computer Vision, vol. In press, pp. 1–13, 2018.
H. Abu Alhaija, Mustikovela, S. K., Mescheder, A., Geiger, C., and Rother, C., “Augmented Reality Meets Computer Vision Efficient Data Generation for Urban Driving Scenes”, IJCV, pp. 1-12, 2018.PDF icon Technical Report (3.83 MB)
H. Abu Alhaija, Mustikovela, S. Karthik, Mescheder, L., Geiger, A., and Rother, C., “Augmented Reality Meets Computer Vision: Efficient Data Generation for Urban Driving Scenes”, International Journal of Computer Vision, vol. 126, pp. 961–972, 2018.
2019
B. Jähne, “Air-Sea Gas Exchange”, Encyclopedia of Ocean Sciences, vol. 6. Academic Press, pp. 1–13, 2019.
K. E. Krall, Smith, A. W., Takagaki, N., and Jähne, B., “Air–sea gas exchange at wind speeds up to 85 m/s”, Ocean Science, vol. 15, p. 1783-–1799, 2019.
C. Schnörr, “Assignment Flows”, Variational Methods for Nonlinear Geometric Data and Applications. Springer, 2019.
2020
A. Wolny, Cerrone, L., Vijayan, A., Tofanelli, R., Vilches-Barro, A., Louveaux, M., Wenzel, C., Strauss, S., Wilson-Sanchez, D., Lymbouridou, R., Steigleder, S. S., Pape, C., Bailoni, A., Duran-Nebreda, S., Bassel, G. W., Lohmann, J. U., Tsiantis, M., Hamprecht, F. A., Schneitz, K., Maizel, A., and Kreshuk, A., “Accurate and Versatile 3D Segmentation of Plant Tissues at Cellular Resolution”, eLife, vol. 9, 2020.
A. Krull, Hirsch, P., Rother, C., Schiffrin, A., and Krull, C., “Artificial-intelligence-driven scanning probe microscopy”, Communications Physics, vol. 3, 2020.
C. Schnörr, “Assignment Flows”, Handbook of Variational Methods for Nonlinear Geometric Data. Springer, p. 235—260, 2020.
A. Zern, Zeilmann, A., and Schnörr, C., “Assignment Flows for Data Labeling on Graphs: Convergence and Stability”, preprint: arXiv, 2020.
2021
D. Sitenko, Boll, B., and Schnörr, C., “Assignment Flow For Order-Constrained OCT Segmentation”, Int J Computer Vision, vol. 129, 2021.
D. Sitenko, Boll, B., and Schnörr, C., “Assignment Flows and Nonlocal PDEs on Graphs”, GCPR, in press. 2021.
D. Gonzalez-Alvarado, Zeilmann, A., and Schnörr, C., “Assignment Flows and Nonlocal PDEs on Graphs”, GCPR, in press. 2021.
2022
E. Fita, Damrich, S., and Hamprecht, F. A., “The Algebraic Path Problem for Graph Metrics”, 39th International Conference on Machine Learning, PMLR. Proceedings , vol. 162. pp. 19178-19204, 2022.

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