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A. Dutzi, “Untersuchungen zum Einfluss der Temperatur auf den Gasaustausch”, Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg, 1984.
G. Balschbach, “Untersuchungen statistischer und geometrischer Eigenschaften von Windwellen und ihrer Wechselwirkung mit der wasserseitigen Grenzschicht”. Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg, 2000.
C. Popp, “Untersuchung von Austauschprozessen an der Wasseroberfläche aus Infrarot-Bildsequenzen mittels frequenzmodulierter Wärmeeinstrahlung”. Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg, 2006.
H. Ehrler, “Untersuchung physiologischer Parameter und Prozesse in Pflanzen mit Hilfe von Bildverarbeitung”, IWR, Fakultät für Physik und Astronomie, Univ. Heidelberg, 1999.
A. Fachat, “Untersuchung eines 3D-Aufnahmeverfahrens für Strömungsvorgänge an der Wasseroberfläche”, Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ. Heidelberg, 1995.
U. Schimpf, “Untersuchung des Gasaustausches und der Mikroturbulenz an der Meeresoberfläche mittels Thermographie”. Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg, 2000.
M. Beyer, “Untersuchung der Relaxationszeiten von winderzeugten Wasseroberflächenwellen durch periodische Variation der Windgeschwindigkeit”, Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg, 1993.
H. Lauer, “Untersuchung der Neigungsstatistik von Wasseroberflächenwellen mittels eines schnellen, bildaufnehmenden Verfahrens”. IWR, Fakultät für Physik und Astronomie, Univ.\ Heidelberg, 1998.
T. Milbich, Bautista, M., Sutter, E., and Ommer, B., “Unsupervised Video Understanding by Reconciliation of Posture Similarities”, in Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2017.
P. Esser, Haux, J., and Ommer, B., “Unsupervised Robust Disentangling of Latent Characteristics for Image Synthesis”, in Proceedings of the Intl. Conf. on Computer Vision (ICCV), 2019.
T. Milbich, Ghori, O., and Ommer, B., “Unsupervised Representation Learning by Discovering Reliable Image Relations”, Pattern Recognition, vol. 102, 2020.
D. Lorenz, Bereska, L., Milbich, T., and Ommer, B., “Unsupervised Part-Based Disentangling of Object Shape and Appearance”, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (Oral + Best paper finalist: top 45 / 5160 submissions), 2019.
S. Braun, Esser, P., and Ommer, B., “Unsupervised Part Discovery by Unsupervised Disentanglement”, Proceedings of the German Conference on Pattern Recognition (GCPR) (Oral). Tübingen, 2020.
M. Dorkenwald, Büchler, U., and Ommer, B., “Unsupervised Magnification of Posture Deviations Across Subjects”, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2020.PDF icon article.pdf (1.15 MB)
M. Zisler, Zern, A., Petra, S., and Schnörr, C., “Unsupervised Labeling by Geometric and Spatially Regularized Self-Assignment”, in Proc. SSVM, 2019.
A. Zern, Zisler, M., Aström, F., Petra, S., and Schnörr, C., “Unsupervised Label Learning on Manifolds by Spatially Regularized Geometric Assignment”, GCPR. Proceedings. pp. 698-713, 2018.PDF icon Technical Report (5.23 MB)
A. Zern, Zisler, M., Aström, F., Petra, S., and Schnörr, C., “Unsupervised Label Learning on Manifolds by Spatially Regularized Geometric Assignment”, in GCPR, 2018.
J. Keuchel, Schnörr, C., Schellewald, C., and Cremers, D., “Unsupervised Image Partitioning with Semidefinite Programming”, in Pattern Recognition, Proc. 24th DAGM Symposium, Zürich, Switzerland, 2002, vol. 2449, pp. 141–149.
B. Brattoli, Büchler, U., Dorkenwald, M., Reiser, P., Filli, L., Helmchen, F., Wahl, A. - S., and Ommer, B., “Unsupervised behaviour analysis and magnification (uBAM) using deep learning”, Nature Machine Intelligence, 2021.
A. Zern, Zisler, M., Petra, S., and Schnörr, C., “Unsupervised Assignment Flow: Label Learning on Feature Manifolds by Spatially Regularized Geometric Assignment”, Journal of Mathematical Imaging and Vision, 2020.
A. Zern, Zisler, M., Petra, S., and Schnörr, C., “Unsupervised Assignment Flow: Label Learning on Feature Manifolds by Spatially Regularized Geometric Assignment”, preprint: arXiv, 2019.
C. Schnörr, “Unique Reconstruction of Piecewise Smooth Images by Minimizing Strictly Convex Non-Quadratic Functionals”, vol. 4, pp. 189–198, 1994.
A. Lifermann, Jähne, B., and Ramamonjiarisoa, A., “Une ètude en soufflerie de la rèflexion des hyperfrèquences par des champs de houles et de vagues”, Oceanologia Acta, vol. SP, p. 15--22, 1987.
D. S. Kirk, Sellen, A. J., Rother, C., and Wood, K. R., “Understanding photowork”, in Conference on Human Factors in Computing Systems - Proceedings, 2006, vol. 2, pp. 761–770.
A. Blattmann, Milbich, T., Dorkenwald, M., and Ommer, B., “Understanding Object Dynamics for Interactive Image-to-Video Synthesis”, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2021.
M. Bellagente, Haußmann, M., Luchmann, M., and Plehn, T., “Understanding Event-Generation Networks via Uncertainties”. arXiv preprint, 2021.
D. Richmond, Kainmueller, D., Glocker, B., Rother, C., and Myers, G., “Uncertainty-driven forest predictors for vertebra localization and segmentation”, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9349. pp. 653–660, 2015.
E. Brachmann, Michel, F., Krull, A., Yang, M. Ying, Gumhold, S., and Rother, C., “Uncertainty-Driven 6D Pose Estimation of Objects and Scenes from a Single RGB Image”, in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016, vol. 2016-Decem, pp. 3364–3372.
E. Brachmann, Michel, F., Krull, A., Yang, M. Ying, Gumhold, S., and Rother, C., “Uncertainty-Driven 6D Pose Estimation of Objects and Scenes from a Single RGB Image”, in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016, vol. 2016-Decem, pp. 3364–3372.
S. Damrich and Hamprecht, F. A., “On UMAP's True Loss Function”, NeurIPS. Proceedings, vol. 34. 2021.PDF icon Technical Report (1.87 MB)
S. Damrich and Hamprecht, F. H., “UMAP does not reproduce high-dimensional similarities due to negative sampling”. arXiv preprint, 2021.
T
W. Peckar, Schnörr, C., Rohr, K., and Stiehl, H. S., “Two-Step Parameter-Free Elastic Image Registration with Prescribed Point Displacements”, in Proc. 9th Int. Conf. on Image Analysis and Processing (ICIAP'97), Florence, Italy, 1997.
S. Hader and Hamprecht, F. A., “Two-Stage Classification with Automatic Feature Selection for an Industrial Application”, Classification, the ubiquitous challenge: Proceedings of GfKl 2004. Springer, pp. 137-144, 2004.PDF icon Technical Report (518.16 KB)
B. Jähne and Riemer, K., “Two-dimensional wave number spectra of small-scale water surface waves”, J. Geophys. Res., vol. 95, p. 11531--11646, 1990.
H. Schilling, Diebold, M., Rother, C., and Jähne, B., “Trust your Model: Light Field Depth Estimation with inline Occlusion Handling”, CVPR. Proceedings. 2018.PDF icon Technical Report (5.46 MB)

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