Publications

Search

Show only items where

Author

Type

Term

Year

Keyword

Export 1965 results:

Author Title [ Type

] Year

Filters: Filter is [Clear All Filters]

Conference Paper

B. Jähne, Degreif, K., and Kuss, J., “Wind/wave-tunnel measurements of chemical enhancement of the carbon dioxide gas exchange rate”, in 6th Int. Symp. Gas Transfer at Water Surfaces, Kyoto, May 17--21, 2010, 2010.

O. Blum, Brattoli, B., and Ommer, B., “X-GAN: Improving Generative Adversarial Networks with ConveX Combinations”, in German Conference on Pattern Recognition (GCPR) (Oral), Stuttgart, Germany, 2018.

Article (6.65 MB)

Supplementary material (7.96 MB)

Oral slides (14.96 MB)

C. Zhang, Huber, F., Knop, M., and Hamprecht, F. A., “Yeast Cell Detection and Segmentation in Bright Field Microscopy”, in ISBI. Proceedings, 2014, pp. 1267-1270.

Technical Report (4.13 MB)

C. Schnörr, “Zur Schätzung von Geschwindigkeitsvektorfeldern in Bildfolgen mit einer richtungsabhängigen Glattheitsforderung”, in Mustererkennung 1989, 11. DAGM-Symposium, Hamburg, 1989, vol. 219, pp. 294–301.

B. Jähne, “Zuverlässig, Schnell und Genau? - Bedeutung von Algorithmen in der Bildverarbeitung für die Praxis”, in Bildverarbeitung'95 - Forschen, Entwickeln, Anwenden, 1995, p. 3--14.

Conference Proceedings

A. Vianello, Manfredi, G., Diebold, M., and Jähne, B., “3D Reconstruction by a Combined Structure Tensor and Hough Transform Light-Field Approach”, Forum Bildverarbeitung. 2016.

B. Jähne, “Air-Water Gas Transfer --- Selected Papers from the Third International Symposium on Air-Water Gas Transfer”. AEON, 1995.

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.

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.

M. Kandemir, “Asymmetric transfer learning with deep Gaussian processes”, ICML. Proceedings. pp. 730-738, 2015.

Technical Report (570.95 KB)

M. Haußmann, Gerwinn, S., and Kandemir, M., “Bayesian Evidential Deep Learning with PAC Regularization ”, 3rd Symposium on Advances in Approximate Bayesian Inference . 2020.

A. Blattmann, Milbich, T., Dorkenwald, M., and Ommer, B., “Behavior-Driven Synthesis of Human Dynamics”, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2021.

A. Monroy and Ommer, B., “Beyond Bounding-Boxes: Learning Object Shape by Model-driven Grouping”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 7574. Springer, p. 582--595, 2012.

Technical Report (1.58 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. Peter, Diego, F., Hamprecht, F. A., and Nadler, B., “Cost-efficient Gradient Boosting”, NIPS, poster. 2017.

S. Lang and Ommer, B., “Das Objekt jenseits der Digitalisierung”, Das digitale Objekt, vol. 7. 2020.

lang_ommer_digitalhumanities_2020_.pdf (599.56 KB)

M. Haußmann, Hamprecht, F. A., and Kandemir, M., “Deep Active Learning with Adaptive Acquisition”, IJCAI. Proceedings. pp. 2470-2476, 2019.

Technical Report (137.6 KB)

M. Kandemir and Hamprecht, F. A., “The Deep Feed-Forward Gaussian Process: An Effective Generalization to Covariance Priors”, NIPS. Proceedings, vol. 44. pp. 145-159, 2015.

Supplementary Material (223.39 KB)

Technical Report (2.58 MB)

E. Fita, Damrich, S., and Hamprecht, F. A., “Directed Probabilistic Watershed”, NeurIPS. Proceedings, vol. 34. 2021.

Technical Report (957.78 KB)

E. Kirschbaum, Bailoni, A., and Hamprecht, F. A., “DISCo: Deep Learning, Instance Segmentation, and Correlations for Cell Segmentation in Calcium Imaging”, MICCAI. Proceedings. pp. 151-162, 2020.

P. Esser, Rombach, R., and Ommer, B., “A Disentangling Invertible Interpretation Network for Explaining Latent Representations”, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2020.

Article (13.07 MB)

T. Milbich, Roth, K., Bharadhwaj, H., Sinha, S., Bengio, Y., Ommer, B., and Cohen, J. Paul, “DiVA: Diverse Visual Feature Aggregation for Deep Metric Learning”, IEEE European Conference on Computer Vision (ECCV). 2020.

C. Haubold, Uhlmann, V., Unser, M., and Hamprecht, F. A., “Diverse M-best Solutions by Dynamic Programming”, GCPR. Proceedings, vol. LNCS 10496. Springer, pp. 255-267, 2017.

A. Sanakoyeu, Tschernezki, V., Büchler, U., and Ommer, B., “Divide and Conquer the Embedding Space for Metric Learning”, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2019.

T. Beier, Andres, B., Köthe, U., and Hamprecht, F. A., “An Efficient Fusion Move Algorithm for the Minimum Cost Lifted Multicut Problem”, ECCV. Proceedings, vol. LNCS 9906. Springer, pp. 715-730, 2016.

Technical Report (4.89 MB)

L. Cerrone, Zeilmann, A., and Hamprecht, F. A., “End-to-End Learned Random Walker for Seeded Image Segmentation”, CVPR. Proceedings. pp. 12559-12568, 2019.

T. Hehn and Hamprecht, F. A., “End-to-end Learning of Deterministic Decision Trees”, German Conference on Pattern Recognition. Proceedings, vol. LNCS 11269. Springer, pp. 612-627, 2018.

Technical Report (1.4 MB)

F. Draxler, Veschgini, K., Salmhofer, M., and Hamprecht, F. A., “Essentially No Barriers in Neural Network Energy Landscape”, ICML. Proceedings, vol. 80. p. 1308--1317, 2018.

Technical Report (685.93 KB)

E. Jenner, Fita, E., and Hamprecht, F. A., “Extensions of Karger's Algorithm: Why They Fail in Theory and How They Are Useful in Practice”, ICCV. Proceedings. pp. 4602-4611, 2021.

Technical Report (1.1 MB)

H. Schilling, Diebold, M., Gutsche, M., Aziz-Ahmad, H., and Jähne, B., “A fractal calibration pattern for improved camera calibration”, Forum Bildverarbeitung. 2016.

M. von Borstel, Kandemir, M., Schmidt, P., Rao, M., Rajamani, K., and Hamprecht, F. A., “Gaussian process density counting from weak supervision”, ECCV. Proceedings, vol. LNCS 9905. Springer, pp. 365-380 , 2016.

Technical Report (1.71 MB)

C. Haubold, Ales, J., Wolf, S., and Hamprecht, F. A., “A Generalized Successive Shortest Paths Solver for Tracking Dividing Targets”, ECCV. Proceedings, vol. LNCS 9911. Springer, pp. 566-582, 2016.

Technical Report (1.18 MB)

H. Abu Alhaija, Mustikovela, S. K., Geiger, A., and Rother, C., “Geometric Image Synthesis”, ACCV. Proceedings, in press. 2018.

Technical Report (1.83 MB)

P. Esser, Rombach, R., Blattmann, A., and Ommer, B., “ImageBART: Bidirectional Context with Multinomial Diffusion for Autoregressive Image Synthesis”. 2021.

Pages