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Publications

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D. Heck, “Proximity Graphs for Nonlinear Dimension Reduction”, University of Heidelberg, 2004.
A. Bailoni, Pape, C., Wolf, S., Kreshuk, A., and Hamprecht, F. A., “Proposal-Free Volumetric Instance Segmentation from Latent Single-Instance Masks”, GCPR, vol. 12544. Springer, pp. 331-344, 2020.
M. Schiegg, Heuer, B., Haubold, C., Wolf, S., Köthe, U., and Hamprecht, F. A., “Proof-reading Guidance in Cell Tracking by Sampling from Tracking-by-assignment Models”, in ISBI. Proceedings, 2015, pp. 394-398.PDF icon Technical Report (648.55 KB)
L. Distributions, “Proof of Lemma 2 Proof of Lemma 3 Proof of Theorem 4 Proof of Lemma 10”, Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, pp. 9–11, 2014.
C. Rother, Carlsson, S., and Tell, D., “Projective factorization of planes and cameras in multiple views”, in Proceedings - International Conference on Pattern Recognition, 2002, vol. 16, pp. 737–740.
L. Görlitz, Menze, B. H., Kelm, B. Michael, and Hamprecht, F. A., “Processing Spectral Data”, Surface and Interface Analysis, vol. 41, pp. 636-644, 2009.PDF icon Technical Report (4.17 MB)
S. Trittler, “Processing of Interferometric Data”. University of Heidelberg, 2007.
R. Chellappa and Machinery., Afor Comput, “Proceedings - 7th Indian Conference on Computer Vision, Graphics and Image Processing, ICVGIP 2010”, ACM International Conference Proceeding Series. ACM, 2010.
F. E Sanmartin, Damrich, S., and Hamprecht, F. A., “Probabilistic Watershed: Sampling all spanning forests for seeded segmentation and semi-supervised learning”, in Advances in Neural Information Processing Systems, 2019.
C. Schellewald and Schnörr, C., “Probabilistic Subgraph Matching Based on Convex Relaxation”, in Proc. Int. Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR'05), 2005, vol. 3757, pp. 171-186.
F. Rathke, Schmidt, S., and Schnörr, C., “Probabilistic Intra-Retinal Layer Segmentation in 3-D OCT Images Using Global Shape Regularization”, Medical Image Analysis, vol. 18, pp. 781-794, 2014.
F. Rathke, Schmidt, S., and Schnörr, C., “Probabilistic Intra-Retinal Layer Segmentation in 3-D OCT Images Using Global Shape Regularization”, Medical Image Analysis, vol. 18, pp. 781-794, 2014.PDF icon Technical Report (4.07 MB)
F. Rathke, Schmidt, S., and Schnörr, C., “Probabilistic Intra-Retinal Layer Segmentation in 3-D OCT Images Using Global Shape Regularization”, Med. Image Anal., vol. 18, pp. 781–794, 2014.
B. Andres, Kappes, J. H., Beier, T., Köthe, U., and Hamprecht, F. A., “Probabilistic Image Segmentation with Closedness Constraints”, in Proceedings of ICCV, 2011.PDF icon Technical Report (2.95 MB)
B. Andres, Kappes, J. H., Beier, T., Köthe, U., and Hamprecht, F. A., “Probabilistic Image Segmentation with Closedness Constraints”, in Proceedings of ICCV, 2011.
B. Andres, Kappes, J. H., Beier, T., Köthe, U., and Hamprecht, F. A., “Probabilistic Image Segmentation with Closedness Constraints”, in ICCV, Proceedings, 2011, pp. 2611 - 2618.PDF icon Technical Report (8.18 MB)
F. Rathke, “Probabilistic Graphical Models for Medical Image Segmentation”. University Heidelberg, 2015.
V. Kolmogorov, Criminisi, A., Blake, A., Cross, G., and Rother, C., “Probabilistic fusion of stereo with color and contrast for bilayer segmentation”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, pp. 1480–1492, 2006.
J. H. Kappes, Swoboda, P., Savchynskyy, B., Hazan, T., and Schnörr, C., “Probabilistic Correlation Clustering and Image Partitioning Using Perturbed Multicuts”, in Proc.~SSVM, 2015.PDF icon Technical Report (1.1 MB)
J. Hendrik Kappes, Swoboda, P., Savchynskyy, B., Hazan, T., and Schnörr, C., “Probabilistic correlation clustering and image partitioning using perturbed Multicuts”, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2015, vol. 9087, pp. 231–242.
J. Kappes, Swoboda, P., Savchynskyy, B., Hazan, T., and Schnörr, C., “Probabilistic Correlation Clustering and Image Partitioning Using Perturbed Multicuts”, in Proc. SSVM, 2015.
T. Hehn, “A probabilistic approach to learn complex differentiable split functions in decision trees using gradient ascent”, Heidelberg University, 2017.
M. Geese, Ruhnau, P., and Jähne, B., “PRNU and DSNU Maximum Likelihood Estimation Using Sensor Statistics”, tm --- Technisches Messen, vol. 80, p. 321--328, 2013.
S. Weber, Schüle, T., and Schnörr, C., “Prior Learning and Convex-Concave Regularization of Binary Tomography”, Electr. Notes in Discr. Math., vol. 20, pp. 313-327, 2005.
B. Jähne, “Prinzipien und Verfahren zur Aufnahme spektraler Bilddaten - Vereinfachte Bildanalyse”, QZ, vol. 53, p. 45--48, 2008.
B. Jähne, Scharr, H., Körkel, S., Jähne, B., Haußecker, H., and Geißler, P., “Principles of Filter Design”, Handbook of Computer Vision and Applications, vol. 2. Academic Press, p. 125--151, 1999.
M. Jäger and Hamprecht, F. A., “Principal Component Imagery for the Quality Monitoring of Dynamic Laser Welding Processes”, IEEE Transactions on Industrial Electronics, vol. 56:4, pp. 1307-1313, 2008.
S. Haller, Prakash, M., Hutschenreiter, L., Pietzsch, T., Rother, C., Jug, F., Swoboda, P., and Savchynskyy, B., “A Primal-Dual Solver for Large-Scale Tracking-by-Assignment”, AISTATS 2020. 2020.PDF icon PDF (1.04 MB)
M. Detert, Jirka, G. H., Jehle, M., Klar, M., Jähne, B., Köhler, H. - J., and Wenka, T., “Pressure fluctuations within subsurface gravel bed caused by turbulent open-channel flow”, in Proc. of River Flow 2004, 2004, pp. 695-701.
F. A. Hamprecht, Jost, D., Rüttimann, M., Calamai, F., and Kowalski, J. J., “Preliminary results on the prediction of countershock success with fibrillation power”, Resuscitation, vol. 50, pp. 297-299, 2001.
B. Jähne, Practical Handbook on Image Processing for Scientific Applications. CRC-Press, Boca Raton, FL, USA, 1997.
B. Jähne, Practical Handbook on Image Processing for Scientific and Technical Applications, 2nd ed. CRC Press, 2004.
C. Kondermann, Kondermann, D., and Garbe, C. S., “Postprocessing of optical flows via surface measures and motion inpainting”, in Pattern Recognition, 2008, vol. 5096, p. 355--364.
C. Kondermann, “Postprocessing and Restoration of Optical Flows”. IWR, Fakultät für Mathematik und Informatik, Univ.\ Heidelberg, 2009.
C. Kondermann, “Postprocessing and Restoration of Optical Flows”. IWR, Fakultät für Mathematik und Informatik, Univ. Heidelberg, 2009.

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