Publications

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Detert, M, Jirka, G H, Jehle, M, Klar, M, Jähne, B, Köhler, H - J and Wenka, T (2004). Pressure fluctuations within subsurface gravel bed caused by turbulent open-channel flow. Proc. of River Flow 2004. A. A. Balkema Publishers. 695-701
Haller, S, Prakash, M, Hutschenreiter, L, Pietzsch, T, Rother, C, Jug, F, Swoboda, P and Savchynskyy, B (2020). A Primal-Dual Solver for Large-Scale Tracking-by-Assignment. AISTATS 2020PDF icon PDF (1.04 MB)
Jäger, M and Hamprecht, F A (2008). Principal Component Imagery for the Quality Monitoring of Dynamic Laser Welding Processes. IEEE Transactions on Industrial Electronics. 56:4 1307-1313
Jähne, B, Scharr, H, Körkel, S, Jähne, B, Haußecker, H and Geißler, P (1999). Principles of Filter Design. Handbook of Computer Vision and Applications. Academic Press. 2 125--151
Jähne, (2008). Prinzipien und Verfahren zur Aufnahme spektraler Bilddaten - Vereinfachte Bildanalyse. QZ. 53 45--48
Weber, S, Schüle, T and Schnörr, C (2005). Prior Learning and Convex-Concave Regularization of Binary Tomography. Electr. Notes in Discr. Math. 20 313-327
Geese, M, Ruhnau, P and Jähne, B (2013). PRNU and DSNU Maximum Likelihood Estimation Using Sensor Statistics. tm --- Technisches Messen. 80 321--328
Hehn, T (2017). A Probabilistic Approach To Learn Complex Differentiable Split Functions In Decision Trees Using Gradient Ascent. Heidelberg University
Kappes, J H, Swoboda, P, Savchynskyy, B, Hazan, T and Schnörr, C (2015). Probabilistic Correlation Clustering and Image Partitioning Using Perturbed Multicuts. Proc.~SSVM. SpringerPDF icon Technical Report (1.1 MB)
Kappes, J Hendrik, Swoboda, P, Savchynskyy, B, Hazan, T and Schnörr, C (2015). Probabilistic correlation clustering and image partitioning using perturbed Multicuts. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 9087 231–242
Kappes, J, Swoboda, P, Savchynskyy, B, Hazan, T and Schnörr, C (2015). Probabilistic Correlation Clustering and Image Partitioning Using Perturbed Multicuts. Proc. SSVM. Springer
Kolmogorov, V, Criminisi, A, Blake, A, Cross, G and Rother, C (2006). Probabilistic fusion of stereo with color and contrast for bilayer segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence. 28 1480–1492. http://research.microsoft.com/vision/cambridge
Rathke, F (2015). Probabilistic Graphical Models for Medical Image Segmentation. University Heidelberg
Andres, B, Kappes, J H, Beier, T, Köthe, U and Hamprecht, F A (2011). Probabilistic Image Segmentation with Closedness Constraints. Proceedings of ICCVPDF icon Technical Report (2.95 MB)
Andres, B, Kappes, J H, Beier, T, Köthe, U and Hamprecht, F A (2011). Probabilistic Image Segmentation with Closedness Constraints. Proceedings of ICCV
Andres, B, Kappes, J H, Beier, T, Köthe, U and Hamprecht, F A (2011). Probabilistic Image Segmentation with Closedness Constraints. ICCV, Proceedings. 2611 - 2618PDF icon Technical Report (8.18 MB)
Rathke, F, Schmidt, S and Schnörr, C (2014). Probabilistic Intra-Retinal Layer Segmentation in 3-D OCT Images Using Global Shape Regularization. Med. Image Anal. 18 781–794
Rathke, F, Schmidt, S and Schnörr, C (2014). Probabilistic Intra-Retinal Layer Segmentation in 3-D OCT Images Using Global Shape Regularization. Medical Image Analysis. 18 781-794PDF icon Technical Report (4.07 MB)
Rathke, F, Schmidt, S and Schnörr, C (2014). Probabilistic Intra-Retinal Layer Segmentation in 3-D OCT Images Using Global Shape Regularization. Medical Image Analysis. 18 781-794
Schellewald, C and Schnörr, C (2005). Probabilistic Subgraph Matching Based on Convex Relaxation. Proc. Int. Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR'05). Springer. 3757 171-186
E Sanmartin, F, Damrich, S and Hamprecht, F A (2019). Probabilistic Watershed: Sampling all spanning forests for seeded segmentation and semi-supervised learning. Advances in Neural Information Processing Systems
Chellappa, R and Machinery., Afor Comput (2010). Proceedings - 7th Indian Conference on Computer Vision, Graphics and Image Processing, ICVGIP 2010. ACM International Conference Proceeding Series. ACM
Trittler, S (2007). Processing of Interferometric Data. University of Heidelberg
Görlitz, L, Menze, B H, Kelm, B Michael and Hamprecht, F A (2009). Processing Spectral Data. Surface and Interface Analysis. 41 636-644PDF icon Technical Report (4.17 MB)
Rother, C, Carlsson, S and Tell, D (2002). Projective factorization of planes and cameras in multiple views. Proceedings - International Conference on Pattern Recognition. 16 737–740
Distributions, L (2014). 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. 9–11
Schiegg, M, Heuer, B, Haubold, C, Wolf, S, Köthe, U and Hamprecht, F A (2015). Proof-reading Guidance in Cell Tracking by Sampling from Tracking-by-assignment Models. ISBI. Proceedings. 394-398PDF icon Technical Report (648.55 KB)
Bailoni, A, Pape, C, Wolf, S, Kreshuk, A and Hamprecht, F A (2020). Proposal-Free Volumetric Instance Segmentation from Latent Single-Instance Masks. GCPR. Springer. 12544 331-344
Heck, D (2004). Proximity Graphs For Nonlinear Dimension Reduction. University of Heidelberg
Pletscher, P, Nowozin, S, Kohli, P and Rother, C (2011). Putting MAP back on the map. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 6835 LNCS 111–121
Pletscher, P, Nowozin, S, Kohli, P and Rother, C (2011). Putting MAP back on the map. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 6835 LNCS 111–121

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