Publications

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Jancsary, J, Nowozin, S and Rother, C (2012). Loss-specific training of non-parametric image restoration models: A new state of the art. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 7578 LNCS 112–125
Janssen, J A M, Calkoen, C J, van Halsema, D, Jähne, B, Janssen, P A E M, Oost, W A, Snoeij, P, Vogelzang, J and Wallbrink, H (1993). The VIERS scatterometer algorithm. Proc. The Air-Sea Interface, Radio and Acoustic Sensing, Turbulence and Wave Dynamics, Marseille, 24--30. June 1993. University of Miami. 749--754
Janssen, J A M, Calkoen, C J, van Halsema, D, Jähne, B, Janssen, P A E M, Oost, W A, Snoeij, P, Vogelzang, J and Wallbrink, H (1996). The VIERS scatterometer algorithm. Proc.\ The Air-Sea Interface, Radio and Acoustic Sensing, Turbulence and Wave Dynamics, Marseille, 24--30. June 1993. RSMAS, University of Miami. 749--754
Jehle, M and Jähne, B (2010). Optimal lighting for defect detection: illumination systems, machine learning, and practical verification. Forum Bildverarbeitung. KIT Scientific Publishing. 241--252. http://digbib.ubka.uni-karlsruhe.de/volltexte/1000020266
Jehle, M and Jähne, B (2010). Optimal Lighting for Defect Detection: Illumination Systems, Machine Learning, and Practical Verification. Forum Bildverarbeitung, Regensburg, 02.-03.12.2010. KIT SCientific Publishing. 301-312
Jehle, M and Jähne, B (2006). Eine neuartige Methode zur raumzeitlichen Analyse von Strömungen in Grenzschichten. Verhandlungen der Deutschen Physikalischen Gesellschaft, Spring Conference, Heidelberg, 15.-17.03.2006. Deutsche Physikalische Gesellschaft. http://www.dpg-verhandlungen.de/2006/heidelberg/up.html
Jehle, M (2011). Hci's Parabolic Lighting Facility - Design And Usage. Heidelberg Collaboratory for Image Processing, University of Heidelberg
Jehle, M and Jähne, B (2008). A novel method for three-dimensional three-component analysis of flow close to free water surfaces. Exp. Fluids. 44 469--480
Jehle, M (2011). Applying Variable Selection To Illumination-Series Data Using The Ilastik Tool. Heidelberg Collaboratory for Image Processing, University of Heidelberg
Jehle, M, Klar, M, Köhler, H - J and Heibaum, M (2004). Bewegungsdetektion und Geschwindigkeitsanalyse in Bildfolgen zur Untersuchung von Sedimentverlagerungen. Mitteilungen des Instituts für Grundbau und Bodenmechanik. 77 371-392
Jehle, M (2006). Spatio-temporal analysis of flows close to water surfaces. IWR, Fakultät für Physik und Astronomie, Univ.\ Heidelberg. http://www.ub.uni-heidelberg.de/archiv/7060/
Jehle, M, Jähne, B and Kertzscher, U (2006). Direct estimation of the wall shear rate using parametric motion models in 3D. Proceedings of the 28th DAGM Symposium on Pattern Recognition. 4174 434--443
Jehle, M, Sommer, C and Jähne, B (2010). Learning of Optimal Illumination for Material Classification. Proceedings of the 32nd DAGM Symposium on Pattern Recognition, Darmstadt, Germany. Springer. 563-572
Jehle, M, Sommer, C and Jähne, B (2010). Learning of optimal illumination for material classification. Pattern Recognition. Springer. 6376 563--572
Jehle, M and Jähne, B (2006). A novel method for spatio-temporal analysis of flows within the water-side viscous boundary layer. 12th Intern. Symp. on Flow Visualization, Göttingen, 10--14. September 2006
Jug, F, Pietzsch, T, Kainmüller, D, Funke, J, Kaiser, M, van Nimwegen, E, Rother, C and Myers, G (2014). Optimal joint segmentation and tracking of escherichia coli in the mother machine. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 8677 25–36
K
Kainmueller, D, Jug, F, Rother, C and Myers, G (2014). Active graph matching for automatic joint segmentation and annotation of C. elegans. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 8673 LNCS 81–88
Kamann, C and Rother, C (2019). Benchmarking the Robustness of Semantic Segmentation Models. http://arxiv.org/abs/1908.05005
Kamann, C and Rother, C (2020). Benchmarking the Robustness of Semantic Segmentation Models. CVPR 2020. http://arxiv.org/abs/1908.05005PDF icon PDF (3.61 MB)
Kandemir, M, Feuchtinger, A, Walch, A and Hamprecht, F A (2014). Digital Pathology: Multiple instance learning can detect Barrett'scancer. ISBI. Proceedings. 1348-1351PDF icon Technical Report (2.86 MB)
Kandemir, M, Klami, A, Gonen, M, Vetek, A and Kaski, S (2014). Multi-task and multi-view learning of user state. Neurocomputing. 139 97-106
Kandemir, M, Rubio, J C, Schmidt, U, Wojek, C, Welbl, J, Ommer, B and Hamprecht, F A (2014). Event Detection by Feature Unpredictability in Phase-Contrast Videos of Cell Cultures. Medical Image Computing and Computer-Assisted Intervention. Springer. 154--161PDF icon Technical Report (2 MB)
Kandemir, M (2015). Asymmetric transfer learning with deep Gaussian processes. ICML. Proceedings. 730-738PDF icon Technical Report (570.95 KB)
Kandemir, M and Hamprecht, F A (2015). The Deep Feed-Forward Gaussian Process: An Effective Generalization to Covariance Priors. NIPS. Proceedings. 44 145-159PDF icon Supplementary Material (223.39 KB)PDF icon Technical Report (2.58 MB)
Kandemir, M and Hamprecht, F A (2014). Instance Label Prediction by Dirichlet Process Multiple Instance Learning. UAI. ProceedingsPDF icon Technical Report (4.26 MB)
Kandemir, M, Hamprecht, F A, Wojek, C and Schmidt, U (2017). Maschinelles Lernen. Patent, Patent Number WO2017032775A1PDF icon Technical Report (317.04 KB)
Kandemir, M and Hamprecht, F A (2014). Computer-aided diagnosis from weak supervision: A benchmarking study. Computerized Medical Imaging and Graphics. 42 44-50PDF icon Technical Report (4.28 MB)
Kandemir, M, Zhang, C and Hamprecht, F A (2014). Empowering multiple instance histopathology cancer diagnosis by cell graphs. MICCAI. Proceedings. Springer. 8674 228-235PDF icon Technical Report (1.76 MB)
Kandemir, M, Haußmann, M, Diego, F, Rajamani, K, van der Laak, J and Hamprecht, F A (2016). Variational weakly-supervised Gaussian processes. BMVC. ProceedingsPDF icon Technical Report (3.28 MB)
Kandemir, M, Hamprecht, F A, Wojek, C and Schmidt, U (2017). Active machine learning for training an event classification. Patent, Patent Number WO2017032775 A1
Kandemir, M and Hamprecht, F A (2015). Cell event detection in phase-contrast microscopy sequences from few annotations. MICCAI. Proceedings. Springer. LNCS 9351 316-323PDF icon Technical Report (564.69 KB)
Kandemir, M, Rubio, J C, Schmidt, U, Welbl, J, Ommer, B and Hamprecht, F A (2014). Event Detection by Feature Unpredictability in Phase-Contrast Videos of Cell Cultures. MICCAI. Proceedings. Springer. 154-161PDF icon Paper (2 MB)
Kandlbinder, T (1994). Gasaustauschmessungen Mit Sauerstoff. Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg
Kannan, A, Winn, J and Rother, C (2007). Clustering appearance and shape by learning jigsaws. Advances in Neural Information Processing Systems. 657–664
Kannan, A, Winn, J and Rother, C (2007). Clustering appearance and shape by learning jigsaws. Advances in Neural Information Processing Systems. 657–664

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