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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 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 (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 (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). Hci's Parabolic Lighting Facility - Design And Usage. 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
K
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, Hamprecht, F A, Wojek, C and Schmidt, U (2017). Maschinelles Lernen. Patent, Patent Number WO2017032775A1PDF icon Technical Report (317.04 KB)
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 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, Hamprecht, F A, Wojek, C and Schmidt, U (2017). Active machine learning for training an event classification. Patent, Patent Number WO2017032775 A1
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)
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)
Kandlbinder, T (1994). Gasaustauschmessungen Mit Sauerstoff. Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg
Kappes, J H, Schmidt, S and Schnörr, C (2010). MRF Inference by k-Fan Decomposition and Tight Lagrangian Relaxation. European Conference on Computer Vision (ECCV). Springer. 6313 735--747
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 H, Speth, M, Reinelt, G and Schnörr, C (2013). Higher-order Segmentation via Multicuts. ArXiv e-printsPDF icon Technical Report (1.07 MB)
Kappes, J H (2011). Inference on Highly-Connected Discrete Graphical Models with Applications to Visual Object Recognition. Ruprecht-Karls-Universität Heidelberg, Faculty of Mathematics and Computer Sciences. http://www.ub.uni-heidelberg.de/archiv/11872/
Kappes, J H, Savchynskyy, B and Schnörr, C (2012). A Bundle Approach To Efficient MAP-Inference by Lagrangian Relaxation. CVPR. Proceedings. 1688-1695
Kappes, J H, Petra, S, Schnörr, C and Zisler, M (2015). TomoGC: Binary Tomography by Constrained Graph Cuts. Proc.~GCPRPDF icon Technical Report (2.46 MB)
Kappes, J H, Speth, M, Reinelt, G and Schnörr, C (2013). Towards Efficient and Exact MAP-Inference for Large Scale Discrete Computer Vision Problems via Combinatorial Optimization. CVPRPDF icon Technical Report (623.84 KB)
Kappes, J H, Speth, M, Andres, B, Reinelt, G and Schnörr, C (2011). Globally Optimal Image Partitioning by Multicuts. EMMCVPR. SpringerPDF icon Technical Report (7.47 MB)
Kappes, J H, Andres, B, Hamprecht, F A, Schnörr, C, Nowozin, S, Batra, D, Kim, S, Kausler, B X, Kröger, T, Lellmann, J, Komodakis, N, Savchynskyy, B and Rother, C (2014). A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems. CoRR. http://arxiv.org/abs/1404.0533
Kappes, J H, Andres, B, Hamprecht, F A, Schnörr, C, Nowozin, S, Batra, D, Kim, S, Kausler, B X, Kröger, T, Lellmann, J, Komodakis, N, Savchynskyy, B and Rother, C (2015). A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems. Int.~J.~Comp.~VisionPDF icon Technical Report (5.12 MB)
Kappes, J H, Speth, M, Andres, B, Reinelt, G and Schnörr, C (2011). Globally Optimal Image Partitioning by Multicuts. EMMCVPR. Springer. 31-44PDF icon Technical Report (7.3 MB)
Kappes, J H, Savchynskyy, B and Schnörr, C (2012). A Bundle Approach To Efficient MAP-Inference by Lagrangian Relaxation. CVPRPDF icon Technical Report (430.63 KB)
Kappes, J H and Schnörr, C (2008). MAP-Inference for Highly-Connected Graphs with DC-Programming. Pattern Recognition -- 30th DAGM Symposium. Springer Verlag. 5096 1--10PDF icon Technical Report (1.91 MB)
Kappes, J H, Beier, T and Schnörr, C (2014). MAP-Inference on Large Scale Higher-Order Discrete Graphical Models by Fusion Moves. Computer Vision - {ECCV} 2014 Workshops - Zurich, Switzerland, September 6-7 and 12, 2014, Proceedings, Part {II}. http://dx.doi.org/10.1007/978-3-319-16181-5_37PDF icon Technical Report (557.49 KB)
Kappes, J H, Andres, B, Hamprecht, F A, Schnörr, C, Nowozin, S, Batra, D, Sungwoong, K, Kausler, B X, Lellmann, J, Komodakis, N and Rother, C (2013). A Comparative Study of Modern Inference Techniques for Discrete Energy Minimization Problems. CVPR 2013. ProceedingsPDF icon Technical Report (1.35 MB)

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