Publications

Search

Show only items where

Author

Type

Term

Year

Keyword

Export 239 results:

] Title Type Year

Filters: First Letter Of Last Name is K [Clear All Filters]

A B C D E F G H I J K L M N O P Q R S T U V W X Y Z

K

Kappes, J H, Petra, S, Schnörr, C and Zisler, M (2015). TomoGC: Binary Tomography by Constrained Graph Cuts. Proc. GCPR

Kappes, J, Swoboda, P, Savchynskyy, B, Hazan, T and Schnörr, C (2015). Probabilistic Correlation Clustering and Image Partitioning Using Perturbed Multicuts. Proc. SSVM. Springer

Kappes, J Hendrik, Beier, T and Schnörr, C (2014). MAP-Inference on Large Scale Higher-Order Discrete Graphical Models by Fusion Moves. International Workshop on Graphical Models in Computer Vision

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 Berlin / Heidelberg. 6313 735–747

Kappes, J H and Schnörr, C (2008). MAP-Inference for Highly-Connected Graphs with DC-Programming. Pattern Recognition – 30th DAGM Symposium. Springer Verlag. 5096 1–10

Kappes, J Hendrik, Speth, M, Andres, B, Reinelt, G and Schnörr, C (2011). Globally Optimal Image Partitioning by Multicuts. EMMCVPR. Springer

Kappes, J Hendrik (2011). Inference on Highly-Connected Discrete Graphical Models with Applications to Visual Object Recognition. Ruprecht-Karls-Universität Heidelberg, Faculty of Mathematics and Computer Sciences, Heidelberg, Germany. http://www.ub.uni-heidelberg.de/archiv/11872/

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. CVPR

Kappes, J Hendrik, Speth, M, Reinelt, G and Schnörr, C (2013). Higher-order Segmentation via Multicuts. ArXiv e-prints

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

Kandlbinder, T (1994). Gasaustauschmessungen Mit Sauerstoff. Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg

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--161

Technical Report (2 MB)

Kandemir, M (2015). Asymmetric transfer learning with deep Gaussian processes. ICML. Proceedings. 730-738

Technical Report (570.95 KB)

Kandemir, M, Feuchtinger, A, Walch, A and Hamprecht, F A (2014). Digital Pathology: Multiple instance learning can detect Barrett'scancer. ISBI. Proceedings. 1348-1351

Technical Report (2.86 MB)

Kandemir, M and Hamprecht, F A (2015). Cell event detection in phase-contrast microscopy sequences from few annotations. MICCAI. Proceedings. Springer. LNCS 9351 316-323

Technical Report (564.69 KB)

Kandemir, M and Hamprecht, F A (2014). Computer-aided diagnosis from weak supervision: A benchmarking study. Computerized Medical Imaging and Graphics. 42 44-50

Technical Report (4.28 MB)

Kandemir, M and Hamprecht, F A (2014). Instance Label Prediction by Dirichlet Process Multiple Instance Learning. UAI. Proceedings

Technical Report (4.26 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, Welbl, J, Ommer, B and Hamprecht, F A (2014). Event Detection by Feature Unpredictability in Phase-Contrast Videos of Cell Cultures. MICCAI. Proceedings. Springer. 154-161

Paper (2 MB)

Kandemir, M, Zhang, C and Hamprecht, F A (2014). Empowering multiple instance histopathology cancer diagnosis by cell graphs. MICCAI. Proceedings. Springer. 8674 228-235

Technical Report (1.76 MB)

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

Supplementary Material (223.39 KB)

Technical Report (2.58 MB)

Kandemir, M, Haußmann, M, Diego, F, Rajamani, K, van der Laak, J and Hamprecht, F A (2016). Variational weakly-supervised Gaussian processes. BMVC. Proceedings

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, Hamprecht, F A, Wojek, C and Schmidt, U (2017). Maschinelles Lernen. Patent, Patent Number WO2017032775A1

Technical Report (317.04 KB)

Kandemir, M, Agkül, A, Haußmann, M and Ünal, G (2021). Evidential Turing Processes. arXiv preprint. https://arxiv.org/abs/2106.01216

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/1908.05005

PDF (3.61 MB)

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

Pages