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Rathke, F, Desana, M and Schnörr, C (2017). Locally Adaptive Probabilistic Models for Global Segmentation of Pathological OCT Scans. MICCAI. Proceedings. 177-184

Technical Report (4.79 MB)

Rathke, F, Desana, M and Schnörr, C (2017). Locally Adaptive Probabilistic Models for Global Segmentation of Pathological OCT Scans. Proc. MICCAI

Schimpf, U, Nagel, L and Jähne, B (2011). Lock-in thermography at the ocean surface: a local and fast method to investigate heat and gas exchange between ocean and atmosphere. DPG Frühjahrstagung Dresden, Fachverband Umweltphysik. http://www.dpg-verhandlungen.de/year/2011/conference/dresden/part/up/session/1/contribution/28

Lempitsky, V, Rother, C and Blake, A (2007). LogCut - Efficient graph cut optimization for markov random fields. Proceedings of the IEEE International Conference on Computer Vision

Bremeyer, R (1995). Lokale Orientierung Zur Auswertung Von Streakbildern. Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ. Heidelberg

Fitzenberger, R (1997). Lokale Transformationsmethoden Zur Auswertung Von Wellenneigungsbildern Der Wasseroberfläche Im Bereich Kleinskaliger Oberflächenwellen. Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ. Heidelberg

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

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

Pinggera, P, Ramos, S, Gehrig, S, Franke, U, Rother, C and Mester, R (2016). Lost and found: Detecting small road hazards for self-driving vehicles. IEEE International Conference on Intelligent Robots and Systems. 2016-Novem 1099–1106. http://www.6d-vision.com/lostandfounddataset

Brattoli, B, Büchler, U, Wahl, A - S, Schwab, M E and Ommer, B (2017). LSTM Self-Supervision for Detailed Behavior Analysis. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). (BB and UB contributed equally)

Article (8.75 MB)

Bruhn, A, Weickert, J and Schnörr, C (2005). Lucas/Kanade Meets Horn/Schunck: Combining Local and Global Optic Flow Methods. 61 211-231

Bopp, M (2014). Luft- Und Wasserseitige Strömungsverhältnisse Im Ringförmigen Heidelberger Wind-Wellen-Kanal (Aeolotron). Institut für Umweltphysik, Universität Heidelberg, Germany

Bopp, M (2014). Luft- Und Wasserseitige Strömungsverhältnisse Im Ringförmigen Heidelberger Wind-Wellen-Kanal (Aeolotron). Institut für Umweltphysik, Universität Heidelberg, Germany. http://www.ub.uni-heidelberg.de/archiv/17151

M

Lindner, M (2011). A Machine Learning Approach To Improve Digital Embryo Analysis. University of Heidelberg

Wolf, S (2020). Machine Learning for Instance Segmentation. Heidelberg University

Peter, S (2019). Machine learning under test-time budget constraints. Heidelberg University

Menze, B H, Kelm, B Michael, Heck, D, Lichy, M P and Hamprecht, F A (2006). Machine-based rejection of low quality spectra and estimation of brain tumor probabilities from magnetic resonance spectroscopic images. Bildverarbeitung für die Medizin. 31-36

Technical Report (672.84 KB)

Rombach, R, Esser, P and Ommer, B (2020). Making Sense of CNNs: Interpreting Deep Representations & Their Invariances with INNs. IEEE European Conference on Computer Vision (ECCV). https://compvis.github.io/invariances/

Aström, F, Hühnerbein, R, Savarino, F, Recknagel, J and Schnörr, C (2017). MAP Image Labeling Using Wasserstein Messages and Geometric Assignment. Proc. SSVM. Springer. 10302

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

Technical Report (1.91 MB)

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, 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, 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_37

Technical Report (557.49 KB)

Richmond, D L, Kainmueller, D, Yang, M Y, Myers, E W and Rother, C (2016). Mapping auto-context decision forests to deep convnets for semantic segmentation. British Machine Vision Conference 2016, BMVC 2016. 2016-Septe 144.1–144.12. http://arxiv.org/abs/1507.07583

Richmond, D L, Kainmueller, D, Yang, M Y, Myers, E W and Rother, C (2016). Mapping auto-context decision forests to deep convnets for semantic segmentation. British Machine Vision Conference 2016, BMVC 2016. 2016-Septe 144.1–144.12. https://github.com/BVLC/caffe/wiki/Model-Zoo\#fcn

Richmond, D L, Kainmueller, D, Yang, M Y, Myers, E W and Rother, C (2016). Mapping auto-context decision forests to deep convnets for semantic segmentation. British Machine Vision Conference 2016, BMVC 2016. 2016-Septe 144.1–144.12

Strouse, T M D (2016). Marijuana's Public Health Pros and Cons | For Better | US News. U.S. News and World Report. http://health.usnews.com/health-news/patient-advice/articles/2016-10-12/marijuanas-public-health-pros-and-cons

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

Technical Report (317.04 KB)

Balluff, B, Hanselmann, M and Heeren, R M A (2017). Mass spectrometry imaging for the investigation of intratumor heterogeneity. Advances in Cancer Research. Elsevier. 134 201-230

Spies, H, Haußecker, H and Köhler, H - J (2000). Material transport and structure changes at soil-water interfaces. Filters and Drainage in Geotechnical and Environmental Engineering. 91--97

Erb, W, Weinmann, A, Ahlborg, M, Brandt, C, Bringout, G, Buzug, T M, Frikel, J, Kaethner, C, Knopp, T, März, T, Möddel, M, Storath, M and Weber, A (2018). Mathematical Analysis of the 1D Model and Reconstruction Schemes for Magnetic Particle Imaging. Inverse Problems. 34

Welk, M, Becker, F, Schnörr, C and Weickert, J (2005). Matrix-Valued Filters as Convex Programs. Scale-Space 2005. Springer. 3459 204–216

Shekhovtsov, A, Swoboda, P and Savchynskyy, B (2018). Maximum Persistency via Iterative Relaxed Inference in Graphical Models. IEEE Transactions on Pattern Analysis and Machine Intelligence. 40 1668–1682. http://www.icg.tugraz.at/

Eigenstetter, A, Yarlagadda, P and Ommer, B (2012). Max-Margin Regularization for Reducing Accidentalness in Chamfer Matching. Proceedins of the Aian Conference on Computer Vision. Springer. 152--163

Technical Report (7.31 MB)

Kirillov, A, Schlesinger, D, Vetrov, D, Rother, C and Savchynskyy, B (2015). M-best-diverse labelings for submodular energies and beyond. Advances in Neural Information Processing Systems. 2015-Janua 613–621

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