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Storath, M, Kiefer, L and Weinmann, A (2019). Smoothing for signals with discontinuities using higher order Mumford-Shah models. Numerische Mathematik. 143(2) 423-460PDF icon Technical Report (1.09 MB)
Straehle, C N (2014). Interactive Segmentation, Uncertainty and Learning. University of Heidelberg
Straehle, C N (2011). Interactive Segmentation Of Neural Electron Microscopy Data. University of Heidelberg
Straehle, C N, Köthe, U, Briggman, K, Denk, W and Hamprecht, F A (2012). Seeded watershed cut uncertainty estimators for guided interactive segmentation. CVPR 2012. Proceedings. 765 - 772PDF icon Technical Report (2.84 MB)
Straehle, C N, Köthe, U and Hamprecht, F A (2013). Weakly supervised learning of image partitioning using decision trees with structured split criteria. ICCV 2013. Proceedings. 1849-1856PDF icon Technical Report (5.97 MB)
Straehle, C N, Kandemir, M, Köthe, U and Hamprecht, F A (2014). Multiple instance learning with response-optimized random forests. ICPR. Proceedings. 3768 - 3773PDF icon Technical Report (296.66 KB)
Straehle, C N, Peter, S, Köthe, U and Hamprecht, F A (2013). K-smallest Spanning Tree Segmentations. German Conference on Pattern Recognition (DAGM/GCPR). Proceedings. Springer. 375-384PDF icon Technical Report (1.18 MB)
Straehle, C N, Köthe, U, Knott, G W and Hamprecht, F A (2011). Carving: Scalable Interactive Segmentation of Neural Volume Electron Microscopy Images. MICCAI 2011, Proceedings. Springer. 6891 653-660PDF icon Technical Report (1.69 MB)
Strobel, J, Görlitz, L and Staudacher, M (2005). Verfahren und Prüfkörper zur Bestimmung der Reinigungswirkung in einem Ultraschallbild
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
Strzodka, R and Garbe, C S (2004). Real-time motion estimation and visualization on graphics cards. Proceedings IEEE Visualization 2004. 545--552
Stybalkowski, P (2001). Strömungsmessung In Sedimentporen Mittel 3D Particle Tracking Velocimetry. IWR, Fakultät für Physik und Astronomie, Univ.\ Heidelberg
Suhr, H, Wehnert, G, Schneider, K, Bittner, C, Scholz, T, Geißler, P, Jähne, B and Scheper, T (1995). In-situ microscopy for on-line characterization of cell-populations in bioreactors, including concentration measurements by depth from focus. Biotechnology and Bioengineering. 47 106--116
Sümer, Ö, Dencker, T and Ommer, B (2017). Self-supervised Learning of Pose Embeddings from Spatiotemporal Relations in Videos. Proceedings of the IEEE International Conference on Computer Vision (ICCV)PDF icon Paper (3.98 MB)PDF icon Supplementary Material (3.36 MB)
Swoboda, P, Savchynskyy, B, Kappes, J H and Schnörr, C (2014). Partial Optimality by Pruning for MAP-inference with General Graphical Models. IEEE Conference on Computer Vision and Pattern Recognition 2014PDF icon Technical Report (703.34 KB)
Swoboda, P, Savchynskyy, B, Kappes, J H and Schnörr, C (2013). Partial Optimality via Iterative Pruning for the Potts Model. Scale Space and Variational Methods (SSVM 2013)PDF icon Technical Report (159.71 KB)
Swoboda, P and Schnörr, C (2013). Variational Image Segmentation and Cosegmentation with the Wasserstein Distance. Energy Minimization Methods in Computer Vision and Pattern Recognition. Springer. 8081 321--334PDF icon Technical Report (8.06 MB)
Swoboda, P and Schnörr, C (2013). Convex Variational Image Restoration with Histogram Priors. SIAM J.~Imag.~Sci. 6 1719-1735PDF icon Technical Report (553.54 KB)
Swoboda, P, Kuske, J and Savchynskyy, B (2016). A Dual Ascent Framework for Lagrangean Decomposition of Combinatorial Problems. arXiv, preprint. https://arxiv.org/pdf/1612.05460.pdf
Swoboda, P, Shekhovtsov, A, Kappes, J Hendrik, Schnörr, C and Savchynskyy, B (2016). Partial Optimality by Pruning for MAP-Inference with General Graphical Models. IEEE Transactions on Pattern Analysis and Machine Intelligence. IEEE Computer Society. 38 1370–1382
Swoboda, P, Shekhovtsov, A, Kappes, J H, Schnörr, C and Savchynskyy, B (2016). Partial Optimality by Pruning for MAP-Inference with General Graphical Models. IEEE Trans. Patt. Anal. Mach. Intell. 38 1370–1382
Swoboda, P, Savchynskyy, B, Kappes, J H and Schnörr, C (2014). Partial Optimality by Pruning for MAP-inference with General Graphical Models. IEEE Conference on Computer Vision and Pattern Recognition 2014
Swoboda, P, Savchynskyy, B, Kappes, J H and Schnörr, C (2013). Partial Optimality via Iterative Pruning for the Potts Model. Scale Space and Variational Methods (SSVM 2013)
Swoboda, P and Schnörr, C (2013). Variational Image Segmentation and Cosegmentation with the Wasserstein Distance. Energy Minimization Methods in Computer Vision and Pattern Recognition. Springer. 8081 321–334
Swoboda, P, Savchynskyy, B, Kappes, J H and Schnörr, C (2013). Partial Optimality via Iterative Pruning for the Potts Model. Proceedings of the 4th International Conference on Scale Space and Variational Methods in Computer Vision SSVM. 477-488
Swoboda, P, Savchynskyy, B, Kappes, J H and Schnörr, C (2013). Persistency by Pruning for General Graphical Models. submitted to NIPS 2013
Swoboda, P, Savchynskyy, B, Kappes, J H and Schnörr, C (2014). Partial Optimality by Pruning for MAP-inference with General GraphicalModels. CVPR. Proceedings. 1170-1177
Szeliski, R, Zabih, R, Scharstein, D, Veksler, O, Kolmogorov, V, Agarwala, A, Tappen, M and Rother, C (2008). A comparative study of energy minimization methods for Markov random fields with smoothness-based priors. IEEE Transactions on Pattern Analysis and Machine Intelligence. Springer-Verlag. 30 1068–1080. http://vision.middlebury.edu/MRF.
Szeliski, R, Zabih, R, Scharstein, D, Veksler, O, Kolmogorov, V, Agarwala, A, Tappen, M and Rother, C (2008). A comparative study of energy minimization methods for Markov random fields with smoothness-based priors. IEEE Transactions on Pattern Analysis and Machine Intelligence. 30 1068–1080
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Takami, M, Bell, P and Ommer, B (2014). An Approach to Large Scale Interactive Retrieval of Cultural Heritage. Eurographics Workshop on Graphics and Cultural Heritage. The Eurographics AssociationPDF icon Technical Report (7.94 MB)
Takami, M, Bell, P and Ommer, B (2014). Offline Learning of Prototypical Negatives for Efficient Online Exemplar SVM. Winter Conference on Applications of Computer Vision. IEEE. 377--384. http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6836075
Tamburic, B (2006). Measurement Of The Modulation Transfer Function Through The Use Of Regional Circular Targets. Imperial College London
Tek, B F, Kröger, T, Mikula, S and Hamprecht, F A (2014). Automated Cell Nucleus Detection for Large-Volume Electron Microscopy of Neural Tissue. ISBI. Proceedings. 69-72PDF icon Technical Report (533.92 KB)
Telea, A, Preußer, T, Garbe, C S, Droske, M and Rumpf, M (2006). A variational approach to joint denoising, edge detection and motion estimation. Proceedings of the 28th DAGM Symposium on Pattern Recognition. 525--535. http://numod.ins.uni-bonn.de/research/papers/public/PrDrGa06.pdf
Thieke, C, Nix, O, Koehn, A, Floca, R, van Straaten, D, Hahn, H, Strauss, L G, Siems, U, Graf, M, Pruem, H, Klein, J, Laue, H and Kaster, F O (2009). A framework and multi-application prototype for integrated radiological diagnostics and radiation therapy. Strahlentherapie und Onkologie. 185 81

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