Publications

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P. Swoboda, Shekhovtsov, A., Kappes, J. Hendrik, Schnörr, C., and Savchynskyy, B., Partial Optimality by Pruning for MAP-Inference with General Graphical Models, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 38, pp. 1370–1382, 2016.
P. Swoboda, Shekhovtsov, A., Kappes, J. H., Schnörr, C., and Savchynskyy, B., Partial Optimality by Pruning for MAP-Inference with General Graphical Models, IEEE Trans. Patt. Anal. Mach. Intell., vol. 38, pp. 1370–1382, 2016.
P. Swoboda, Savchynskyy, B., Kappes, J. H., and Schnörr, C., Partial Optimality by Pruning for MAP-inference with General Graphical Models, in IEEE Conference on Computer Vision and Pattern Recognition 2014, 2014.
P. Swoboda, Savchynskyy, B., Kappes, J. H., and Schnörr, C., Partial Optimality via Iterative Pruning for the Potts Model, in Scale Space and Variational Methods (SSVM 2013), 2013.
P. Swoboda and Schnörr, C., Variational Image Segmentation and Cosegmentation with the Wasserstein Distance, in Energy Minimization Methods in Computer Vision and Pattern Recognition, 2013, vol. 8081, pp. 321–334.
P. Swoboda, Savchynskyy, B., Kappes, J. H., and Schnörr, C., Partial Optimality via Iterative Pruning for the Potts Model, in Proceedings of the 4th International Conference on Scale Space and Variational Methods in Computer Vision SSVM, 2013, pp. 477-488.
P. Swoboda, Savchynskyy, B., Kappes, J. H., and Schnörr, C., Persistency by Pruning for General Graphical Models, in submitted to NIPS 2013., 2013.
P. Swoboda, Savchynskyy, B., Kappes, J. H., and Schnörr, C., Partial Optimality by Pruning for MAP-inference with General GraphicalModels, CVPR. Proceedings. pp. 1170-1177, 2014.
P. Swoboda, Savchynskyy, B., Kappes, J. H., and Schnörr, C., Partial Optimality by Pruning for MAP-inference with General Graphical Models, in IEEE Conference on Computer Vision and Pattern Recognition 2014, 2014.PDF icon Technical Report (703.34 KB)
P. Swoboda, Savchynskyy, B., Kappes, J. H., and Schnörr, C., Partial Optimality via Iterative Pruning for the Potts Model, in Scale Space and Variational Methods (SSVM 2013), 2013.PDF icon Technical Report (159.71 KB)
P. Swoboda and Schnörr, C., Variational Image Segmentation and Cosegmentation with the Wasserstein Distance, in Energy Minimization Methods in Computer Vision and Pattern Recognition, 2013, vol. 8081, p. 321--334.PDF icon Technical Report (8.06 MB)
P. Swoboda and Schnörr, C., Convex Variational Image Restoration with Histogram Priors, SIAM J.~Imag.~Sci., vol. 6, pp. 1719-1735, 2013.PDF icon Technical Report (553.54 KB)
P. Swoboda, Kuske, J., and Savchynskyy, B., A Dual Ascent Framework for Lagrangean Decomposition of Combinatorial Problems, arXiv, preprint, 2016.
Ö. Sümer, Dencker, T., and Ommer, B., Self-supervised Learning of Pose Embeddings from Spatiotemporal Relations in Videos, in Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2017.PDF icon Paper (3.98 MB)PDF icon Supplementary Material (3.36 MB)
H. Suhr, Wehnert, G., Schneider, K., Bittner, C., Scholz, T., Geißler, P., Jähne, B., and Scheper, T., In-situ microscopy for on-line characterization of cell-populations in bioreactors, including concentration measurements by depth from focus, Biotechnology and Bioengineering, vol. 47, p. 106--116, 1995.
P. Stybalkowski, Strömungsmessung in Sedimentporen mittel 3D Particle Tracking Velocimetry, IWR, Fakultät für Physik und Astronomie, Univ.\ Heidelberg, 2001.
R. Strzodka and Garbe, C. S., Real-time motion estimation and visualization on graphics cards, in Proceedings IEEE Visualization 2004, 2004, p. 545--552.
T. M. D. Strouse, Marijuana's Public Health Pros and Cons | For Better | US News, U.S. News and World Report, 2016.
J. Strobel, Görlitz, L., and Staudacher, M., Verfahren und Prüfkörper zur Bestimmung der Reinigungswirkung in einem Ultraschallbild. 2005.
C. N. Straehle, Interactive Segmentation, Uncertainty and Learning. University of Heidelberg, 2014.
C. N. Straehle, Interactive Segmentation of Neural Electron Microscopy Data, University of Heidelberg, 2011.
C. N. Straehle, Köthe, U., Briggman, K., Denk, W., and Hamprecht, F. A., Seeded watershed cut uncertainty estimators for guided interactive segmentation, in CVPR 2012. Proceedings, 2012, pp. 765 - 772.PDF icon Technical Report (2.84 MB)
C. N. Straehle, Köthe, U., and Hamprecht, F. A., Weakly supervised learning of image partitioning using decision trees with structured split criteria, in ICCV 2013. Proceedings, 2013, pp. 1849-1856.PDF icon Technical Report (5.97 MB)
C. N. Straehle, Kandemir, M., Köthe, U., and Hamprecht, F. A., Multiple instance learning with response-optimized random forests, in ICPR. Proceedings, 2014, pp. 3768 - 3773.PDF icon Technical Report (296.66 KB)
C. N. Straehle, Peter, S., Köthe, U., and Hamprecht, F. A., K-smallest Spanning Tree Segmentations, in German Conference on Pattern Recognition (DAGM/GCPR). Proceedings, 2013, pp. 375-384.PDF icon Technical Report (1.18 MB)
C. N. Straehle, Köthe, U., Knott, G. W., and Hamprecht, F. A., Carving: Scalable Interactive Segmentation of Neural Volume Electron Microscopy Images, in MICCAI 2011, Proceedings., 2011, vol. 6891, pp. 653-660.PDF icon Technical Report (1.69 MB)
M. Storath, Brandt, C., Hofmann, M., Knopp, T., Salamon, J., Weber, A., and Weinmann, A., Edge preserving and noise reducing reconstruction for magnetic particle imaging, IEEE Transactions on Medical Imaging, vol. 36, no. 1, pp. 74 - 85, 2017.PDF icon Technical Report (1.43 MB)
M. Storath, Weinmann, A., and Unser, M., Jump-penalized least absolute values estimation of scalar or circle-valued signals, Information and Inference, vol. 6, no. 3, pp. 225–245, 2017.PDF icon Technical Report (3.4 MB)
M. Storath and Weinmann, A., Fast median filtering for phase or orientation data, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 40, no. 3, pp. 639–652, 2018.PDF icon Technical Report (7.32 MB)
M. Storath, Rickert, D., Unser, M., and Weinmann, A., Fast segmentation from blurred data in 3D fluorescence microscopy, IEEE Transactions on Image Processing, vol. 26, no. 10, 2017.
M. Storath, Kiefer, L., and Weinmann, A., Smoothing for signals with discontinuities using higher order Mumford-Shah models, Numerische Mathematik, vol. 143(2), pp. 423-460, 2019.PDF icon Technical Report (1.09 MB)
A. Stolz, Infrarot-Absorptionsspektroskopie zur Bestimmung der Luftkonzentration von Spurengasen, Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg, 1997.
M. Stöhr, Analysis of Flow and Transport in Refractive Index Matched Porous Media. Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg, 2003.
M. Stöhr, Entwicklung dreidimensionaler Particle Tracking Velocimetry zur Messung der Zweiphasenströmung in Gas-Flüssig-Reaktoren, University of Heidelberg, 1998.
M. Stöhr, Garbe, C. S., Engelmann, D., Geißler, P., Gomes, S., Hering, F., Jähne, B., Keil, F., Mackens, W., Voß, H., and Wagner, H. - G., 4D particle tracking velocimetry applied to gas-liquid reactors, in In Scientific Computing in Chemical Engineering II - Simulation, Image Processing, Optimization and Control., J. Werther, Ed. Springer Verlag, 1999, p. 270--279.

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