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2011
J. H. Kappes, Speth, M., Andres, B., Reinelt, G., and Schnörr, C., “Globally Optimal Image Partitioning by Multicuts”, in EMMCVPR, 2011.PDF icon Technical Report (7.47 MB)
J. Hendrik Kappes, Speth, M., Andres, B., Reinelt, G., and Schnörr, C., “Globally Optimal Image Partitioning by Multicuts”, in EMMCVPR, 2011.
2012
K. Ellen Krall, “Gas Exchange Test Campaign at the Kyoto High Speed Wind-Wave Tank 2011”, Institut für Umweltphysik, Universität Heidelberg, 2012.
B. Jähne, “Gas transfer at water surfaces”, Environmental Fluid Mechanics - Memorial Volume in honour of Professor Gerhard H. Jirka. CRC Press/Balkema, p. 389--404, 2012.
B. Jähne, “Gegenwart und Zukunft der industriellen Bildverarbeitung --- Aufbruch in drei Dimensionen plus”. 2012.
B. Savchynskyy and Schmidt, S., “Getting Feasible Variable Estimates From Infeasible Ones: MRF Local Polytope Study”, arXiv:1210.4081, 2012.
S. Wanner and Goldlücke, B., “Globally Consistent Depth Labeling of 4D Light Fields”, in CVPR. Proceedings, 2012, pp. 41-48.
S. Wanner and Goldlücke, B., “Globally Consistent Depth Labeling of 4D Lightfields”, in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2012.
B. Andres, Kröger, T., Briggmann, K. L., Denk, W., Norogod, N., Knott, G. W., Köthe, U., and Hamprecht, F. A., “Globally Optimal Closed-Surface Segmentation for Connectomics”, in ECCV 2012. Proceedings, Part 3, 2012, pp. 778-791.PDF icon Technical Report (2.72 MB)
R. Nair, Meister, S., Lambers, M., Balda, M., Hofmann, H., Kolb, A., Kondermann, D., and Jähne, B., “Ground truth for evaluating time of flight imaging”, Time-of-Flight and Depth Imaging. Sensors, Algorithms, and Applications, vol. 8200. Springer, p. 52--74, 2012.
2013
B. Savchynskyy and Schmidt, S., “Getting Feasible Variable Estimates From Infeasible Ones: MRF Local Polytope Study”, in Workshop on Inference for Probabilistic Graphical Models at ICCV. Proceedings, 2013.
B. Savchynskyy, Kappes, J. H., Swoboda, P., and Schnörr, C., “Global MAP-Optimality by Shrinking the Combinatorial Search Area with Convex Relaxation”, in NIPS. Proceedings, 2013, pp. 1950-1958.
B. Savchynskyy, Kappes, J. Hendrik, Swoboda, P., and Schnörr, C., “Global MAP-Optimality by Shrinking the Combinatorial Search Area with Convex Relaxation”, in NIPS, 2013.
B. Savchynskyy, Kappes, J. H., Swoboda, P., and Schnörr, C., “Global MAP-Optimality by Shrinking the Combinatorial Search Area with Convex Relaxation”, in NIPS, 2013.PDF icon Technical Report (499.17 KB)
S. Wanner, Straehle, C. N., and Goldlücke, B., “Globally Consistent Multi-Label Assignment on the Ray Space of 4D Light Fields”, CVPR 2013. Proceedings, pp. 1011-1018, 2013.
S. Wanner, Straehle, C. N., and Goldlücke, B., “Globally consistent multi-label assignment on the ray space of 4D light fields”, in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013.
2014
B. Schmitzer and Schnörr, C., “Globally Optimal Joint Image Segmentation and Shape Matching based on Wasserstein Modes”. 2014.PDF icon Technical Report (2.9 MB)
B. Schmitzer and Schnörr, C., “Globally Optimal Joint Image Segmentation and Shape Matching based on Wasserstein Modes”. 2014.
T. Beier, “Graph based image analysis”, University of Heidelberg, 2014.
2015
J. C. Rubio, Eigenstetter, A., and Ommer, B., “Generative Regularization with Latent Topics for Discriminative Object Recognition”, Pattern Recognition, vol. 48, p. 3871--3880, 2015.PDF icon Technical Report (5.49 MB)
B. Schmitzer and Schnörr, C., “Globally Optimal Joint Image Segmentation and Shape Matching based on Wasserstein Modes”, J.~Math.~Imag.~Vision, vol. 52, p. 436--458, 2015.PDF icon Technical Report (1.97 MB)
B. Schmitzer and Schnörr, C., “Globally Optimal Joint Image Segmentation and Shape Matching based on Wasserstein Modes”, J. Math. Imag. Vision, vol. 52, pp. 436–458, 2015.
H. Abu Alhaija, Sellent, A., Kondermann, D., and Rother, C., “Graphflow—6D large displacement scene flow via graph matching”, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2015, vol. 9358, pp. 285–296.
M. Schiegg, Hanslovsky, P., Haubold, C., Köthe, U., Hufnagel, L., and Hamprecht, F. A., “Graphical Model for Joint Segmentation and Tracking of Multiple Dividing Cell”, Bioinformatics, vol. 31, no. 6, pp. 948-956, 2015.PDF icon Technical Report (534.29 KB)
2016
M. von Borstel, Kandemir, M., Schmidt, P., Rao, M., Rajamani, K., and Hamprecht, F. A., “Gaussian process density counting from weak supervision”, ECCV. Proceedings, vol. LNCS 9905. Springer, pp. 365-380 , 2016.PDF icon Technical Report (1.71 MB)
C. Haubold, Ales, J., Wolf, S., and Hamprecht, F. A., “A Generalized Successive Shortest Paths Solver for Tracking Dividing Targets”, ECCV. Proceedings, vol. LNCS 9911. Springer, pp. 566-582, 2016.PDF icon Technical Report (1.18 MB)
F. Aström and Schnörr, C., “A Geometric Approach to Color Image Regularization”. 2016.
F. Aström, Petra, S., Schmitzer, B., and Schnörr, C., “A Geometric Approach to Image Labeling”, in Proc. ECCV, 2016.
2017
F. Aström and Schnörr, C., “A Geometric Approach for Color Image Regularization”, Comp. Vision Image Understanding, vol. 165, pp. 43–59, 2017.
A. Zern, Rohr, K., and Schnörr, C., “Geometric Image Labeling with Global Convex Labeling Constraints”, in Proc. EMMCVPR, 2017.
F. Michel, Kirillov, A., Brachmann, E., Krull, A., Gumhold, S., Savchynskyy, B., and Rother, C., “Global hypothesis generation for 6D object pose estimation”, in Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, 2017, vol. 2017-Janua, pp. 115–124.
M. Zisler, Savarino, F., Petra, S., and Schnörr, C., “Gradient Flows on a Riemannian Submanifold for Discrete Tomography”, in Proc. GCPR, 2017.
2018
A. Zern, Rohr, K., and Schnörr, C., “Geometric Image Labeling with Global Convex Labeling Constraints”, in EMMCVPR, 2018, vol. 10746, pp. 533–547.
H. Abu Alhaija, Mustikovela, S. K., Geiger, A., and Rother, C., “Geometric Image Synthesis”, ACCV. Proceedings, in press. 2018.PDF icon Technical Report (1.83 MB)
A. Zeilmann, Savarino, F., Petra, S., and Schnörr, C., “Geometric Numerical Integration of the Assignment Flow”, preprint: arXiv, 2018.

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