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2017
A. - S. Wahl, Büchler, U., Brändli, A., Brattoli, B., Musall, S., Kasper, H., Ineichen, B. V., Helmchen, F., Ommer, B., and Schwab, M. E., “Optogenetically stimulating the intact corticospinal tract post-stroke restores motor control through regionalized functional circuit formation”, Nature Communications, p. (ASW & UB contributed equally; BO and MES contributed equally), 2017.
A. Krull, Brachmann, E., Nowozin, S., Michel, F., Shotton, J., and Rother, C., “PoseAgent: Budget-constrained 6D object pose estimation via reinforcement learning”, in Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, 2017, vol. 2017-Janua, pp. 2566–2574.
D. Massiceti, Krull, A., Brachmann, E., Rother, C., and Torr, P. H. S., “Random Forests versus Neural Networks − What's best for camera location”. 2017.
P. Markowsky, Reith, S., Zuber, T. E., König, R., Rohr, K., and Schnörr, C., “Segmentation of cell structure using model-based set covering with iterative reweighting”, in Proc. ISBI, 2017.
M. Hullin, Klein, R., Schultz, T., Yao, A., Li, W., Hosseini Jafari, O., and Rother, C., “Semantic-Aware Image Smoothing”, Vision, Modeling, and Visualization, 2017.
C. Pape, Beier, T., Li, P., Jain, V., Brock, D. D., and Kreshuk, A., “Solving Large Multicut Problems for Connectomics via Domain Decomposition”, Bioimage Computing Workshop. ICCV. pp. 1-10, 2017.
S. Peter, Kirschbaum, E., Both, M., Campbell, L. A., Harvey, B. K., Heins, C., Durstewitz, D., Diego, F., and Hamprecht, F. A., “Sparse convolutional coding for neuronal assembly detection”, NIPS, poster. 2017.
M. Haußmann, Hamprecht, F. A., and Kandemir, M., “Variational Bayesian Multiple Instance Learning with Gaussian Processes”, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp. 6570-6579, 2017.PDF icon Technical Report (1.29 MB)
2016
J. Kunz and Jähne, B., “Active thermography as a tool to investigate heat and gas transfer across the air-water interface”, in 13th Quantitative Infrared Thermographie Conference (QIRT 2016), Gdansk 4–8 July 2016, 2016.
N. Krasowski, “Automated Segmentation for Connectomics Utilizing Higher-Order Biological Priors”. University of Heidelberg, 2016.
L. A. Royer, Richmond, D. L., Rother, C., Andres, B., and Kainmueller, D., “Convexity shape constraints for image segmentation”, in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016, vol. 2016-Decem, pp. 402–410.
B. Güssefeld, Honauer, K., and Kondermann, D., “Creating Feasible Reflectance Data for Synthetic Optical Flow Datasets”, in Advances in Visual Computing - 12th International Symposium, {ISVC} 2016, Las Vegas, NV, USA, December 12-14, 2016, Proceedings, Part {I}, 2016.
K. Honauer, Johannsen, O., Kondermann, D., and Goldlücke, B., “A Dataset and Evaluation Methodology for Depth Estimation on 4D Light Fields”, in Computer Vision - ACCV 2016 : 13th Asian Conference on Computer Vision, Taipei, Taiwan, November 20-24, 2016, Revised Selected Papers, Part III, Cham, 2016.
J. Kleesiek, Urban, G., Hubert, A., Schwarz, D., Maier-Hein, K., Bendszus, M., and Biller, A., “Deep MRI brain extraction: A 3D convolutional neural network for skull stripping.”, NeuroImage, vol. 129, pp. 460-469, 2016.PDF icon Technical Report (1.14 MB)
P. Swoboda, Kuske, J., and Savchynskyy, B., “A Dual Ascent Framework for Lagrangean Decomposition of Combinatorial Problems”, arXiv, preprint, 2016.
T. Beier, Andres, B., Köthe, U., and Hamprecht, F. A., “An Efficient Fusion Move Algorithm for the Minimum Cost Lifted Multicut Problem”, ECCV. Proceedings, vol. LNCS 9906. Springer, pp. 715-730, 2016.PDF icon Technical Report (4.89 MB)
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)
D. Kondermann, Nair, R., Honauer, K., Krispin, K., Andrulis, J., Brock, A., Güssefeld, B., Rahimimoghaddam, M., Hofmann, S., Brenner, C., and Jähne, B., “The HCI Benchmark Suite: Stereo and Flow Ground Truth With Uncertainties for Urban Autonomous Driving”, in The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2016.
D. Kondermann, Nair, R., Honauer, K., Krispin, K., Andrulis, J., Brock, A., Güssefeld, B., Rahimimoghaddam, M., Hofmann, S., Brenner, C., and Jähne, B., “The HCI Benchmark Suite: Stereo and Flow Ground Truth With Uncertainties for Urban Autonomous Driving”, in The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2016.
J. Kappes, Speth, M., Reinelt, G., and Schnörr, C., “Higher-order Segmentation via Multicuts”, Comp. Vision Image Understanding, vol. 143, pp. 104–119, 2016.
A. Biller, Badde, S., Nagel, A., Neumann, J. O., Wick, W., Hertenstein, A., Bendszus, M., Sahm, F., Benkhedah, N., and Kleesiek, J., “Improved Brain Tumor Classification by Sodium MR Imaging: Prediction of IDH Mutation Status and Tumor Progression”, American Journal of Neuroradiology, vol. 37 , pp. 66-73, 2016.
D. L. Richmond, Kainmueller, D., Yang, M. Y., Myers, E. W., and Rother, C., “Mapping auto-context decision forests to deep convnets for semantic segmentation”, in British Machine Vision Conference 2016, BMVC 2016, 2016, vol. 2016-Septe, pp. 144.1–144.12.
D. L. Richmond, Kainmueller, D., Yang, M. Y., Myers, E. W., and Rother, C., “Mapping auto-context decision forests to deep convnets for semantic segmentation”, in British Machine Vision Conference 2016, BMVC 2016, 2016, vol. 2016-Septe, pp. 144.1–144.12.
D. L. Richmond, Kainmueller, D., Yang, M. Y., Myers, E. W., and Rother, C., “Mapping auto-context decision forests to deep convnets for semantic segmentation”, in British Machine Vision Conference 2016, BMVC 2016, 2016, vol. 2016-Septe, pp. 144.1–144.12.
J. H. Kappes, Swoboda, P., Savchynskyy, B., Hazan, T., and Schnörr, C., “Multicuts and Perturb & MAP for Probabilistic Graph Clustering”, J. Math. Imag. Vision, vol. 56, pp. 221–237, 2016.
J. Hendrik Kappes, Swoboda, P., Savchynskyy, B., Hazan, T., and Schnörr, C., “Multicuts and Perturb & MAP for Probabilistic Graph Clustering”, Journal of Mathematical Imaging and Vision, vol. 56, pp. 221–237, 2016.
M. Zisler, Kappes, J. H., Schnörr, C., Petra, S., and Schnörr, C., “Non-Binary Discrete Tomography by Continuous Non-Convex Optimization”, IEEE Comp. Imaging, vol. 2, pp. 335-347, 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, 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.
C. Haubold, Schiegg, M., Kreshuk, A., Berg, S., Köthe, U., and Hamprecht, F. A., “Segmenting and Tracking Multiple Dividing Targets Using ilastik”, in Focus on Bio-Image Informatics, vol. 219, Springer, 2016, pp. 199-229.PDF icon Technical Report (4.46 MB)
C. Haubold, Schiegg, M., Kreshuk, A., Berg, S., Köthe, U., and Hamprecht, F. A., “Segmenting and Tracking Multiple Dividing Targets Using ilastik”, in Focus on Bio-Image Informatics, vol. 219, Springer, 2016, pp. 199-229.PDF icon Technical Report (4.46 MB)
A. Kiem, “Structured Learning on Calcium Imaging Data”, University of Heidelberg, 2016.
E. Brachmann, Michel, F., Krull, A., Yang, M. Ying, Gumhold, S., and Rother, C., “Uncertainty-Driven 6D Pose Estimation of Objects and Scenes from a Single RGB Image”, in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016, vol. 2016-Decem, pp. 3364–3372.
E. Brachmann, Michel, F., Krull, A., Yang, M. Ying, Gumhold, S., and Rother, C., “Uncertainty-Driven 6D Pose Estimation of Objects and Scenes from a Single RGB Image”, in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016, vol. 2016-Decem, pp. 3364–3372.
M. Kandemir, Haußmann, M., Diego, F., Rajamani, K., van der Laak, J., and Hamprecht, F. A., “Variational weakly-supervised Gaussian processes”, BMVC. Proceedings. 2016.PDF icon Technical Report (3.28 MB)

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