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2016
J. Kleesiek, Petersen, J., Döring, M., Maier-Hein, K., Köthe, U., Wick, W., Hamprecht, F. A., Bendszus, M., and Biller, A., “Virtual Raters for Reproducible and Objective Assessments in Radiology”, Nature Scientific Reports, vol. 6, 2016.PDF icon Technical Report (2.81 MB)
2017
R. Dalitz, Petra, S., and Schnörr, C., “Compressed Motion Sensing”, in Proc. SSVM, 2017, vol. 10302.
S. Peter, Diego, F., Hamprecht, F. A., and Nadler, B., “Cost-efficient Gradient Boosting”, NIPS, poster. 2017.
S. Ramos, Gehrig, S., Pinggera, P., Franke, U., and Rother, C., “Detecting unexpected obstacles for self-driving cars: Fusing deep learning and geometric modeling”, in IEEE Intelligent Vehicles Symposium, Proceedings, 2017, pp. 1025–1032.
M. Zisler, Savarino, F., Petra, S., and Schnörr, C., “Gradient Flows on a Riemannian Submanifold for Discrete Tomography”, in Proc. GCPR, 2017.
F. Aström, Petra, S., Schmitzer, B., and Schnörr, C., “Image Labeling by Assignment”, J. Math. Imag. Vision, vol. 58, pp. 211–238, 2017.
M. Zisler, Aström, F., Petra, S., and Schnörr, C., “Image Reconstruction by Multilabel Propagation”, in Proc. SSVM, 2017, vol. 10302.
E. Bodnariuc, Petra, S., Schnörr, C., and Voorneveld, J., “A Local Spatio-Temporal Approach to Plane Wave Ultrasound Particle Image Velocimetry”, in Proc. GCPR, 2017.
T. Beier, Pape, C., Rahaman, N., Prange, T., Berg, S., Bock, D., Cardona, A., Knott, G. W., Plaza, S. M., Scheffer, L. K., Köthe, U., Kreshuk, A., and Hamprecht, F. A., “Multicut brings automated neurite segmentation closer to human performance”, Nature Methods, vol. 14, no. 2, pp. 101-102, 2017.
T. Beier, Pape, C., Rahaman, N., Prange, T., Berg, S., Bock, D., Cardona, A., Knott, G. W., Plaza, S. M., Scheffer, L. K., Köthe, U., Kreshuk, A., and Hamprecht, F. A., “Multicut brings automated neurite segmentation closer to human performance”, Nature Methods, vol. 14, no. 2, pp. 101-102, 2017.
T. Beier, Pape, C., Rahaman, N., Prange, T., Berg, S., Bock, D., Cardona, A., Knott, G. W., Plaza, S. M., Scheffer, L. K., Köthe, U., Kreshuk, A., and Hamprecht, F. A., “Multicut brings automated neurite segmentation closer to human performance”, Nature Methods, vol. 14, no. 2, pp. 101-102, 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.
2018
A. Zeilmann, Savarino, F., Petra, S., and Schnörr, C., “Geometric Numerical Integration of the Assignment Flow”, preprint: arXiv, 2018.
O. Hosseini Jafari, Mustikovela, S. K., Pertsch, K., Brachmann, E., and Rother, C., “iPose: Instance-Aware 6D Pose Estimation of Partly Occluded Objects”, ACCV. Proceedings, in press. 2018.PDF icon Technical Report (3.28 MB)
S. Wolf, Pape, C., Bailoni, A., Rahaman, N., Kreshuk, A., Köthe, U., and Hamprecht, F. A., “The Mutex Watershed: Efficient, Parameter-Free Image Partitioning”, ECCV. Proceedings. Springer, pp. 571-587, 2018.
S. Wolf, Pape, C., Bailoni, A., Rahaman, N., Kreshuk, A., Köthe, U., and Hamprecht, F. A., “The Mutex Watershed: Efficient, Parameter-Free Image Partitioning”, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2018, vol. 11208 LNCS, pp. 571–587.
A. Zern, Zisler, M., Aström, F., Petra, S., and Schnörr, C., “Unsupervised Label Learning on Manifolds by Spatially Regularized Geometric Assignment”, GCPR. Proceedings. pp. 698-713, 2018.PDF icon Technical Report (5.23 MB)
A. Zern, Zisler, M., Aström, F., Petra, S., and Schnörr, C., “Unsupervised Label Learning on Manifolds by Spatially Regularized Geometric Assignment”, in GCPR, 2018.
2019
A. L. Bendinger, Debus, C., Glowa, C., Karger, C. P., Peter, J., and Storath, M., “Bolus arrival time estimation in dynamic contrast-enhanced magnetic resonance imaging of small animals based on spline models, in press”, Physics in Medicine and Biology, vol. 64, no. 4, 2019.
J. Kleesiek, Morshuis, J. Nikolas, Isensee, F., Deike-Hofmann, K., Paech, D., Kickingereder, P., Köthe, U., Rother, C., Forsting, M., Wick, W., Bendszus, M., Schlemmer, H. Peter, and Radbruch, A., “Can Virtual Contrast Enhancement in Brain MRI Replace Gadolinium?: A Feasibility Study”, Investigative Radiology, vol. 54, pp. 653–660, 2019.
M. Papst, “Development of a method for quantitative imaging of air-water gas exchange”, Institut für Umweltphysik, Universität Heidelberg, Germany, 2019.
A. Zeilmann, Savarino, F., Petra, S., and Schnörr, C., “Geometric Numerical Integration of the Assignment Flow”, Inverse Problems, 2019.
O. Hosseini Jafari, Mustikovela, S. Karthik, Pertsch, K., Brachmann, E., and Rother, C., “iPose: Instance-Aware 6D Pose Estimation of Partly Occluded Objects”, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2019, vol. 11363 LNCS, pp. 477–492.
R. Hühnerbein, Savarino, F., Petra, S., and Schnörr, C., “Learning Adaptive Regularization for Image Labeling Using Geometric Assignment”, preprint: arXiv, 2019.
R. Hühnerbein, Savarino, F., Petra, S., and Schnörr, C., “Learning Adaptive Regularization for Image Labeling Using Geometric Assignment”, in Proc. SSVM, 2019.
S. Peter, “Machine learning under test-time budget constraints”. Heidelberg University, 2019.
Y. Bengio, Deleu, T., Rahaman, N., Ke, R., Lachapelle, S., Bilaniuk, O., Goyal, A., and Pal, C., “A Meta-Transfer Objective for Learning to Disentangle Causal Mechanisms”, arXiv preprint arXiv:1901.10912, 2019.PDF icon Technical Report (871.59 KB)
M. Zisler, Zern, A., Petra, S., and Schnörr, C., “Self-Assignment Flows for Unsupervised Data Labeling on Graphs”, preprint: arXiv, 2019.
Y. Censor, Petra, S., and Schnörr, C., “Superiorization vs. Accelerated Convex Optimization: The Superiorized/Regularized Least Squares Case”, preprint: arXiv, 2019.
A. Zern, Zisler, M., Petra, S., and Schnörr, C., “Unsupervised Assignment Flow: Label Learning on Feature Manifolds by Spatially Regularized Geometric Assignment”, preprint: arXiv, 2019.
M. Zisler, Zern, A., Petra, S., and Schnörr, C., “Unsupervised Labeling by Geometric and Spatially Regularized Self-Assignment”, in Proc. SSVM, 2019.
N. Pandey, “Weakly Supervised Semantic Segmentation”, Heidelberg University, 2019.
2020
A. Wolny, Cerrone, L., Vijayan, A., Tofanelli, R., Vilches-Barro, A., Louveaux, M., Wenzel, C., Strauss, S., Wilson-Sanchez, D., Lymbouridou, R., Steigleder, S. S., Pape, C., Bailoni, A., Duran-Nebreda, S., Bassel, G. W., Lohmann, J. U., Tsiantis, M., Hamprecht, F. A., Schneitz, K., Maizel, A., and Kreshuk, A., “Accurate and Versatile 3D Segmentation of Plant Tissues at Cellular Resolution”, eLife, vol. 9, 2020.
S. Bollweg, Haußmann, M., Kasieczka, G., Luchmann, M., Plehn, T., and Thompson, J., “Deep-Learning Jets with Uncertainties and More”, SciPost Phys, vol. 8, no. 1, 2020.PDF icon Technical Report (1.65 MB)

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