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Author Title Type [ Year(Desc)]
2019
M. Haußmann, Hamprecht, F. A., and Kandemir, M., “Deep Active Learning with Adaptive Acquisition”, IJCAI. Proceedings. pp. 2470-2476, 2019.PDF icon Technical Report (137.6 KB)
W. Li, Hosseini Jafari, O., and Rother, C., “Deep Object Co-segmentation”, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2019, vol. 11363 LNCS, pp. 638–653.
M. Papst, “Development of a method for quantitative imaging of air-water gas exchange”, Institut für Umweltphysik, Universität Heidelberg, Germany, 2019.
B. Savchynskyy, “Discrete Graphical Models — An Optimization Perspective”, Foundations and Trends® in Computer Graphics and Vision, vol. 11, pp. 160–429, 2019.
A. Sanakoyeu, Tschernezki, V., Büchler, U., and Ommer, B., “Divide and Conquer the Embedding Space for Metric Learning”, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2019.
L. Kiefer, Storath, M., and Weinmann, A., “An efficient algorithm for the piecewise affine-linear Mumford-Shah model based on a Taylor jet splitting”, IEEE Transactions on Image Processing, vol. 29, 2019.PDF icon Technical Report (2.04 MB)
L. Cerrone, Zeilmann, A., and Hamprecht, F. A., “End-to-End Learned Random Walker for Seeded Image Segmentation”, CVPR. Proceedings. pp. 12559-12568, 2019.
A. Imle, Kumberger, P., Schnellbächer, N. D., Fehr, J., Carillo-Bustamente, P., Ales, J., Schmidt, P., Ritter, C., Godinez, W. J., Müller, B., Rohr, K., Hamprecht, F. A., Schwarz, U. S., Graw, F., and Fackler, O. T., “Experimental and computational analyses reveal that environmental restrictions shape HIV-1 spread in 3D cultures”, Nature Communications, vol. 13;10(1), 2019.
E. Brachmann and Rother, C., “Expert sample consensus applied to camera re-localization”, in Proceedings of the IEEE International Conference on Computer Vision, 2019, vol. 2019-Octob, pp. 7524–7533.
D. M. Kirchhöfer, Holst, G. A., Wouters, F. S., Hock, S., and Jähne, B., “Extended noise equalisation for image compression in microscopical applications”, tm - Technisches Messen, vol. 86, pp. 422–432, 2019.
F. Rathke and Schnörr, C., “Fast Multivariate Log-Concave Density Estimation”, Comp. Statistics & Data Analysis, vol. 140, pp. 41-58, 2019.
F. Rathke and Schnörr, C., “Fast Multivariate Log-Concave Density Estimation”, Comp. Statistics & Data Analysis, vol. 140, pp. 41–58, 2019.
A. Klein, “The Fetch Dependency of Small-Scale Air-Sea Interaction Processes at Low to Moderate Wind Speeds”, vol. Dissertation. Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ. Heidelberg, Heidelberg, 2019.
H. Abu Alhaija, Mustikovela, S. Karthik, Geiger, A., and Rother, C., “Geometric Image Synthesis”, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2019, vol. 11366 LNCS, pp. 85–100.
A. Zeilmann, Savarino, F., Petra, S., and Schnörr, C., “Geometric Numerical Integration of the Assignment Flow”, Inverse Problems, 2019.
L. Kostrykin, Schnörr, C., and Rohr, K., “Globally Optimal Segmentation of Cell Nuclei in Fluoroscence Microscopy Images using Shape and Intensity Information”, Medical Image Analysis, 2019.
L. Ardizzone, Lüth, C., Kruse, J., Rother, C., and Köthe, U., “Guided Image Generation with Conditional Invertible Neural Networks”, 2019.
L. Ardizzone, Lüth, C., Kruse, J., Rother, C., and Köthe, U., “Guided Image Generation with Conditional Invertible Neural Networks”, 2019.
S. Berg, Kutra, D., Kroeger, T., Straehle, C. N., Kausler, B. X., Haubold, C., Schiegg, M., Ales, J., Beier, T., Rudy, M., Eren, K., Cervantes, J. I., Xu, B., Beuttenmüller, F., Wolny, A., Zhang, C., Köthe, U., Hamprecht, F. A., and Kreshuk, A., “ilastik: interactive machine learning for (bio)image analysis”, Nature Methods, vol. 16, pp. 1226-1232, 2019.
R. Remme, “Instance Segmentation via Associative Pixel Embeddings”, Heidelberg University, 2019.
S. I. Friman and Jähne, B., “Investigating SO2 transfer across the air–water interface via LIF”, Exp. Fluids, vol. 60, p. 65, 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.
P. Hanslovsky, “Isotropic Reconstruction of Neural Morphology from Large Non-Isotropic 3D Electron MIcroscopy”. Heidelberg University, 2019.
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.
T. Leistner, Schilling, H., Mackowiak, R., Gumhold, S., and Rother, C., “Learning to Think Outside the Box: Wide-Baseline Light Field Depth Estimation with EPI-Shift”, in Proceedings - 2019 International Conference on 3D Vision, 3DV 2019, 2019, pp. 249–257.PDF icon PDF (8.94 MB)
E. Kirschbaum, Haußmann, M., Wolf, S., Sonntag, H., Schneider, J., Elzoheiry, S., Kann, O., Durstewitz, D., and Hamprecht, F. A., “LeMoNADe: Learned Motif and Neuronal Assembly Detection in calcium imaging videos”, ICLR. Proceedings. 2019.
W. Li, Hosseini Jafari, O., and Rother, C., “Localizing Common Objects Using Common Component Activation Map”, 2019.
S. Peter, “Machine learning under test-time budget constraints”. Heidelberg University, 2019.
L. Nagel, Krall, K. E., and Jähne, B., “Measurement of air-sea gas transfer velocities in the Baltic Sea”, Ocean Science, vol. 15, pp. 235–247, 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)
B. Brattoli, Roth, K., and Ommer, B., “MIC: Mining Interclass Characteristics for Improved Metric Learning”, in Proceedings of the Intl. Conf. on Computer Vision (ICCV), 2019.
E. Brachmann and Rother, C., “Neural-guided RANSAC: Learning where to sample model hypotheses”, in Proceedings of the IEEE International Conference on Computer Vision, 2019, vol. 2019-Octob, pp. 4321–4330.PDF icon PDF (8.02 MB)
A. Ravindran, “Novel Deep Learning-based Instance Segmentation Using Mutex Watershed for Microscopy Cell Images”, Heidelberg University, 2019.
E. Kirschbaum, “Novel Machine Learning Approaches for Neurophysiological Data Analysis”. Heidelberg University, 2019.

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