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2019

Ardizzone, L, Lüth, C, Kruse, J, Rother, C and Köthe, U (2019). Guided Image Generation with Conditional Invertible Neural Networks. http://arxiv.org/abs/1907.02392

Berg, S, 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 (2019). ilastik: interactive machine learning for (bio)image analysis. Nature Methods. 16 1226-1232

Remme, R (2019). Instance Segmentation Via Associative Pixel Embeddings. Heidelberg University

Friman, S I and Jähne, B (2019). Investigating SO2 transfer across the air–water interface via LIF. Exp. Fluids. 60 65

Hosseini Jafari, O, Mustikovela, S Karthik, Pertsch, K, Brachmann, E and Rother, C (2019). iPose: Instance-Aware 6D Pose Estimation of Partly Occluded Objects. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 11363 LNCS 477–492

Hanslovsky, P (2019). Isotropic Reconstruction of Neural Morphology from Large Non-Isotropic 3D Electron MIcroscopy. Heidelberg University

Hühnerbein, R, Savarino, F, Petra, S and Schnörr, C (2019). Learning Adaptive Regularization for Image Labeling Using Geometric Assignment. Proc. SSVM. Springer

Hühnerbein, R, Savarino, F, Petra, S and Schnörr, C (2019). Learning Adaptive Regularization for Image Labeling Using Geometric Assignment. preprint: arXiv. https://arxiv.org/abs/1910.09976

Leistner, T, Schilling, H, Mackowiak, R, Gumhold, S and Rother, C (2019). Learning to Think Outside the Box: Wide-Baseline Light Field Depth Estimation with EPI-Shift. Proceedings - 2019 International Conference on 3D Vision, 3DV 2019. 249–257. http://arxiv.org/abs/1909.09059 http://dx.doi.org/10.1109/3DV.2019.00036

PDF (8.94 MB)

Kirschbaum, E, Haußmann, M, Wolf, S, Sonntag, H, Schneider, J, Elzoheiry, S, Kann, O, Durstewitz, D and Hamprecht, F A (2019). LeMoNADe: Learned Motif and Neuronal Assembly Detection in calcium imaging videos. ICLR. Proceedings

Li, W, Hosseini Jafari, O and Rother, C (2019). Localizing Common Objects Using Common Component Activation Map

Peter, S (2019). Machine learning under test-time budget constraints. Heidelberg University

Nagel, L, Krall, K E and Jähne, B (2019). Measurement of air-sea gas transfer velocities in the Baltic Sea. Ocean Science. 15 235–247

Bengio, Y, Deleu, T, Rahaman, N, Ke, R, Lachapelle, S, Bilaniuk, O, Goyal, A and Pal, C (2019). A Meta-Transfer Objective for Learning to Disentangle Causal Mechanisms. arXiv preprint arXiv:1901.10912

Technical Report (871.59 KB)

Brachmann, E and Rother, C (2019). Neural-guided RANSAC: Learning where to sample model hypotheses. Proceedings of the IEEE International Conference on Computer Vision. 2019-Octob 4321–4330. http://arxiv.org/abs/1905.04132

PDF (8.02 MB)

Ravindran, A (2019). Novel Deep Learning-Based Instance Segmentation Using Mutex Watershed For Microscopy Cell Images. Heidelberg University

Kirschbaum, E (2019). Novel Machine Learning Approaches for Neurophysiological Data Analysis. Heidelberg University

Adler, T J, Ayala, L, Ardizzone, L, Kenngott, H G, Vemuri, A, Müller-Stich, B P, Rother, C, Köthe, U and Maier-Hein, L (2019). Out of Distribution Detection for Intra-operative Functional Imaging. MICCAI UNSURE Workshop 2019. 11840 LNCS 75–82

PDF (3.1 MB)

Snajder, R (2019). Pipeline Für Die Automatisierte Objektsegmentierung Von 3D Lightshet Mikroskopiebildern. Heidelberg University

E Sanmartin, F, Damrich, S and Hamprecht, F A (2019). Probabilistic Watershed: Sampling all spanning forests for seeded segmentation and semi-supervised learning. Advances in Neural Information Processing Systems

Bhowmik, A, Gumhold, S, Rother, C and Brachmann, E (2019). Reinforced Feature Points: Optimizing Feature Detection and Description for a High-Level Task. http://arxiv.org/abs/1912.00623

Li, J (2019). Robust Single Object Tracking Via Fully Convolutional Siamese Networks. Heidelberg University

Haußmann, M, Hamprecht, F A and Kandemir, M (2019). Sampling-Free Variational Inference of Bayesian Neural Networks by Variance Backpropagation. UAI. Proceedings. 563-573

Technical Report (1.04 MB)

Zisler, M, Zern, A, Petra, S and Schnörr, C (2019). Self-Assignment Flows for Unsupervised Data Labeling on Graphs. preprint: arXiv. https://arxiv.org/abs/1911.03472

Li, Y (2019). Semantic Instance Segmentation With The Multiway Mutex Watershed. Heidelberg University

Fita, E (2019). Semi-Supervised Distance-Based Segmentation. Heidelberg University

Voigt, P (2019). Simulation And Measurement Of The Water-Sided Viscous Shear Stress Without Waves. Institut für Umweltphysik, Universität Heidelberg, Germany

Storath, M, Kiefer, L and Weinmann, A (2019). Smoothing for signals with discontinuities using higher order Mumford-Shah models. Numerische Mathematik. 143(2) 423-460

Technical Report (1.09 MB)

Desana, M and Schnörr, C (2019). Sum-Product Graphical Models. Machine Learning. https://doi.org/10.1007/s10994-019-05813-2

Censor, Y, Petra, S and Schnörr, C (2019). Superiorization vs. Accelerated Convex Optimization: The Superiorized/Regularized Least Squares Case. preprint: arXiv. https://arxiv.org/abs/1911.05498

Großkinsky, (2019). Synaptic Cleft Prediction On Electron Microsope Images. Heidelberg University

Esposito, M, Hennersperger, C, Göbl, R, Demaret, L, Storath, M, Navab, N, Baust, M and Weinmann, A (2019). Total variation regularization of pose signals with an application to 3D freehand ultrasound. IEEE Transactions on Medical Imaging. 38(10) 2245-2258

Xiao, S (2019). Tracking Dividing Cells Using Spatio-Temporal Embeddings. Heidelberg University

Zern, A, Zisler, M, Petra, S and Schnörr, C (2019). Unsupervised Assignment Flow: Label Learning on Feature Manifolds by Spatially Regularized Geometric Assignment. preprint: arXiv. https://arxiv.org/abs/1904.10863

Zisler, M, Zern, A, Petra, S and Schnörr, C (2019). Unsupervised Labeling by Geometric and Spatially Regularized Self-Assignment. Proc. SSVM. Springer

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