All Publications

2017

Zisler, M, Aström, F, Petra, S and Schnörr, C (2017). Image Reconstruction by Multilabel Propagation. Proc. SSVM. Springer. 10302
Aström, F, Hühnerbein, R, Savarino, F, Recknagel, J and Schnörr, C (2017). MAP Image Labeling Using Wasserstein Messages and Geometric Assignment. Proc. SSVM. Springer. 10302
Savarino, F, Hühnerbein, R, Aström, F, Recknagel, J and Schnörr, C (2017). Numerical Integration of Riemannian Gradient Flows for Image Labeling. Proc. SSVM. Springer. 10302
Dalitz, R, Petra, S and Schnörr, C (2017). Compressed Motion Sensing. Proc. SSVM. Springer. 10302
Rathke, F, Desana, M and Schnörr, C (2017). Locally Adaptive Probabilistic Models for Global Segmentation of Pathological OCT Scans. Proc. MICCAI
Bodnariuc, E, Petra, S, Schnörr, C and Voorneveld, J (2017). A Local Spatio-Temporal Approach to Plane Wave Ultrasound Particle Image Velocimetry. Proc. GCPR
Zisler, M, Savarino, F, Petra, S and Schnörr, C (2017). Gradient Flows on a Riemannian Submanifold for Discrete Tomography. Proc. GCPR
Zern, A, Rohr, K and Schnörr, C (2017). Geometric Image Labeling with Global Convex Labeling Constraints. Proc. EMMCVPR
Hühnerbein, R, Savarino, F, Aström, F and Schnörr, C (2017). Image Labeling Based on Graphical Models Using Wasserstein Messages and Geometric Assignment. http://arxiv.org/abs/1710.01493
Aström, F and Schnörr, C (2017). A Geometric Approach for Color Image Regularization. Comp. Vision Image Understanding. 165 43–59. https://doi.org/10.1016/j.cviu.2017.10.013
Abu Alhaija, H, Mustikovela, S Karthik, Mescheder, L, Geiger, A and Rother, C (2017). Augmented reality meets deep learning for car instance segmentation in urban scenes. British Machine Vision Conference 2017, BMVC 2017
Kruse, J, Rother, C, Schmidt, U and Dresden, T U (2017). Learning To Push The Limits Of Efficient Fft-Based Image Deconvolution - Supplemental Material
Brachmann, E, Krull, A, Nowozin, S, Shotton, J, Michel, F, Gumhold, S and Rother, C (2017). DSAC - Differentiable RANSAC for camera localization. Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017. 2017-Janua 2492–2500. http://arxiv.org/abs/1611.05705
Kirillov, A, Levinkov, E, Andres, B, Savchynskyy, B and Rother, C (2017). InstanceCut: From edges to instances with MultiCut. Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017. 2017-Janua 7322–7331
Balluff, B, Hanselmann, M and Heeren, R M A (2017). Mass spectrometry imaging for the investigation of intratumor heterogeneity. Advances in Cancer Research. Elsevier. 134 201-230
Kirillov, A, Schlesinger, D, Zheng, S, Savchynskyy, B, Torr, P H S and Rother, C (2017). Joint training of generic CNN-CRF models with stochastic optimization. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 10112 LNCS 221–236. http://host.robots.ox.ac.uk:8080/leaderboard
Levinkov, E, Uhrig, J, Tang, S, Omran, M, Insafutdinov, E, Kirillov, A, Rother, C, Brox, T, Schiele, B and Andres, B (2017). Joint graph decomposition & node labeling: Problem, algorithms, applications. Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017. 2017-Janua 1904–1912
Hosseini Jafari, O, Groth, O, Kirillov, A, Yang, M Ying and Rother, C (2017). Analyzing modular CNN architectures for joint depth prediction and semantic segmentation. Proceedings - IEEE International Conference on Robotics and Automation. 4620–4627. http://arxiv.org/abs/1702.08009 http://dx.doi.org/10.1109/ICRA.2017.7989537
Storath, M, Brandt, C, Hofmann, M, Knopp, T, Salamon, J, Weber, A and Weinmann, A (2017). Edge preserving and noise reducing reconstruction for magnetic particle imaging. IEEE Transactions on Medical Imaging. 36 74 - 85PDF icon Technical Report (1.43 MB)
Storath, M, Weinmann, A and Unser, M (2017). Jump-penalized least absolute values estimation of scalar or circle-valued signals. Information and Inference. 6 225–245PDF icon Technical Report (3.4 MB)
Michel, F, Kirillov, A, Brachmann, E, Krull, A, Gumhold, S, Savchynskyy, B and Rother, C (2017). Global hypothesis generation for 6D object pose estimation. Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017. 2017-Janua 115–124. http://arxiv.org/abs/1612.02287
Krull, A, Brachmann, E, Nowozin, S, Michel, F, Shotton, J and Rother, C (2017). PoseAgent: Budget-constrained 6D object pose estimation via reinforcement learning. Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017. 2017-Janua 2566–2574. http://arxiv.org/abs/1612.03779
Schlesinger, D, Jug, F, Myers, G, Rother, C and Kainmueller, D (2017). Crowd sourcing image segmentation with iaSTAPLE. Proceedings - International Symposium on Biomedical Imaging. 401–405
Sümer, Ö, Dencker, T and Ommer, B (2017). Self-supervised Learning of Pose Embeddings from Spatiotemporal Relations in Videos. Proceedings of the IEEE International Conference on Computer Vision (ICCV)PDF icon Paper (3.98 MB)PDF icon Supplementary Material (3.36 MB)
Storath, M, Rickert, D, Unser, M and Weinmann, A (2017). Fast segmentation from blurred data in 3D fluorescence microscopy. IEEE Transactions on Image Processing. 26
Ramos, S, Gehrig, S, Pinggera, P, Franke, U and Rother, C (2017). Detecting unexpected obstacles for self-driving cars: Fusing deep learning and geometric modeling. IEEE Intelligent Vehicles Symposium, Proceedings. 1025–1032. http://arxiv.org/abs/1612.06573
Brosowsky, M (2017). Cluster Resolving For Animal Tracking: Multi Hypotheses Tracking With Part Based Model For Object Hypotheses Generation And Pose Estimation. University of Heidelberg
Beier, T, 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 (2017). Multicut brings automated neurite segmentation closer to human performance. Nature Methods. 14 101-102. http://rdcu.be/oVDQ
Krasowki, N, Beier, T, Knott, G W, Köthe, U, Hamprecht, F A and Kreshuk, A (2017). Neuron Segmentation with High-Level Biological Priors. IEEE Transactions on Medical Imaging. 37
Haubold, C (2017). Scalable Inference for Multi-Target Tracking on Proliferating Cells. University of Heidelberg
Massiceti, D, Krull, A, Brachmann, E, Rother, C and Torr, P H S (2017). Random Forests versus Neural Networks − What's best for camera location
Hullin, M, Klein, R, Schultz, T, Yao, A, Li, W, Hosseini Jafari, O and Rother, C (2017). Semantic-Aware Image Smoothing. Vision, Modeling, and Visualization. https://hci.iwr.uni-heidelberg.de/vislearn/wp-content/uploads/2014/08/paper1024_CRC.pdf
Wahl, A - S, Büchler, U, Brändli, A, Brattoli, B, Musall, S, Kasper, H, Ineichen, B V, Helmchen, F, Ommer, B and Schwab, M E (2017). Optogenetically stimulating the intact corticospinal tract post-stroke restores motor control through regionalized functional circuit formation. Nature Communications. (ASW & UB contributed equally; BO and MES contributed equally). https://www.nature.com/articles/s41467-017-01090-6
Kruse, J, Rother, C and Schmidt, U (2017). Learning to Push the Limits of Efficient FFT-Based Image Deconvolution. Proceedings of the IEEE International Conference on Computer Vision. 2017-Octob 4596–4604
Behl, A, Hosseini Jafari, O, Mustikovela, S Karthik, Abu Alhaija, H, Rother, C and Geiger, A (2017). Bounding Boxes, Segmentations and Object Coordinates: How Important is Recognition for 3D Scene Flow Estimation in Autonomous Driving Scenarios?. Proceedings of the IEEE International Conference on Computer Vision. 2017-Octob 2593–2602
Behl, A, Hosseini Jafari, O, Mustikovela, S Karthik, Abu Alhaija, H, Rother, C and Geiger, A (2017). Bounding Boxes, Segmentations and Object Coordinates: How Important is Recognition for 3D Scene Flow Estimation in Autonomous Driving Scenarios?. Proceedings of the IEEE International Conference on Computer Vision. 2017-Octob 2593–2602
Milbich, T, Bautista, M, Sutter, E and Ommer, B (2017). Unsupervised Video Understanding by Reconciliation of Posture Similarities. Proceedings of the IEEE International Conference on Computer Vision (ICCV). https://hciweb.iwr.uni-heidelberg.de/compvis/research/tmilbich_iccv17
Haußmann, M, Hamprecht, F A and Kandemir, M (2017). Variational Bayesian Multiple Instance Learning with Gaussian Processes. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 6570-6579PDF icon Technical Report (1.29 MB)
Haubold, C, Uhlmann, V, Unser, M and Hamprecht, F A (2017). Diverse M-best Solutions by Dynamic Programming. GCPR. Proceedings. Springer. LNCS 10496 255-267
Bautista, M, Fuchs, P and Ommer, B (2017). Learning Where to Drive by Watching Others. Proceedings of the German Conference Pattern Recognition. Springer-Verlag, Basel. 1

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