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2017
S. Wolf, Schott, L., Köthe, U., and Hamprecht, F. A., “Learned Watershed: End-to-End Learning of Seeded Segmentation”, ICCV. pp. 2030-2038, 2017.PDF icon Technical Report (3.76 MB)
J. Kruse, Rother, C., Schmidt, U., and Dresden, T. U., “Learning to Push the Limits of Efficient FFT-based Image Deconvolution - Supplemental Material”, 2017.
J. Kruse, Rother, C., and Schmidt, U., “Learning to Push the Limits of Efficient FFT-Based Image Deconvolution”, in Proceedings of the IEEE International Conference on Computer Vision, 2017, vol. 2017-Octob, pp. 4596–4604.
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.
F. Rathke, Desana, M., and Schnörr, C., “Locally Adaptive Probabilistic Models for Global Segmentation of Pathological OCT Scans”, MICCAI. Proceedings. pp. 177-184, 2017.PDF icon Technical Report (4.79 MB)
F. Rathke, Desana, M., and Schnörr, C., “Locally Adaptive Probabilistic Models for Global Segmentation of Pathological OCT Scans”, in Proc. MICCAI, 2017.
B. Brattoli, Büchler, U., Wahl, A. - S., Schwab, M. E., and Ommer, B., “LSTM Self-Supervision for Detailed Behavior Analysis”, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.PDF icon Article (8.75 MB)
F. Aström, Hühnerbein, R., Savarino, F., Recknagel, J., and Schnörr, C., “MAP Image Labeling Using Wasserstein Messages and Geometric Assignment”, in Proc. SSVM, 2017, vol. 10302.
F. Aström, Hühnerbein, R., Savarino, F., Recknagel, J., and Schnörr, C., “MAP Image Labeling Using Wasserstein Messages and Geometric Assignment”, in Proc. SSVM, 2017, vol. 10302.
M. Kandemir, Hamprecht, F. A., Wojek, C., and Schmidt, U., “Maschinelles Lernen”, Patent, Patent Number WO2017032775A1, 2017.PDF icon Technical Report (317.04 KB)
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.
F. Savarino, Hühnerbein, R., Aström, F., Recknagel, J., and Schnörr, C., “Numerical Integration of Riemannian Gradient Flows for Image Labeling”, in Proc. SSVM, 2017, vol. 10302.
F. Savarino, Hühnerbein, R., Aström, F., Recknagel, J., and Schnörr, C., “Numerical Integration of Riemannian Gradient Flows for Image Labeling”, in Proc. SSVM, 2017, vol. 10302.
V. Ulman, Maška, M., Magnusson, K. E. G., Ronneberger, O., Haubold, C., Harder, N., Matula, P., Matula, P., Svoboda, D., Radojevic, M., Smal, I., Rohr, K., Jaldén, J., Blau, H. M., Dzyubachyk, O., Lelieveldt, B., Xiao, P., Li, Y., Cho, S. - Y., Dufour, A., Olivo-Marin, J. C., Reyes-Aldasoro, C. C., Solis-Lemus, J. A., Bensch, R., Brox, T., Stegmaier, J., Mikut, R., Wolf, S., Hamprecht, F. A., Esteves, T., Quelhas, P., Demirel, Ö., Malström, L., Jug, F., Tomančák, P., Meijering, E., Muñoz-Barrutia, A., Kozubek, M., and Ortiz-de-Solorzano, C., “An Objective Comparison of Cell Tracking Algorithms”, Nature Methods, vol. 14, no. 12, pp. 1141-1152, 2017.PDF icon Technical Report (4.24 MB)
V. Ulman, Maška, M., Magnusson, K. E. G., Ronneberger, O., Haubold, C., Harder, N., Matula, P., Matula, P., Svoboda, D., Radojevic, M., Smal, I., Rohr, K., Jaldén, J., Blau, H. M., Dzyubachyk, O., Lelieveldt, B., Xiao, P., Li, Y., Cho, S. - Y., Dufour, A., Olivo-Marin, J. C., Reyes-Aldasoro, C. C., Solis-Lemus, J. A., Bensch, R., Brox, T., Stegmaier, J., Mikut, R., Wolf, S., Hamprecht, F. A., Esteves, T., Quelhas, P., Demirel, Ö., Malström, L., Jug, F., Tomančák, P., Meijering, E., Muñoz-Barrutia, A., Kozubek, M., and Ortiz-de-Solorzano, C., “An Objective Comparison of Cell Tracking Algorithms”, Nature Methods, vol. 14, no. 12, pp. 1141-1152, 2017.PDF icon Technical Report (4.24 MB)
V. Ulman, Maška, M., Magnusson, K. E. G., Ronneberger, O., Haubold, C., Harder, N., Matula, P., Matula, P., Svoboda, D., Radojevic, M., Smal, I., Rohr, K., Jaldén, J., Blau, H. M., Dzyubachyk, O., Lelieveldt, B., Xiao, P., Li, Y., Cho, S. - Y., Dufour, A., Olivo-Marin, J. C., Reyes-Aldasoro, C. C., Solis-Lemus, J. A., Bensch, R., Brox, T., Stegmaier, J., Mikut, R., Wolf, S., Hamprecht, F. A., Esteves, T., Quelhas, P., Demirel, Ö., Malström, L., Jug, F., Tomančák, P., Meijering, E., Muñoz-Barrutia, A., Kozubek, M., and Ortiz-de-Solorzano, C., “An Objective Comparison of Cell Tracking Algorithms”, Nature Methods, vol. 14, no. 12, pp. 1141-1152, 2017.PDF icon Technical Report (4.24 MB)
V. Ulman, Maška, M., Magnusson, K. E. G., Ronneberger, O., Haubold, C., Harder, N., Matula, P., Matula, P., Svoboda, D., Radojevic, M., Smal, I., Rohr, K., Jaldén, J., Blau, H. M., Dzyubachyk, O., Lelieveldt, B., Xiao, P., Li, Y., Cho, S. - Y., Dufour, A., Olivo-Marin, J. C., Reyes-Aldasoro, C. C., Solis-Lemus, J. A., Bensch, R., Brox, T., Stegmaier, J., Mikut, R., Wolf, S., Hamprecht, F. A., Esteves, T., Quelhas, P., Demirel, Ö., Malström, L., Jug, F., Tomančák, P., Meijering, E., Muñoz-Barrutia, A., Kozubek, M., and Ortiz-de-Solorzano, C., “An Objective Comparison of Cell Tracking Algorithms”, Nature Methods, vol. 14, no. 12, pp. 1141-1152, 2017.PDF icon Technical Report (4.24 MB)
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.
J. Berger, Lenzen, F., Becker, F., Neufeld, A., and Schnörr, C., “{Second-Order Recursive Filtering on the Rigid-Motion Lie Group SE(3) Based on Nonlinear Observations”, J. Math. Imag. Vision, vol. 58, pp. 102–129, 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.
Ö. Sümer, Dencker, T., and Ommer, B., “Self-supervised Learning of Pose Embeddings from Spatiotemporal Relations in Videos”, in Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2017.PDF icon Paper (3.98 MB)PDF icon Supplementary Material (3.36 MB)
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.
T. Milbich, Bautista, M., Sutter, E., and Ommer, B., “Unsupervised Video Understanding by Reconciliation of Posture Similarities”, in Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2017.
2016
F. Aström, Petra, S., Schmitzer, B., and Schnörr, C., “The Assignment Manifold: A Smooth Model for Image Labeling”, in Proc. 2nd Int. Workshop on Differential Geometry in Computer Vision and Machine Learning (DIFF-CVML'16; oral presentation; Grenander best paper award), 2016.
F. Aström, Petra, S., Schmitzer, B., and Schnörr, C., “The Assignment Manifold: A Smooth Model for Image Labeling”, in Proc. 2nd Int. Workshop on Differential Geometry in Computer Vision and Machine Learning (DIFF-CVML'16; oral presentation; Grenander best paper award), 2016.
M. Bautista, Sanakoyeu, A., Sutter, E., and Ommer, B., “CliqueCNN: Deep Unsupervised Exemplar Learning”, in Proceedings of the Conference on Advances in Neural Information Processing Systems (NIPS), Barcelona, 2016.PDF icon Article (5.79 MB)
M. Bautista, Sanakoyeu, A., Sutter, E., and Ommer, B., “CliqueCNN: Deep Unsupervised Exemplar Learning”, in Proceedings of the Conference on Advances in Neural Information Processing Systems (NIPS), Barcelona, 2016.PDF icon Article (5.79 MB)
M. Baust, Weinmann, A., Wieczorek, M., Lasser, T., Storath, M., and Navab, N., “Combined Tensor Fitting and TV Regularization in Diffusion Tensor Imaging based on a Riemannian Manifold Approach”, IEEE Transactions on Medical Imaging, vol. 35, no. 8, pp. 1972–1989, 2016.PDF icon Technical Report (8.65 MB)
P. Schmidt, “Deep Learning for Bioimage Analysis”, University of Heidelberg, 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)
M. Zisler, Petra, S., Schnörr, C., and Schnörr, C., “Discrete Tomography by Continuous Multilabeling Subject to Projection Constraints”, in Proc. GCPR, 2016.
M. Zisler, Petra, S., Schnörr, C., and Schnörr, C., “Discrete Tomography by Continuous Multilabeling Subject to Projection Constraints”, in Proc. GCPR, 2016.
F. Aström and Schnörr, C., “Double-Opponent Vectorial Total Variation”, in Proc. ECCV, 2016.
P. Swoboda, Kuske, J., and Savchynskyy, B., “A Dual Ascent Framework for Lagrangean Decomposition of Combinatorial Problems”, arXiv, preprint, 2016.

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