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2017
M. Kandemir, Hamprecht, F. A., Wojek, C., and Schmidt, U., “Active machine learning for training an event classification”, Patent, Patent Number WO2017032775 A1, 2017.
O. Hosseini Jafari, Groth, O., Kirillov, A., Yang, M. Ying, and Rother, C., “Analyzing modular CNN architectures for joint depth prediction and semantic segmentation”, in Proceedings - IEEE International Conference on Robotics and Automation, 2017, pp. 4620–4627.
L. Gerhard Holtmann, “Aufbau eines aktiven Thermographiesystems zur Messung des Geschwindigkeitsgradienten in der windgetriebenen wasserseitigen viskosen Grenzschicht”, Institut für Umweltphysik, Universität Heidelberg, Germany, 2017.
A. Behl, Hosseini Jafari, O., Mustikovela, S. Karthik, Abu Alhaija, H., Rother, C., and Geiger, A., “Bounding Boxes, Segmentations and Object Coordinates: How Important is Recognition for 3D Scene Flow Estimation in Autonomous Driving Scenarios?”, in Proceedings of the IEEE International Conference on Computer Vision, 2017, vol. 2017-Octob, pp. 2593–2602.
A. Behl, Hosseini Jafari, O., Mustikovela, S. Karthik, Abu Alhaija, H., Rother, C., and Geiger, A., “Bounding Boxes, Segmentations and Object Coordinates: How Important is Recognition for 3D Scene Flow Estimation in Autonomous Driving Scenarios?”, in Proceedings of the IEEE International Conference on Computer Vision, 2017, vol. 2017-Octob, pp. 2593–2602.
S. Peter, Diego, F., Hamprecht, F. A., and Nadler, B., “Cost-efficient Gradient Boosting”, NIPS, poster. 2017.
C. Haubold, Uhlmann, V., Unser, M., and Hamprecht, F. A., “Diverse M-best Solutions by Dynamic Programming”, GCPR. Proceedings, vol. LNCS 10496. Springer, pp. 255-267, 2017.
C. Haubold, Uhlmann, V., Unser, M., and Hamprecht, F. A., “Diverse M-best Solutions by Dynamic Programming”, GCPR. Proceedings, vol. LNCS 10496. Springer, pp. 255-267, 2017.
V. Uhlmann, Haubold, C., Hamprecht, F. A., and Unser, M., “Diverse Shortest Paths for Bioimage Analysis”, Bioinformatics, pp. 1-3, 2017.
V. Uhlmann, Haubold, C., Hamprecht, F. A., and Unser, M., “Diverse Shortest Paths for Bioimage Analysis”, Bioinformatics, pp. 1-3, 2017.
M. Storath, Brandt, C., Hofmann, M., Knopp, T., Salamon, J., Weber, A., and Weinmann, A., “Edge preserving and noise reducing reconstruction for magnetic particle imaging”, IEEE Transactions on Medical Imaging, vol. 36, no. 1, pp. 74 - 85, 2017.PDF icon Technical Report (1.43 MB)
R. Hühnerbein, Savarino, F., Aström, F., and Schnörr, C., “Image Labeling Based on Graphical Models Using Wasserstein Messages and Geometric Assignment”. 2017.
J. Hennies, “Improvement and Validation of Neural EM Volume Image Segmentation by High-Level Information”, University of Heidelberg, 2017.
A. Haller, “Interactive Watershed Based Segmentation for Biological Images”, University of Heidelberg, 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)
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)
B. Balluff, Hanselmann, M., and Heeren, R. M. A., “Mass spectrometry imaging for the investigation of intratumor heterogeneity”, in Advances in Cancer Research, vol. 134, Elsevier, 2017, pp. 201-230.
B. Balluff, Hanselmann, M., and Heeren, R. M. A., “Mass spectrometry imaging for the investigation of intratumor heterogeneity”, in Advances in Cancer Research, vol. 134, Elsevier, 2017, pp. 201-230.
C. Haltebourg, “Modeling of Heat Exchange Across the Ocean Surface as Measured by Active Thermography”, vol. Dissertation. Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ. Heidelberg, 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.
N. Krasowki, Beier, T., Knott, G. W., Köthe, U., Hamprecht, F. A., and Kreshuk, A., “Neuron Segmentation with High-Level Biological Priors”, IEEE Transactions on Medical Imaging, vol. 37, no. 4, 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.
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.
T. Hehn, “A probabilistic approach to learn complex differentiable split functions in decision trees using gradient ascent”, Heidelberg University, 2017.
C. Haubold, “Scalable Inference for Multi-Target Tracking on Proliferating Cells”. University of Heidelberg, 2017.
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.
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.
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.
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.
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.
M. Haußmann, Hamprecht, F. A., and Kandemir, M., “Variational Bayesian Multiple Instance Learning with Gaussian Processes”, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp. 6570-6579, 2017.PDF icon Technical Report (1.29 MB)

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