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2015
J. Kappes, Swoboda, P., Savchynskyy, B., Hazan, T., and Schnörr, C., “Probabilistic Correlation Clustering and Image Partitioning Using Perturbed Multicuts”, in Proc. SSVM, 2015.
M. Schiegg, Heuer, B., Haubold, C., Wolf, S., Köthe, U., and Hamprecht, F. A., “Proof-reading Guidance in Cell Tracking by Sampling from Tracking-by-assignment Models”, in ISBI. Proceedings, 2015, pp. 394-398.PDF icon Technical Report (648.55 KB)
M. Schiegg, Heuer, B., Haubold, C., Wolf, S., Köthe, U., and Hamprecht, F. A., “Proof-reading Guidance in Cell Tracking by Sampling from Tracking-by-assignment Models”, in ISBI. Proceedings, 2015, pp. 394-398.PDF icon Technical Report (648.55 KB)
M. Schiegg, Heuer, B., Haubold, C., Wolf, S., Köthe, U., and Hamprecht, F. A., “Proof-reading Guidance in Cell Tracking by Sampling from Tracking-by-assignment Models”, in ISBI. Proceedings, 2015, pp. 394-398.PDF icon Technical Report (648.55 KB)
D. Kondermann, Nair, R., Meister, S., Mischler, W., Güssefeld, B., Honauer, K., Hofmann, S., Brenner, C., and Jähne, B., “Stereo Ground Truth with Error Bars”, in Computer Vision – ACCV 2014: 12th Asian Conference on Computer Vision, Singapore, Singapore, November 1-5, 2014, Revised Selected Papers, Part V, Cham: Springer International Publishing, 2015, pp. 595–610.
D. Kondermann, Nair, R., Meister, S., Mischler, W., Güssefeld, B., Honauer, K., Hofmann, S., Brenner, C., and Jähne, B., “Stereo Ground Truth with Error Bars”, in Computer Vision – ACCV 2014: 12th Asian Conference on Computer Vision, Singapore, Singapore, November 1-5, 2014, Revised Selected Papers, Part V, Cham: Springer International Publishing, 2015, pp. 595–610.
C. Cali, Baghabra, J., Boges, D. J., Holst, G. R., Kreshuk, A., Hamprecht, F. A., Srinivasan, M., Lehväslaiho, H., and Magistretti, P. J., “Three-dimensional immersive virtual reality for studying cellular compartments in 3D models from EM preparations of neural tissues”, Journal of Comparative Neurology, vol. 524, pp. 23-38, 2015.
C. Cali, Baghabra, J., Boges, D. J., Holst, G. R., Kreshuk, A., Hamprecht, F. A., Srinivasan, M., Lehväslaiho, H., and Magistretti, P. J., “Three-dimensional immersive virtual reality for studying cellular compartments in 3D models from EM preparations of neural tissues”, Journal of Comparative Neurology, vol. 524, pp. 23-38, 2015.
J. P. Kauppi, Kandemir, M., Saarinen, V. M., Hirvenkari, L., Parkkonen, L., Klami, A., Hari, R., and Kaski, S., “Towards brain-activity-controlled information retrieval: Decoding image relevance from MEG signals”, NeuroImage, vol. 112, pp. 288-298, 2015.PDF icon Technical Report (2.39 MB)
J. P. Kauppi, Kandemir, M., Saarinen, V. M., Hirvenkari, L., Parkkonen, L., Klami, A., Hari, R., and Kaski, S., “Towards brain-activity-controlled information retrieval: Decoding image relevance from MEG signals”, NeuroImage, vol. 112, pp. 288-298, 2015.PDF icon Technical Report (2.39 MB)
A. Kreshuk, Funke, J., Cardona, A., and Hamprecht, F. A., “Who is talking to whom: synaptic partner detection in anisotropic volumes of insect brain”, MICCAI. Proceedings, vol. LNCS 9349. Springer, pp. 661-668, 2015.PDF icon Technical Report (2.14 MB)
2016
B. Güssefeld, Honauer, K., and Kondermann, D., “Creating Feasible Reflectance Data for Synthetic Optical Flow Datasets”, in Advances in Visual Computing - 12th International Symposium, {ISVC} 2016, Las Vegas, NV, USA, December 12-14, 2016, Proceedings, Part {I}, 2016.
K. Honauer, Johannsen, O., Kondermann, D., and Goldlücke, B., “A Dataset and Evaluation Methodology for Depth Estimation on 4D Light Fields”, in Computer Vision - ACCV 2016 : 13th Asian Conference on Computer Vision, Taipei, Taiwan, November 20-24, 2016, Revised Selected Papers, Part III, Cham, 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)
T. Beier, Andres, B., Köthe, U., and Hamprecht, F. A., “An Efficient Fusion Move Algorithm for the Minimum Cost Lifted Multicut Problem”, ECCV. Proceedings, vol. LNCS 9906. Springer, pp. 715-730, 2016.PDF icon Technical Report (4.89 MB)
M. von Borstel, Kandemir, M., Schmidt, P., Rao, M., Rajamani, K., and Hamprecht, F. A., “Gaussian process density counting from weak supervision”, ECCV. Proceedings, vol. LNCS 9905. Springer, pp. 365-380 , 2016.PDF icon Technical Report (1.71 MB)
C. Haubold, Ales, J., Wolf, S., and Hamprecht, F. A., “A Generalized Successive Shortest Paths Solver for Tracking Dividing Targets”, ECCV. Proceedings, vol. LNCS 9911. Springer, pp. 566-582, 2016.PDF icon Technical Report (1.18 MB)
C. Haubold, Ales, J., Wolf, S., and Hamprecht, F. A., “A Generalized Successive Shortest Paths Solver for Tracking Dividing Targets”, ECCV. Proceedings, vol. LNCS 9911. Springer, pp. 566-582, 2016.PDF icon Technical Report (1.18 MB)
D. Kondermann, Nair, R., Honauer, K., Krispin, K., Andrulis, J., Brock, A., Güssefeld, B., Rahimimoghaddam, M., Hofmann, S., Brenner, C., and Jähne, B., “The HCI Benchmark Suite: Stereo and Flow Ground Truth With Uncertainties for Urban Autonomous Driving”, in The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2016.
D. Kondermann, Nair, R., Honauer, K., Krispin, K., Andrulis, J., Brock, A., Güssefeld, B., Rahimimoghaddam, M., Hofmann, S., Brenner, C., and Jähne, B., “The HCI Benchmark Suite: Stereo and Flow Ground Truth With Uncertainties for Urban Autonomous Driving”, in The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2016.
E. Meijering, Carpenter, A. E., Peng, H., Hamprecht, F. A., and Olivo-Marin, J., “Imagining the future of bioimage analysis”, Nature Biotechnology, vol. 34, no. 12, pp. 1250-1255, 2016.PDF icon Technical Report (924.57 KB)
A. Biller, Badde, S., Nagel, A., Neumann, J. O., Wick, W., Hertenstein, A., Bendszus, M., Sahm, F., Benkhedah, N., and Kleesiek, J., “Improved Brain Tumor Classification by Sodium MR Imaging: Prediction of IDH Mutation Status and Tumor Progression”, American Journal of Neuroradiology, vol. 37 , pp. 66-73, 2016.
M. Schiegg, Diego, F., and Hamprecht, F. A., “Learning Diverse Models: The Coulomb Structured Support Vector Machine”, ECCV. Proceedings, vol. LNCS 9907. Springer, pp. 585-599, 2016.PDF icon Technical Report (2.54 MB)
J. Hendrik Kappes, Swoboda, P., Savchynskyy, B., Hazan, T., and Schnörr, C., “Multicuts and Perturb & MAP for Probabilistic Graph Clustering”, Journal of Mathematical Imaging and Vision, vol. 56, pp. 221–237, 2016.
J. H. Kappes, Swoboda, P., Savchynskyy, B., Hazan, T., and Schnörr, C., “Multicuts and Perturb & MAP for Probabilistic Graph Clustering”, J. Math. Imag. Vision, vol. 56, pp. 221–237, 2016.
O. Hosseini Jafari and Yang, M. Ying, “Real-time RGB-D based template matching pedestrian detection”, in Proceedings - IEEE International Conference on Robotics and Automation, 2016, vol. 2016-June, pp. 5520–5527.
C. Haubold, Schiegg, M., Kreshuk, A., Berg, S., Köthe, U., and Hamprecht, F. A., “Segmenting and Tracking Multiple Dividing Targets Using ilastik”, in Focus on Bio-Image Informatics, vol. 219, Springer, 2016, pp. 199-229.PDF icon Technical Report (4.46 MB)
C. Haubold, Schiegg, M., Kreshuk, A., Berg, S., Köthe, U., and Hamprecht, F. A., “Segmenting and Tracking Multiple Dividing Targets Using ilastik”, in Focus on Bio-Image Informatics, vol. 219, Springer, 2016, pp. 199-229.PDF icon Technical Report (4.46 MB)
F. Diego and Hamprecht, F. A., “Structured Regression Gradient Boosting”, CVPR. Proceedings. pp. 1459-1467, 2016.PDF icon Technical Report (3.97 MB)
M. Kandemir, Haußmann, M., Diego, F., Rajamani, K., van der Laak, J., and Hamprecht, F. A., “Variational weakly-supervised Gaussian processes”, BMVC. Proceedings. 2016.PDF icon Technical Report (3.28 MB)
M. Kandemir, Haußmann, M., Diego, F., Rajamani, K., van der Laak, J., and Hamprecht, F. A., “Variational weakly-supervised Gaussian processes”, BMVC. Proceedings. 2016.PDF icon Technical Report (3.28 MB)
J. Kleesiek, Petersen, J., Döring, M., Maier-Hein, K., Köthe, U., Wick, W., Hamprecht, F. A., Bendszus, M., and Biller, A., “Virtual Raters for Reproducible and Objective Assessments in Radiology”, Nature Scientific Reports, vol. 6, 2016.PDF icon Technical Report (2.81 MB)
M. Haußmann, “Weakly Supervised Detection with Gaussian Processes”, University of Heidelberg, 2016.
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.

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