Publications

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2015
Kappes, J H, Andres, B, Hamprecht, F A, Schnörr, C, Nowozin, S, Batra, D, Kim, S, Kausler, B X, Kröger, T, Lellmann, J, Komodakis, N, Savchynskyy, B and Rother, C (2015). A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems. Int.~J.~Comp.~VisionPDF icon Technical Report (5.12 MB)
Kappes, J H, Andres, B, Hamprecht, F A, Schnörr, C, Nowozin, S, Batra, D, Kim, S, Kausler, B X, Kröger, T, Lellmann, J, Komodakis, N, Savchynskyy, B and Rother, C (2015). A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems. International Journal of Computer Vision. 1-30PDF icon Technical Report (1.5 MB)
Kandemir, M and Hamprecht, F A (2015). The Deep Feed-Forward Gaussian Process: An Effective Generalization to Covariance Priors. NIPS. Proceedings. 44 145-159PDF icon Supplementary Material (223.39 KB)PDF icon Technical Report (2.58 MB)
Beier, T, Hamprecht, F A and Kappes, J H (2015). Fusion Moves for Correlation Clustering. CVPR. Proceedings. 3507-3516PDF icon Technical Report (1.19 MB)
Schiegg, M, Hanslovsky, P, Haubold, C, Köthe, U, Hufnagel, L and Hamprecht, F A (2015). Graphical Model for Joint Segmentation and Tracking of Multiple Dividing Cell. Bioinformatics. 31 948-956. http://bioinformatics.oxfordjournals.org/content/early/2014/11/17/bioinformatics.btu764.full.pdf?keytype=ref&ijkey=mTXWsiFrci7R8tcPDF icon Technical Report (534.29 KB)
Krasowski, N, Beier, T, Knott, G W, Köthe, U, Hamprecht, F A and Kreshuk, A (2015). Improving 3D EM Data Segmentation by Joint Optimization over Boundary Evidence and Biological Priors. 12th {IEEE} International Symposium on Biomedical Imaging, {ISBI} 2015, Brooklyn, NY, USA, April 16-19, 2015. 536-539PDF icon Technical Report (2.25 MB)
Funke, J, Hamprecht, F A and Zhang, C (2015). Learning to Segment: Training Hierarchical Segmentation under a Topological Loss. MICCAI. Proceedings, Part III. Springer. 9351 268-275PDF icon Technical Report (2.92 MB)
Schiegg, M, Heuer, B, Haubold, C, Wolf, S, Köthe, U and Hamprecht, F A (2015). Proof-reading Guidance in Cell Tracking by Sampling from Tracking-by-assignment Models. ISBI. Proceedings. 394-398PDF icon Technical Report (648.55 KB)
Cali, C, Baghabra, J, Boges, D J, Holst, G R, Kreshuk, A, Hamprecht, F A, Srinivasan, M, Lehväslaiho, H and Magistretti, P J (2015). Three-dimensional immersive virtual reality for studying cellular compartments in 3D models from EM preparations of neural tissues. Journal of Comparative Neurology. 524 23-38
Kreshuk, A, Funke, J, Cardona, A and Hamprecht, F A (2015). Who is talking to whom: synaptic partner detection in anisotropic volumes of insect brain. MICCAI. Proceedings. Springer. LNCS 9349 661-668PDF icon Technical Report (2.14 MB)
2016
Beier, T, Andres, B, Köthe, U and Hamprecht, F A (2016). An Efficient Fusion Move Algorithm for the Minimum Cost Lifted Multicut Problem. ECCV. Proceedings. Springer. LNCS 9906 715-730PDF icon Technical Report (4.89 MB)
von Borstel, M, Kandemir, M, Schmidt, P, Rao, M, Rajamani, K and Hamprecht, F A (2016). Gaussian process density counting from weak supervision. ECCV. Proceedings. Springer. LNCS 9905 365-380 PDF icon Technical Report (1.71 MB)
Haubold, C, Ales, J, Wolf, S and Hamprecht, F A (2016). A Generalized Successive Shortest Paths Solver for Tracking Dividing Targets. ECCV. Proceedings. Springer. LNCS 9911 566-582PDF icon Technical Report (1.18 MB)
Meijering, E, Carpenter, A E, Peng, H, Hamprecht, F A and Olivo-Marin, J (2016). Imagining the future of bioimage analysis. Nature Biotechnology. 34 1250-1255PDF icon Technical Report (924.57 KB)
Schiegg, M, Diego, F and Hamprecht, F A (2016). Learning Diverse Models: The Coulomb Structured Support Vector Machine. ECCV. Proceedings. Springer. LNCS 9907 585-599PDF icon Technical Report (2.54 MB)
Haubold, C, Schiegg, M, Kreshuk, A, Berg, S, Köthe, U and Hamprecht, F A (2016). Segmenting and Tracking Multiple Dividing Targets Using ilastik. Focus on Bio-Image Informatics. Springer. 219 199-229PDF icon Technical Report (4.46 MB)
Diego, F and Hamprecht, F A (2016). Structured Regression Gradient Boosting. CVPR. Proceedings. 1459-1467PDF icon Technical Report (3.97 MB)
Kandemir, M, Haußmann, M, Diego, F, Rajamani, K, van der Laak, J and Hamprecht, F A (2016). Variational weakly-supervised Gaussian processes. BMVC. ProceedingsPDF icon Technical Report (3.28 MB)
Kleesiek, J, Petersen, J, Döring, M, Maier-Hein, K, Köthe, U, Wick, W, Hamprecht, F A, Bendszus, M and Biller, A (2016). Virtual Raters for Reproducible and Objective Assessments in Radiology. Nature Scientific Reports. 6PDF icon Technical Report (2.81 MB)
2017
Kandemir, M, Hamprecht, F A, Wojek, C and Schmidt, U (2017). Active machine learning for training an event classification. Patent, Patent Number WO2017032775 A1
Peter, S, Diego, F, Hamprecht, F A and Nadler, B (2017). Cost-efficient Gradient Boosting. NIPS, poster
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
Uhlmann, V, Haubold, C, Hamprecht, F A and Unser, M (2017). Diverse Shortest Paths for Bioimage Analysis. Bioinformatics. 1-3
Wolf, S, Schott, L, Köthe, U and Hamprecht, F A (2017). Learned Watershed: End-to-End Learning of Seeded Segmentation. ICCV. 2030-2038PDF icon Technical Report (3.76 MB)
Kandemir, M, Hamprecht, F A, Wojek, C and Schmidt, U (2017). Maschinelles Lernen. Patent, Patent Number WO2017032775A1PDF icon Technical Report (317.04 KB)
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
Ulman, V, 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 (2017). An Objective Comparison of Cell Tracking Algorithms. Nature Methods. 14 1141-1152PDF icon Technical Report (4.24 MB)
Peter, S, Kirschbaum, E, Both, M, Campbell, L A, Harvey, B K, Heins, C, Durstewitz, D, Diego, F and Hamprecht, F A (2017). Sparse convolutional coding for neuronal assembly detection. NIPS, poster
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)

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