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Publications

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Journal Article
Jäger, M, Kiel, A, Herten, D - P and Hamprecht, F A (2009). Analysis of Single-Molecule Fluorescence Spectroscopic Data with a Markov Modulated Poisson Process. ChemPhysChem. 10:14 2486-2495
Hayn, M, Beirle, S, Hamprecht, F A, Platt, U, Menze, B H and Wagner, T (2009). Analysing spatio-temporal patterns of the global NO2-distribution retrieved frome GOME satellite observations using a generalized additive model. Atmospheric Chemistry and Physics. 9 9367-9398PDF icon Technical Report (2.52 MB)
Kirchner, M, Saussen, B, Steen, H, Steen, J A J and Hamprecht, F A (2007). amsrpm: Robust Point Matching in Retention Time Alignment of LC/MS Data with R. Journal of Statistical Software. 18 1-12. http://www.jstatsoft.org/v18/i04/paper
Görlitz, L, Hamprecht, F A and Staudacher, M (2009). Allocation of particles to development processes. PatentPDF icon Technical Report (406.7 KB)
Lou, X, Schiegg, M and Hamprecht, F A (2014). Active Structured Learning for Cell Tracking: Algorithm, Framework and Usability. IEEE Transactions on Medical Imaging. 33 (4) 849-860PDF icon Technical Report (6.84 MB)
Kandemir, M, Hamprecht, F A, Wojek, C and Schmidt, U (2017). Active machine learning for training an event classification. Patent, Patent Number WO2017032775 A1
Röder, J, Kunzmann, K, Nadler, B and Hamprecht, F A (2012). Active Learning with Distributional Estimates. UAI 2012. Proceedings. 715-725PDF icon Technical Report (267.67 KB)
Hanselmann, M, Röder, J, Köthe, U, Renard, B Y, Heeren, R M A and Hamprecht, F A (2012). Active Learning for Convenient Annotation and Classification of Secondary Ion Mass Spectrometry Images. Analytical Chemistry. 85 (1) 147-155PDF icon Technical Report (2.58 MB)
Andres, B, Köthe, U, Kröger, T, Helmstaedter, M, Briggmann, K L, Denk, W and Hamprecht, F A (2012). 3D Segmentation of SBFSEM Images of Neuropil by a Graphical Model over Supervoxel Boundaries. Medical Image Analysis. 16 (2012) 796-805PDF icon Technical Report (20.85 MB)
inproceedings
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, Feuchtinger, A, Walch, A and Hamprecht, F A (2014). Digital Pathology: Multiple instance learning can detect Barrett'scancer. ISBI. Proceedings. 1348-1351PDF icon Technical Report (2.86 MB)
In Collection
Hader, S and Hamprecht, F A (2004). Two-Stage Classification with Automatic Feature Selection for an Industrial Application. Classification, the ubiquitous challenge: Proceedings of GfKl 2004. Springer. 137-144PDF icon Technical Report (518.16 KB)
Lou, X, Kloft, M, Rätsch, G and Hamprecht, F A (2014). Structured Learning from Cheap Data. Advanced Structured Prediction. The MIT PressPDF icon Technical Report (8.35 MB)
Eisele, H and Hamprecht, F A (2003). A new approach for defect detection in X-ray CT images. Pattern Recognition. Springer. 2449 345-352PDF icon Technical Report (398.88 KB)
Hamprecht, F A and Agrell, E (2003). Exploring a space of materials: spatial sampling design and subset selection. Experimental Design for Combinatorial and High Throughput Materials Development. WileyPDF icon Technical Report (2.28 MB)
Hader, S and Hamprecht, F A (2003). Efficient Density Clustering. Between Data Science and Applied Data Analysis. Springer. 39-48
Hamprecht, F A (2004). Classification. Practical Handbook on Image Processing for Scientific and Technical Applications. CRC Press. 509-519PDF icon Technical Report (320.84 KB)
Conference Proceedings
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)
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)
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)
Diego, F and Hamprecht, F A (2016). Structured Regression Gradient Boosting. CVPR. Proceedings. 1459-1467PDF icon Technical Report (3.97 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 (2019). Sampling-Free Variational Inference of Bayesian Neural Networks by Variance Backpropagation. UAI. Proceedings. 563-573PDF icon Technical Report (1.04 MB)
Schnörr, C and Jähne, B (2007). Pattern Recognition, 29th DAGM Symposium, Heidelberg, Germany, September 12-14. Springer. 4713
(2007). Pattern Recognition – 29th DAGM Symposium. LCNS. Springer. 4713
Wolf, S, Pape, C, Bailoni, A, Rahaman, N, Kreshuk, A, Köthe, U and Hamprecht, F A (2018). The Mutex Watershed: Efficient, Parameter-Free Image Partitioning. ECCV. Proceedings. Springer. 571-587
Kirschbaum, E, Haußmann, M, Wolf, S, Sonntag, H, Schneider, J, Elzoheiry, S, Kann, O, Durstewitz, D and Hamprecht, F A (2019). LeMoNADe: Learned Motif and Neuronal Assembly Detection in calcium imaging videos. ICLR. Proceedings
Weiler, M, Hamprecht, F A and Storath, M (2018). Learning Steerable Filters for Rotation Equivariant CNNs. CVPR. Proceedings. 849-858PDF icon Technical Report (1.35 MB)
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, 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)
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)
Draxler, F, Veschgini, K, Salmhofer, M and Hamprecht, F A (2018). Essentially No Barriers in Neural Network Energy Landscape. ICML. Proceedings. 80 1308--1317PDF icon Technical Report (685.93 KB)
Hehn, T and Hamprecht, F A (2018). End-to-end Learning of Deterministic Decision Trees. German Conference on Pattern Recognition. Proceedings. Springer. LNCS 11269 612-627PDF icon Technical Report (1.4 MB)
Cerrone, L, Zeilmann, A and Hamprecht, F A (2019). End-to-End Learned Random Walker for Seeded Image Segmentation. CVPR. Proceedings. 12559-12568
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)

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