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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)
Straehle, C N, Köthe, U, Briggman, K, Denk, W and Hamprecht, F A (2012). Seeded watershed cut uncertainty estimators for guided interactive segmentation. CVPR 2012. Proceedings. 765 - 772PDF icon Technical Report (2.84 MB)
Andres, B, Köthe, U, Helmstaedter, M, Denk, W and Hamprecht, F A (2008). Segmentation of SBFSEM Volume Data of Neural Tissue by Hierarchical Classification. Pattern Recognition. 30th DAGM Symposium Munich, Germany, June 10-13, 2008. Proceedings. Springer. 5096 142-152PDF icon Technical Report (1.21 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)
Andres, B, Hamprecht, F A and Garbe, C S (2007). Selection of Local Optical Flow Models by Means of Residual Analysis. Pattern Recognition. Springer. 4713 72-81PDF icon Technical Report (229.64 KB)
Andres, B, Hamprecht, F A and Garbe, C S (2007). Selection of Local Optical Flow Models by Means of Residual Analysis. Pattern Recognition. Springer. 4713 72-81PDF icon Technical Report (229.64 KB)
Andres, B, Garbe, C S, Schnörr, C and Jähne, B (2007). Selection of local optical flow models by means of residual analysis. Proceedings of the 29th DAGM Symposium on Pattern Recognition. Springer. 72--81
Andres, B, Garbe, C S, Schnörr, C and Jähne, B (2007). Selection of local optical flow models by means of residual analysis. Proceedings of the 29th DAGM Symposium on Pattern Recognition. Springer. 72--81
Staudacher, M, Hamprecht, F A and Görlitz, L (2009). Self Adjustment of Scanning Electron Microscopes / Selbstadaptivität von Rasterelektronenmikroskopen. Patent, Patent Number WO2009062781A1PDF icon Technical Report (46.64 KB)
Maco, B, Cantoni, M, Holtmaat, A, Kreshuk, A, Hamprecht, F A and Knott, G W (2014). Semiautomated Correlative 3D Electron Microscopy of In Vivo Imaged Axons and Dendrites. Nature Protocols. 9 1354-1366PDF icon Technical Report (2.01 MB)
Drory, A, Haubold, C, Avidan, S and Hamprecht, F A (2014). Semi-Global Matching: A Principled Derivation in Terms of Message Passing. GCPR. Proceedings. 43-53PDF icon Technical Report (2.6 MB)
Görlitz, L, Menze, B H, Weber, M - A and Kelm, B Michael (2007). Semi-Supervised Tumor Detection in MRSI With Discriminative Random Fields. Pattern Recognition. Springer. 4713 224-233PDF icon Technical Report (872.46 KB)
Görlitz, L, Menze, B H, Weber, M - A and Kelm, B Michael (2007). Semi-Supervised Tumor Detection in MRSI With Discriminative Random Fields. Pattern Recognition. Springer. 4713 224-233PDF icon Technical Report (872.46 KB)
Hanselmann, M, Voss, B, Renard, B Y, Lindner, M, Köthe, U, Kirchner, M and Hamprecht, F A (2011). SIMA: Simultaneous Multiple Alignment of LC/MS Peak Lists. Bioinformatics. 27 (7) 987-993PDF icon Technical Report (2.2 MB)
Köthe, U, Herrmannsdörfer, F, Kats, I and Hamprecht, F A (2014). SimpleSTORM: a fast, self-calibrating reconstruction algorithm for localization microscopy. Histochemistry and Cell Biology. 141 613-627PDF icon Technical Report (2.29 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
Diego, F and Hamprecht, F A (2014). Sparse Space-Time Deconvolution for Calcium Image Analysis. NIPS. Proceedings. 64-72. http://papers.nips.cc/paper/5342-sparse-space-time-deconvolution-for-calcium-image-analysisPDF icon Technical Report (5.27 MB)
Rahaman, N, Arpit, D, Baratin, A, Draxler, F, Lin, M, Hamprecht, F A, Bengio, Y and Courville, A (2018). On the spectral bias of deep neural networks. arXiv preprint arXiv:1806.08734
Jäger, M, Humbert, S and Hamprecht, F A (2008). Sputter Tracking for the Automatic Monitoring of Industrial Laser Welding Processes. IEEE Transactions on Industrial Electronics. 55 2177-2184PDF icon Technical Report (1.83 MB)
Hamprecht, F A, Peter, C, Daura, X, Thiel, W and van Gunsteren, W F (2001). A strategy for analysis of (molecular) equilibrium simulations: configuration space density estimation, clustering and visualization. Journal of Chemical Physics. 114 2079-2089
Lou, X and Hamprecht, F A (2011). Structured Learning for Cell Tracking. NIPS 2011. Proceedings. 1296-1304PDF icon Technical Report (1.41 MB)
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)
Lou, X and Hamprecht, F A (2012). Structured Learning from Partial Annotations. ICML 2012. Proceedings. http://icml.cc/discuss/2012/753.htmlPDF icon Technical Report (843.45 KB)
Diego, F and Hamprecht, F A (2016). Structured Regression Gradient Boosting. CVPR. Proceedings. 1459-1467PDF icon Technical Report (3.97 MB)
T
Frank, M, Plaue, M, Rapp, H, Köthe, U, Jähne, B and Hamprecht, F A (2009). Theoretical and Experimental Error Analysis of Continuous-Wave Time-Of-Flight Range Cameras. Optical Engineering. 48, 013602PDF icon Technical Report (2.03 MB)
Frank, M, Plaue, M, Rapp, H, Köthe, U, Jähne, B and Hamprecht, F A (2009). Theoretical and experimental error analysis of continuous-wave time-of-flight range cameras. Opt. Eng. 48 013602
Rapp, H, Frank, M, Hamprecht, F A and Jähne, B (2008). A Theoretical and Experimental Investigation of the Systematic Errors and Statistical Uncertainties of Time-of-Flight Cameras. Int. J. Intelligent Systems Technologies and Applications. 5 402-413PDF icon Technical Report (798.23 KB)
Rapp, H, Frank, M, Hamprecht, F A and Jähne, B (2008). A theoretical and experimental investigation of the systematic errors and statistical uncertainties of time-of-flight cameras. Int. J. Intelligent Systems Technologies and Applications. 5 402--413
Rapp, H, Frank, M, Hamprecht, F A and Jähne, B (2007). A theoretical and experimental investigation of the systematic errors and statistical uncertainties of time-of-flight cameras. Proc.\ Dyn3D Workshop, Heidelberg, Sept. 11, 2007. ZESS, Univ.\ Siegen
Bühl, M and Hamprecht, F A (1998). Theoretical Investigation of NMR Chemical Shifts and Reactivities of Oxovanadium (V) Compounds. Journal of Computational Chemistry. 19 113-122
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
Schmähling, J, Hamprecht, F A and Hoffmann, D M P (2006). A three-dimensional measure of surface roughness based on mathematical morphology. International Journal of Machine Tools and Manufacture. 46 (14) 1764-1769PDF icon Technical Report (524.97 KB)
Kubinyi, H, Hamprecht, F A and Mietzner, T (1998). Threedimensional Quantitative Similarity-Activity Relationships (3DQSiAR) from SEAL Similarity Matrices. Journal of Medicinal Chemistry. 41 2553-2564
Hanselmann, M, Köthe, U, Kirchner, M, Renard, B Y, Amstalden, E R, Glunde, K, Heeren, R M A and Hamprecht, F A (2009). Towards Digital Staining using Imaging Mass Spectrometry and Random Forests. Journal of Proteome Research. 8 3558-3567PDF icon Technical Report (1.47 MB)
Fiaschi, L, Diego, F, Grosser, K - H, Schiegg, M, Köthe, U, Zlatic, M and Hamprecht, F A (2014). Tracking indistinguishable translucent objects over time using weakly supervised structured learning. CVPR. Proceedings. 2736 - 2743PDF icon Technical Report (1.47 MB)

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