HCI Logo
Heidelberg University Logo
IWR Logo

Publications

Export 224 results:
Author Title [ Type(Asc)] Year
Filters: Author is Fred A. Hamprecht  [Clear All Filters]
Journal Article
M. Jäger, Kiel, A., Herten, D. - P., and Hamprecht, F. A., “Analysis of Single-Molecule Fluorescence Spectroscopic Data with a Markov Modulated Poisson Process”, ChemPhysChem, vol. 10:14, pp. 2486-2495, 2009.
M. Hayn, Beirle, S., Hamprecht, F. A., Platt, U., Menze, B. H., and Wagner, T., “Analysing spatio-temporal patterns of the global NO2-distribution retrieved frome GOME satellite observations using a generalized additive model”, Atmospheric Chemistry and Physics, vol. 9, pp. 9367-9398, 2009.PDF icon Technical Report (2.52 MB)
M. Kirchner, Saussen, B., Steen, H., Steen, J. A. J., and Hamprecht, F. A., “amsrpm: Robust Point Matching in Retention Time Alignment of LC/MS Data with R”, Journal of Statistical Software, vol. 18, pp. 1-12, 2007.
L. Görlitz, Hamprecht, F. A., and Staudacher, M., “Allocation of particles to development processes”, Patent, 2009.PDF icon Technical Report (406.7 KB)
X. Lou, Schiegg, M., and Hamprecht, F. A., “Active Structured Learning for Cell Tracking: Algorithm, Framework and Usability”, IEEE Transactions on Medical Imaging, vol. 33 (4), pp. 849-860, 2014.PDF icon Technical Report (6.84 MB)
M. Kandemir, Hamprecht, F. A., Wojek, C., and Schmidt, U., “Active machine learning for training an event classification”, Patent, Patent Number WO2017032775 A1, 2017.
J. Röder, Kunzmann, K., Nadler, B., and Hamprecht, F. A., “Active Learning with Distributional Estimates”, UAI 2012. Proceedings, pp. 715-725, 2012.PDF icon Technical Report (267.67 KB)
M. Hanselmann, Röder, J., Köthe, U., Renard, B. Y., Heeren, R. M. A., and Hamprecht, F. A., “Active Learning for Convenient Annotation and Classification of Secondary Ion Mass Spectrometry Images”, Analytical Chemistry, vol. 85 (1), pp. 147-155, 2012.PDF icon Technical Report (2.58 MB)
B. Andres, Köthe, U., Kröger, T., Helmstaedter, M., Briggmann, K. L., Denk, W., and Hamprecht, F. A., “3D Segmentation of SBFSEM Images of Neuropil by a Graphical Model over Supervoxel Boundaries”, Medical Image Analysis, vol. 16 (2012), pp. 796-805, 2012.PDF icon Technical Report (20.85 MB)
inproceedings
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)
M. Kandemir, Feuchtinger, A., Walch, A., and Hamprecht, F. A., “Digital Pathology: Multiple instance learning can detect Barrett'scancer”, ISBI. Proceedings. pp. 1348-1351, 2014.PDF icon Technical Report (2.86 MB)
In Collection
S. Hader and Hamprecht, F. A., “Two-Stage Classification with Automatic Feature Selection for an Industrial Application”, Classification, the ubiquitous challenge: Proceedings of GfKl 2004. Springer, pp. 137-144, 2004.PDF icon Technical Report (518.16 KB)
X. Lou, Kloft, M., Rätsch, G., and Hamprecht, F. A., “Structured Learning from Cheap Data”, Advanced Structured Prediction. The MIT Press, 2014.PDF icon Technical Report (8.35 MB)
H. Eisele and Hamprecht, F. A., “A new approach for defect detection in X-ray CT images”, Pattern Recognition, vol. 2449. Springer, pp. 345-352, 2003.PDF icon Technical Report (398.88 KB)
F. A. Hamprecht and Agrell, E., “Exploring a space of materials: spatial sampling design and subset selection”, Experimental Design for Combinatorial and High Throughput Materials Development. Wiley, 2003.PDF icon Technical Report (2.28 MB)
S. Hader and Hamprecht, F. A., “Efficient Density Clustering”, Between Data Science and Applied Data Analysis. Springer, pp. 39-48, 2003.
F. A. Hamprecht, “Classification”, Practical Handbook on Image Processing for Scientific and Technical Applications. CRC Press, pp. 509-519, 2004.PDF icon Technical Report (320.84 KB)
Conference Proceedings
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)
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. 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)
F. Diego and Hamprecht, F. A., “Structured Regression Gradient Boosting”, CVPR. Proceedings. pp. 1459-1467, 2016.PDF icon Technical Report (3.97 MB)
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., “Sampling-Free Variational Inference of Bayesian Neural Networks by Variance Backpropagation”, UAI. Proceedings. pp. 563-573, 2019.PDF icon Technical Report (1.04 MB)
C. Schnörr and Jähne, B., “Pattern Recognition, 29th DAGM Symposium, Heidelberg, Germany, September 12-14”, vol. 4713. Springer, 2007.
F. A. Hamprecht, Schnörr, C., and Jähne, B., Eds., “Pattern Recognition – 29th DAGM Symposium”, LCNS, vol. 4713. Springer, 2007.
S. Wolf, Pape, C., Bailoni, A., Rahaman, N., Kreshuk, A., Köthe, U., and Hamprecht, F. A., “The Mutex Watershed: Efficient, Parameter-Free Image Partitioning”, ECCV. Proceedings. Springer, pp. 571-587, 2018.
E. Kirschbaum, Haußmann, M., Wolf, S., Sonntag, H., Schneider, J., Elzoheiry, S., Kann, O., Durstewitz, D., and Hamprecht, F. A., “LeMoNADe: Learned Motif and Neuronal Assembly Detection in calcium imaging videos”, ICLR. Proceedings. 2019.
M. Weiler, Hamprecht, F. A., and Storath, M., “Learning Steerable Filters for Rotation Equivariant CNNs”, CVPR. Proceedings. pp. 849-858, 2018.PDF icon Technical Report (1.35 MB)
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)
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)
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)
F. Draxler, Veschgini, K., Salmhofer, M., and Hamprecht, F. A., “Essentially No Barriers in Neural Network Energy Landscape”, ICML. Proceedings, vol. 80. p. 1308--1317, 2018.PDF icon Technical Report (685.93 KB)
T. Hehn and Hamprecht, F. A., “End-to-end Learning of Deterministic Decision Trees”, German Conference on Pattern Recognition. Proceedings, vol. LNCS 11269. Springer, pp. 612-627, 2018.PDF icon Technical Report (1.4 MB)
L. Cerrone, Zeilmann, A., and Hamprecht, F. A., “End-to-End Learned Random Walker for Seeded Image Segmentation”, CVPR. Proceedings. pp. 12559-12568, 2019.
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)

Pages