Publications

Show only items where

Author

Type

Term

Year

Keyword

Export 224 results:

Author Title Type [ Year

]

Filters: Author is Fred A. Hamprecht [Clear All Filters]

2014

C. Zhang, Huber, F., Knop, M., and Hamprecht, F. A., “Yeast Cell Detection and Segmentation in Bright Field Microscopy”, in ISBI. Proceedings, 2014, pp. 1267-1270.

Technical Report (4.13 MB)

2015

A. Kreshuk, Walecki, R., Köthe, U., Gierthmühlen, M., Plachta, D., Genoud, C., Haastert-Talini, K., and Hamprecht, F. A., “Automated Tracing of Myelinated Axons and Detection of the Nodes of Ranvier in Serial Images of Peripheral Nerves”, Journal of Microscopy, vol. 259 (2), pp. 143-154, 2015.

M. Kandemir and Hamprecht, F. A., “Cell event detection in phase-contrast microscopy sequences from few annotations”, MICCAI. Proceedings, vol. LNCS 9351. Springer, pp. 316-323, 2015.

Technical Report (564.69 KB)

J. H. Kappes, 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., “A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems”, International Journal of Computer Vision, pp. 1-30, 2015.

Technical Report (1.5 MB)

Technical Report (5.12 MB)

M. Kandemir and Hamprecht, F. A., “The Deep Feed-Forward Gaussian Process: An Effective Generalization to Covariance Priors”, NIPS. Proceedings, vol. 44. pp. 145-159, 2015.

Supplementary Material (223.39 KB)

Technical Report (2.58 MB)

T. Beier, Hamprecht, F. A., and Kappes, J. H., “Fusion Moves for Correlation Clustering”, in CVPR. Proceedings, 2015, pp. 3507-3516.

Technical Report (1.19 MB)

M. Schiegg, Hanslovsky, P., Haubold, C., Köthe, U., Hufnagel, L., and Hamprecht, F. A., “Graphical Model for Joint Segmentation and Tracking of Multiple Dividing Cell”, Bioinformatics, vol. 31, no. 6, pp. 948-956, 2015.

Technical Report (534.29 KB)

N. Krasowski, Beier, T., Knott, G. W., Köthe, U., Hamprecht, F. A., and Kreshuk, A., “Improving 3D EM Data Segmentation by Joint Optimization over Boundary Evidence and Biological Priors”, in 12th {IEEE} International Symposium on Biomedical Imaging, {ISBI} 2015, Brooklyn, NY, USA, April 16-19, 2015, 2015, pp. 536-539.

Technical Report (2.25 MB)

J. Funke, Hamprecht, F. A., and Zhang, C., “Learning to Segment: Training Hierarchical Segmentation under a Topological Loss”, in MICCAI. Proceedings, Part III, 2015, vol. 9351, pp. 268-275.

Technical Report (2.92 MB)

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.

Technical Report (648.55 KB)

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.

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.

Technical Report (2.14 MB)

2016

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.

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.

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.

Technical Report (1.18 MB)

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.

Technical Report (924.57 KB)

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.

Technical Report (2.54 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.

Technical Report (4.46 MB)

F. Diego and Hamprecht, F. A., “Structured Regression Gradient Boosting”, CVPR. Proceedings. pp. 1459-1467, 2016.

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.

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.

Technical Report (2.81 MB)

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.

S. Peter, Diego, F., Hamprecht, F. A., and Nadler, B., “Cost-efficient Gradient Boosting”, NIPS, poster. 2017.

C. Haubold, Uhlmann, V., Unser, M., and Hamprecht, F. A., “Diverse M-best Solutions by Dynamic Programming”, GCPR. Proceedings, vol. LNCS 10496. Springer, pp. 255-267, 2017.

V. Uhlmann, Haubold, C., Hamprecht, F. A., and Unser, M., “Diverse Shortest Paths for Bioimage Analysis”, Bioinformatics, pp. 1-3, 2017.

S. Wolf, Schott, L., Köthe, U., and Hamprecht, F. A., “Learned Watershed: End-to-End Learning of Seeded Segmentation”, ICCV. pp. 2030-2038, 2017.

Technical Report (3.76 MB)

M. Kandemir, Hamprecht, F. A., Wojek, C., and Schmidt, U., “Maschinelles Lernen”, Patent, Patent Number WO2017032775A1, 2017.

Technical Report (317.04 KB)

T. Beier, 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., “Multicut brings automated neurite segmentation closer to human performance”, Nature Methods, vol. 14, no. 2, pp. 101-102, 2017.

N. Krasowki, Beier, T., Knott, G. W., Köthe, U., Hamprecht, F. A., and Kreshuk, A., “Neuron Segmentation with High-Level Biological Priors”, IEEE Transactions on Medical Imaging, vol. 37, no. 4, 2017.

V. Ulman, 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., “An Objective Comparison of Cell Tracking Algorithms”, Nature Methods, vol. 14, no. 12, pp. 1141-1152, 2017.

Technical Report (4.24 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.

Publications

Pages