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B. H. Menze, Wormit, M., Bachert, P., Lichy, M. P., Schlemmer, H. - P., and Hamprecht, F. A., “Classification of in vivo magnetic resonance spectra”, in Classification in ubiquitous challenge: Proceedings of the GfKl 2004, 2004, pp. 362-369.

Technical Report (240.1 KB)

F. O. Kaster, Kelm, B. Michael, Zechmann, C. M., Weber, M. - A., Hamprecht, F. A., and Nix, O., “Classification of Spectroscopic Images in the DIROlab Environment”, in World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany, 2009, vol. 25/V, p. 252--255.

Technical Report (145.73 KB)

M. F. Carlsohn, Menze, B. H., Kelm, B. Michael, Hamprecht, F. A., Kercek, A., Leitner, R., and Polder, G., “Color image processing”, vol. 7(17), R. Lukac and Plataniotis, K. N., Eds. CRC Press, 2006, pp. 393-419.

J. H. Kappes, Andres, B., Hamprecht, F. A., Schnörr, C., Nowozin, S., Batra, D., Sungwoong, K., Kausler, B. X., Lellmann, J., Komodakis, N., and Rother, C., “A Comparative Study of Modern Inference Techniques for Discrete Energy Minimization Problems”, in CVPR 2013. Proceedings, 2013.

Technical Report (1.35 MB)

J. H. Kappes, Andres, B., Hamprecht, F. A., Schnörr, C., Nowozin, S., Batra, D., Kim, S., Kausler, B. X., Lellmann, J., Komodakis, N., and Rother, C., “A Comparative Study of Modern Inference Techniques for Discrete Energy Minimization Problem”, in CVPR, 2013.

Technical Report (1.35 MB)

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, vol. 115, pp. 155–184, 2015.

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, vol. 115, pp. 155–184, 2015.

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, vol. 115, pp. 155–184, 2015.

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”, CoRR, 2014.

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)

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”, Int.~J.~Comp.~Vision, 2015.

Technical Report (5.12 MB)

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”, CoRR, vol. abs/1404.0533, 2014.

Technical Report (3.32 MB)

F. O. Kaster, Weber, M. - A., and Hamprecht, F. A., “Comparative Validation of Graphical Models for Learning Tumor Segmentations from Noisy Manual Annotations”, in LNCS, 2011, vol. LNCS 6533, pp. 74-85.

Technical Report (544.56 KB)

C. Weber, Zechmann, C. M., Kelm, B. Michael, Zamecnik, R., Hendricks, D., Waldherr, R., Hamprecht, F. A., Delorme, S., Bachert, P., and Ikinger, U., “Comparison of correctness of manuel and automatic evaluation of MR-spectrum with prostrate cancer”, Der Urologe, vol. 46, p. 1252, 2007.

B. H. Menze, Kelm, B. Michael, Masuch, R., Himmelreich, U., Bachert, P., Petrich, W., and Hamprecht, F. A., “A Comparison of Random Forest and its Gini Importance with Standard Chemometric Methods for the Feature Selection and Classification of Spectral Data”, BMC Bioinformatics, vol. 10:213, 2009.

Technical Report (675 KB)

M. Kirchner, Renard, B. Y., Köthe, U., Pappin, D. J., Hamprecht, F. A., Steen, J. A. J., and Steen, H., “Computational Protein Profile Similarity Screening for Quantitative Mass Spectrometry Experiments”, Bioinformatics, vol. 26 (1), pp. 77-83, 2010.

Technical Report (380.19 KB)

M. Kandemir and Hamprecht, F. A., “Computer-aided diagnosis from weak supervision: A benchmarking study”, Computerized Medical Imaging and Graphics, vol. 42, pp. 44-50, 2014.

Technical Report (4.28 MB)

M. Hanselmann, Kirchner, M., Renard, B. Y., Amstalden, E. R., Glunde, K., Heeren, R. M. A., and Hamprecht, F. A., “Concise Representation of MS Images by Probabilistic Latent Semantic Analysis”, Analytical Chemistry, vol. 80, pp. 9649-9658, 2008.

Technical Report (3.91 MB)

M. Schiegg, Hanslovsky, P., Kausler, B. X., Hufnagel, L., and Hamprecht, F. A., “Conservation Tracking”, in ICCV 2013. Proceedings, 2013, p. 2928--2935.

Technical Report (5.22 MB)

B. Maco, Holtmaat, A., Cantoni, M., Kreshuk, A., Straehle, C. N., Hamprecht, F. A., and Knott, G. W., “Correlative in vivo 2 photon and focused ion beam scanning electron microscopy of cortical neurons”, PloS one, vol. 8 (2), 2013.

Technical Report (2.13 MB)

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

T. Beier, Kröger, T., Kappes, J. H., Köthe, U., and Hamprecht, F. A., “Cut, Glue and Cut: A Fast, Approximate Solver for Multicut Partitioning”, in 2014 {IEEE} Conference on Computer Vision and Pattern Recognition, {CVPR} 2014, Columbus, OH, USA, June 23-28, 2014, 2014.

Technical Report (10.06 MB)

D

M. Haußmann, Hamprecht, F. A., and Kandemir, M., “Deep Active Learning with Adaptive Acquisition”, IJCAI. Proceedings. pp. 2470-2476, 2019.

Technical Report (137.6 KB)

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)

X. Lou, Kaster, F. O., Lindner, M., Kausler, B. X., Köthe, U., Höckendorf, B., Wittbrodt, J., Jänicke, H., and Hamprecht, F. A., “DELTR: Digital Embryo Lineage Tree Reconstructor”, in Eighth IEEE International Symposium on Biomedical Imaging (ISBI). Proceedings, 2011, pp. 1557-1560.

Technical Report (1.44 MB)

M. Frank, Plaue, M., and Hamprecht, F. A., “Denoising of Continuous-Wave Time-Of-Flight Depth Images Using Confidence Measures”, Optical Engineering, vol. 48, 077003, 2009.

Technical Report (2.5 MB)

C. Decker and Hamprecht, F. A., “Detecting individual body parts improves mouse behavior classification”, in Workshop on visual observation and analysis of Vertebrate And Insect Behavior (VAIB), 22nd International Conference on Pattern Recognition (ICPR). Proceedings, 2014.

Technical Report (1.48 MB)

L. Görlitz, Hamprecht, F. A., and Staudacher, M., “Detektion von Partikeln in Intensitätsbildern mit Hilfe eines morphologischen Skalenraumes”. Robert-Bosch GmbH, University of Heidelberg, 2005.

X. Lou, Kirchner, M., Renard, B. Y., Köthe, U., Graf, C., Lee, C., Steen, J. A. J., Steen, H., Mayer, M. P., and Hamprecht, F. A., “Deuteration Distribution Estimation with Improved Sequence Coverage for HX/MS Experiments”, Bioinformatics, vol. 26(12), pp. 1535-1541, 2010.

Technical Report (518.01 KB)

F. A. Hamprecht, Cohen, A. J., Tozer, D. J., and Handy, N. C., “Development and assessment of new exchange-correlation functionals”, Journal of Chemical Physics, vol. 109, pp. 6264-6271, 1998.

J. A. J. Steen, Steen, H., Georgi, A., Parker, K. C., Springer, M., Kirchner, M., Hamprecht, F. A., and Kirschner, M. W., “Different Phosphorylation States of the Anaphase Promoting Complex in Response to Anti-Mitotic Drugs: A Quantitative Proteomic Analysis”, Proceedings of the National Academy of Sciences, vol. 105, pp. 6069-6074, 2008.

Technical Report (173.02 KB)

A. Vijayan, Tofanelli, R., Strauss, S., Cerrone, L., Wolny, A., Strohmeier, J., Kreshuk, A., Hamprecht, F. A., Smith, R. S., and Schneitz, K., “A Digital 3D Reference Atlas Reveals Cellular Growth Patterns Shaping the Arabidopsis Ovule”, eLife, 2021.

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.

Technical Report (2.86 MB)

B. X. Kausler, Schiegg, M., Andres, B., Lindner, M., Köthe, U., Leitte, H., Wittbrodt, J., Hufnagel, L., and Hamprecht, F. A., “A Discrete Chain Graph Model for 3d+t Cell Tracking with High Misdetection Robustness”, in ECCV 2012. Proceedings, 2012, vol. 7574, pp. 144-157.

Technical Report (809.07 KB)

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.

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