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C. Kamann and Rother, C., “Benchmarking the Robustness of Semantic Segmentation Models”, in CVPR 2020, 2020.

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J. Kruse, Ardizzone, L., Rother, C., and Köthe, U., “Benchmarking Invertible Architectures on Inverse Problems”, i, 2019.

S. Weber, Nagy, A., Schüle, T., Schnörr, C., and Kuba, A., “A Benchmark Evaluation of Large-Scale Optimization Approaches to Binary Tomography”, in Discrete Geometry for Computer Imagery (DGCI 2006), 2006, vol. 4245, pp. 146-156.

Technical Report (301.1 KB)

A. Blattmann, Milbich, T., Dorkenwald, M., and Ommer, B., “Behavior-Driven Synthesis of Human Dynamics”, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2021.

M. Hissmann and Hamprecht, F. A., “Bayessche Schätzung von Höhenkarten aus der Wei\DF licht-Interferometrie”, in Oberflächenmesstechnik 2003, 2003, p. 187--196.

M. Hissmann and Hamprecht, F. A., “Bayesian surface estimation for white light interferometry”, Optical Engineering, vol. 44, pp. 1-9, 2005.

Technical Report (549.46 KB)

M. Haußmann, Gerwinn, S., and Kandemir, M., “Bayesian Prior Networks with PAC Training”, arXiv preprint arXiv:1906.00816, 2019.

M. Haußmann, “Bayesian Neural Networks for Probabilistic Machine Learning”. Heidelberg University, 2021.

J. Giebel, Gavrila, D. M., and Schnörr, C., “A Bayesian Framework for Multi-cue 3D Object Tracking”, in Computer Vision – ECCV 2004, 2004, vol. 3024, pp. 241-252.

M. Haußmann, Gerwinn, S., and Kandemir, M., “Bayesian Evidential Deep Learning with PAC Regularization ”, 3rd Symposium on Advances in Approximate Bayesian Inference . 2020.

B. Michael Kelm, Müller, N., Menze, B. H., and Hamprecht, F. A., “Bayesian Estimation of Smooth Parameter Maps for Dynamic Contrast-Enhanced MR Images with Block-ICM”, in Proc Computer Vision and Pattern Recognition Workshop (Mathematical Methods in Biomedical Image Analysis), 2006, pp. 96-103.

Technical Report (232.69 KB)

M. Hissmann, “Bayesian Estimation for White Light Interferometry”. University of Heidelberg, 2005.

P. Vincent Gehler, Rother, C., Blake, A., Minka, T., and Sharp, T., “Bayesian color constancy revisited”, in 26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR, 2008.

S. T. Radev, Mertens, U. K., Voss, A., Ardizzone, L., and Köthe, U., “BayesFlow: Learning complex stochastic models with invertible neural networks”, 2020.

PDF (5.36 MB)

J. Fehr and Burkhardt, H., “A Bag of Features Approach for 3D Shape Retrieval”, in Proceedings of the ISVC 2009, Part I, 2009, vol. 5875, pp. 34-43.

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S. Petra and Schnörr, C., “Average Case Recovery Analysis of Tomographic Compressive Sensing”, Linear Algebra and its Applications, vol. 441, pp. 168-198, 2014.

Technical Report (1.85 MB)

M. Jäger, Knoll, C., and Hamprecht, F. A., “Automatisierte Klassifikation von Laserschwei\DFprozessen durch Nutzung von 3D Signalverarbeitungs-Algorithmen”. Robert Bosch GmbH, Schwieberdingen and IWR, Uni Heidelberg, 2005.

B. Michael Kelm, Menze, B. H., and Hamprecht, F. A., “Automatische Lokalisation von Tumoren in 1H-NMR-spektroskopischen in vivo Aufnahmen”, in VDI-Berichte, 2005, vol. 1883, pp. 457-466.

Technical Report (221.54 KB)

D. Bister, Rohr, K., and Schnörr, C., “Automatische Bestimmung der Trajektorien von sich bewegenden Objekten aus einer Grauwertbildfolge”, in Mustererkennung 1990, 12. DAGM-Symposium, Oberkochen-Aalen, 1990, vol. 254, pp. 44–51.

T. Prange, “Automatic Segmentation of Neurons in Electron Microscopy Data with Membrane Defects”, University of Heidelberg, 2016.

C. Pape, “Automatic Segmentation of Neurites from Anisotropic EM-Imaging”, University of Heidelberg, 2016.

D. Wierzimok and Jähne, B., “Automatic particle tracking velocimetry beneath a wind-stressed wavy water surface with image processing”, in 5th International Symposium on Flow Visualization, 1989.

D. Wierzimok and Jähne, B., “Automatic particle tracking beneath a wind-stressed wavy water surface with image processing”, in Proc.\ 5th Int. Symposium Flow Visualization, Praque 1989, 1990, p. 943--956.

J. Keuchel, Naumann, S., Heiler, M., and Siegmund, A., “Automatic Land Cover Analysis for Tenerife by Supervised Classification using Remotely Sensed Data”, Remote Sensing of Environment, 2002.

C. M. Zechmann, Menze, B. H., Kelm, B. Michael, Zamecnik, P., Ikinger, U., Waldherr, R., Delorme, S., Hamprecht, F. A., and Bachert, P., “Automated vs. manual pattern recognition of 3D 1H MRSI data of patients with prostate cancer”, Academic Radiology, vol. 19, 6, pp. 675-684, 2012.

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.

A. Kreshuk, Straehle, C. N., Sommer, C., Köthe, U., Knott, G. W., and Hamprecht, F. A., “Automated Segmentation of Synapses in 3D EM Data”, in Eighth IEEE International Symposium on Biomedical Imaging (ISBI 2011). Proceedings, 2011, pp. 220-223.

B. Andres, “Automated Segmentation of Large 3D Images of Nervous Systems Using a Higher-order Graphical Model”. University of Heidelberg, 2011.

N. Krasowski, “Automated Segmentation for Connectomics Utilizing Higher-Order Biological Priors”. University of Heidelberg, 2016.

C. Scheelen, “Automated Quality Control in the Life Sciences”, University of Heidelberg, 2010.

R. Mikut, Dickmeis, T., Driever, W., Geurts, P., Hamprecht, F. A., Kausler, B. X., Ledesma-Carbayo, M., Marée, R., Mikula, K., Pantazis, P., Ronneberger, O., Santos, A., and Stotzka, R., “Automated Processing of Zebrafish Imaging Data: A Survey”, Zebrafish, vol. 10 (3), 2013.

Technical Report (1.73 MB)

M. Arnold, Bell, P., and Ommer, B., “Automated Learning of Self-Similarity and Informative Structures in Architecture”, in Scientific Computing & Cultural Heritage, 2013.

F. Diego, Reichinnek, S., Both, M., and Hamprecht, F. A., “Automated Identification of Neuronal Activity from Calcium Imaging by Sparse Dictionary Learning”, ISBI 2013. Proceedings, pp. 1058-1061, 2013.

Technical Report (2.82 MB)

B. Michael Kelm, Menze, B. H., Zechmann, C. M., Baudendistel, K. T., and Hamprecht, F. A., “Automated Estimation of Tumor Probability in Prostate MRSI: Pattern Recognition vs. Quantification”, Magnetic Resonance in Medicine, vol. 57, pp. 150-159, 2007.

Technical Report (348.05 KB)

A. Kreshuk, Köthe, U., Pax, E., Bock, D. D., and Hamprecht, F. A., “Automated Detection of Synapses in Serial Section Transmission Electron Microscopy Image Stacks”, PLoS ONE, vol. 9, p. 2, 2014.

Technical Report (16.66 MB)

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