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Author [ Title(Desc)] Type Year
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A
C. Decker, “Automated Animal Behavior Classification”, University of Heidelberg, 2014.
B. F. Tek, Kröger, T., Mikula, S., and Hamprecht, F. A., “Automated Cell Nucleus Detection for Large-Volume Electron Microscopy of Neural Tissue”, in ISBI. Proceedings, 2014, pp. 69-72.PDF icon Technical Report (533.92 KB)
H. Eisele, “Automated defect detection and evaluation in X-ray CT images”. University of Heidelberg, 2002.
A. Kreshuk, Stankiewicz, M., Lou, X., Kirchner, M., Hamprecht, F. A., and Mayer, M. P., “Automated detection and analysis of bimodal isotope peak distribution in H/D exchange mass spectrometry using HeXicon”, International Journal of Mass Spectrometry, vol. 302, pp. 125-131, 2010.PDF icon Technical Report (3.22 MB)
A. Kreshuk, Straehle, C. N., Sommer, C., Köthe, U., Cantoni, M., Knott, G. W., and Hamprecht, F. A., “Automated Detection and Segmentation of Synaptic Contacts in Nearly Isotropic Serial Electron Microscopy Images”, PLoS ONE, vol. 6 (10), 2011.PDF icon Technical Report (290.48 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.PDF icon Technical Report (16.66 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.PDF icon Technical Report (348.05 KB)
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.PDF icon Technical Report (2.82 MB)
M. Arnold, Bell, P., and Ommer, B., “Automated Learning of Self-Similarity and Informative Structures in Architecture”, in Scientific Computing & Cultural Heritage, 2013.
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.PDF icon Technical Report (1.73 MB)
C. Scheelen, “Automated Quality Control in the Life Sciences”, University of Heidelberg, 2010.
N. Krasowski, “Automated Segmentation for Connectomics Utilizing Higher-Order Biological Priors”. University of Heidelberg, 2016.
B. Andres, “Automated Segmentation of Large 3D Images of Nervous Systems Using a Higher-order Graphical Model”. University of Heidelberg, 2011.
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.
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.
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.
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.
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.
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.
C. Pape, “Automatic Segmentation of Neurites from Anisotropic EM-Imaging”, University of Heidelberg, 2016.
T. Prange, “Automatic Segmentation of Neurons in Electron Microscopy Data with Membrane Defects”, University of Heidelberg, 2016.
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.
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.PDF icon Technical Report (221.54 KB)
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.
S. Petra and Schnörr, C., “Average Case Recovery Analysis of Tomographic Compressive Sensing”, Linear Algebra and its Applications, vol. 441, pp. 168-198, 2014.PDF icon Technical Report (1.85 MB)
B
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.
S. T. Radev, Mertens, U. K., Voss, A., Ardizzone, L., and Köthe, U., “BayesFlow: Learning complex stochastic models with invertible neural networks”, 2020.PDF icon PDF (5.36 MB)
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.
M. Hissmann, “Bayesian Estimation for White Light Interferometry”. University of Heidelberg, 2005.
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.PDF icon Technical Report (232.69 KB)
M. Haußmann, Gerwinn, S., and Kandemir, M., “Bayesian Evidential Deep Learning with PAC Regularization ”, 3rd Symposium on Advances in Approximate Bayesian Inference . 2020.
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, “Bayesian Neural Networks for Probabilistic Machine Learning”. Heidelberg University, 2021.
M. Haußmann, Gerwinn, S., and Kandemir, M., “Bayesian Prior Networks with PAC Training”, arXiv preprint arXiv:1906.00816, 2019.
M. Hissmann and Hamprecht, F. A., “Bayesian surface estimation for white light interferometry”, Optical Engineering, vol. 44, pp. 1-9, 2005.PDF icon Technical Report (549.46 KB)

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