HCI Logo
Heidelberg University Logo
IWR Logo

Publications

Export 1965 results:
Author [ Title(Desc)] Type Year
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z 
A
Decker, C (2014). Automated Animal Behavior Classification. University of Heidelberg
Tek, B F, Kröger, T, Mikula, S and Hamprecht, F A (2014). Automated Cell Nucleus Detection for Large-Volume Electron Microscopy of Neural Tissue. ISBI. Proceedings. 69-72PDF icon Technical Report (533.92 KB)
Eisele, H (2002). Automated defect detection and evaluation in X-ray CT images. University of Heidelberg. www.ub.uni-heidelberg.de/archiv/3106
Kreshuk, A, Stankiewicz, M, Lou, X, Kirchner, M, Hamprecht, F A and Mayer, M P (2010). Automated detection and analysis of bimodal isotope peak distribution in H/D exchange mass spectrometry using HeXicon. International Journal of Mass Spectrometry. 302 125-131PDF icon Technical Report (3.22 MB)
Kreshuk, A, Straehle, C N, Sommer, C, Köthe, U, Cantoni, M, Knott, G W and Hamprecht, F A (2011). Automated Detection and Segmentation of Synaptic Contacts in Nearly Isotropic Serial Electron Microscopy Images. PLoS ONE. 6 (10)PDF icon Technical Report (290.48 KB)
Kreshuk, A, Köthe, U, Pax, E, Bock, D D and Hamprecht, F A (2014). Automated Detection of Synapses in Serial Section Transmission Electron Microscopy Image Stacks. PLoS ONE. 9 2PDF icon Technical Report (16.66 MB)
Kelm, B Michael, Menze, B H, Zechmann, C M, Baudendistel, K T and Hamprecht, F A (2007). Automated Estimation of Tumor Probability in Prostate MRSI: Pattern Recognition vs. Quantification. Magnetic Resonance in Medicine. 57 150-159PDF icon Technical Report (348.05 KB)
Diego, F, Reichinnek, S, Both, M and Hamprecht, F A (2013). Automated Identification of Neuronal Activity from Calcium Imaging by Sparse Dictionary Learning. ISBI 2013. Proceedings. 1058-1061PDF icon Technical Report (2.82 MB)
Arnold, M, Bell, P and Ommer, B (2013). Automated Learning of Self-Similarity and Informative Structures in Architecture. Scientific Computing & Cultural Heritage
Mikut, R, 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 (2013). Automated Processing of Zebrafish Imaging Data: A Survey. Zebrafish. 10 (3)PDF icon Technical Report (1.73 MB)
Scheelen, C (2010). Automated Quality Control In The Life Sciences. University of Heidelberg
Krasowski, N (2016). Automated Segmentation for Connectomics Utilizing Higher-Order Biological Priors. University of Heidelberg
Andres, B (2011). Automated Segmentation of Large 3D Images of Nervous Systems Using a Higher-order Graphical Model. University of Heidelberg
Kreshuk, A, Straehle, C N, Sommer, C, Köthe, U, Knott, G W and Hamprecht, F A (2011). Automated Segmentation of Synapses in 3D EM Data. Eighth IEEE International Symposium on Biomedical Imaging (ISBI 2011). Proceedings. 220-223
Kreshuk, A, Walecki, R, Köthe, U, Gierthmühlen, M, Plachta, D, Genoud, C, Haastert-Talini, K and Hamprecht, F A (2015). Automated Tracing of Myelinated Axons and Detection of the Nodes of Ranvier in Serial Images of Peripheral Nerves. Journal of Microscopy. 259 (2) 143-154
Zechmann, C M, Menze, B H, Kelm, B Michael, Zamecnik, P, Ikinger, U, Waldherr, R, Delorme, S, Hamprecht, F A and Bachert, P (2012). Automated vs. manual pattern recognition of 3D 1H MRSI data of patients with prostate cancer. Academic Radiology. 19, 6 675-684
Keuchel, J, Naumann, S, Heiler, M and Siegmund, A (2002). Automatic Land Cover Analysis for Tenerife by Supervised Classification using Remotely Sensed Data. Remote Sensing of Environment
Wierzimok, D and Jähne, B (1990). Automatic particle tracking beneath a wind-stressed wavy water surface with image processing. Proc.\ 5th Int. Symposium Flow Visualization, Praque 1989. 943--956
Wierzimok, D and Jähne, B (1989). Automatic particle tracking velocimetry beneath a wind-stressed wavy water surface with image processing. 5th International Symposium on Flow Visualization
Pape, C (2016). Automatic Segmentation Of Neurites From Anisotropic Em-Imaging. University of Heidelberg
Prange, T (2016). Automatic Segmentation Of Neurons In Electron Microscopy Data With Membrane Defects. University of Heidelberg
Bister, D, Rohr, K and Schnörr, C (1990). Automatische Bestimmung der Trajektorien von sich bewegenden Objekten aus einer Grauwertbildfolge. Mustererkennung 1990, 12. DAGM-Symposium. Springer-Verlag, Oberkochen-Aalen. 254 44–51
Kelm, B Michael, Menze, B H and Hamprecht, F A (2005). Automatische Lokalisation von Tumoren in 1H-NMR-spektroskopischen in vivo Aufnahmen. VDI-Berichte. 1883 457-466PDF icon Technical Report (221.54 KB)
Jäger, M, Knoll, C and Hamprecht, F A (2005). Automatisierte Klassifikation von Laserschwei\DFprozessen durch Nutzung von 3D Signalverarbeitungs-Algorithmen. Robert Bosch GmbH, Schwieberdingen and IWR, Uni Heidelberg
Petra, S and Schnörr, C (2014). Average Case Recovery Analysis of Tomographic Compressive Sensing. Linear Algebra and its Applications. 441 168-198PDF icon Technical Report (1.85 MB)
B
Fehr, J and Burkhardt, H (2009). A Bag of Features Approach for 3D Shape Retrieval. Proceedings of the ISVC 2009, Part I. Springer. 5875 34-43
Radev, S T, Mertens, U K, Voss, A, Ardizzone, L and Köthe, U (2020). BayesFlow: Learning complex stochastic models with invertible neural networks. http://arxiv.org/abs/2003.06281PDF icon PDF (5.36 MB)
Gehler, P Vincent, Rother, C, Blake, A, Minka, T and Sharp, T (2008). Bayesian color constancy revisited. 26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR
Hissmann, M (2005). Bayesian Estimation for White Light Interferometry. University of Heidelberg. http://www.ub.uni-heidelberg.de/archiv/5742
Kelm, B Michael, Müller, N, Menze, B H and Hamprecht, F A (2006). Bayesian Estimation of Smooth Parameter Maps for Dynamic Contrast-Enhanced MR Images with Block-ICM. Proc Computer Vision and Pattern Recognition Workshop (Mathematical Methods in Biomedical Image Analysis). IEEE Computer Society. 96-103PDF icon Technical Report (232.69 KB)
Haußmann, M, Gerwinn, S and Kandemir, M (2020). Bayesian Evidential Deep Learning with PAC Regularization . 3rd Symposium on Advances in Approximate Bayesian Inference
Giebel, J, Gavrila, D M and Schnörr, C (2004). A Bayesian Framework for Multi-cue 3D Object Tracking. Computer Vision – ECCV 2004. Springer. 3024 241-252
Haußmann, (2021). Bayesian Neural Networks for Probabilistic Machine Learning. Heidelberg University
Haußmann, M, Gerwinn, S and Kandemir, M (2019). Bayesian Prior Networks with PAC Training. arXiv preprint arXiv:1906.00816
Hissmann, M and Hamprecht, F A (2005). Bayesian surface estimation for white light interferometry. Optical Engineering. 44 1-9PDF icon Technical Report (549.46 KB)

Pages