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Rathke, F, Desana, M and Schnörr, C (2017). Locally Adaptive Probabilistic Models for Global Segmentation of Pathological OCT Scans. MICCAI. Proceedings. 177-184PDF icon Technical Report (4.79 MB)
Rathke, F and Schnörr, C (2018). Fast Multivariate Log-Concave Density Estimation. preprint: ArXiv. https://arxiv.org/pdf/1805.07272.pdfPDF icon Technical Report (3.54 MB)
Rathke, F and Schnörr, C (2019). Fast Multivariate Log-Concave Density Estimation. Comp. Statistics & Data Analysis. 140 41-58
Rathke, F and Schnörr, C (2019). Fast Multivariate Log-Concave Density Estimation. Comp. Statistics & Data Analysis. 140 41–58
Rathke, F and Schnörr, C (2018). Fast Multivariate Log-Concave Density Estimation. preprint: arXiv. https://arxiv.org/pdf/1805.07272.pdf
Rathke, F, Desana, M and Schnörr, C (2017). Locally Adaptive Probabilistic Models for Global Segmentation of Pathological OCT Scans. Proc. MICCAI
Rathke, F, Schmidt, S and Schnörr, C (2014). Probabilistic Intra-Retinal Layer Segmentation in 3-D OCT Images Using Global Shape Regularization. Medical Image Analysis. 18 781-794
Rathke, F, Schmidt, S and Schnörr, C (2011). Order Preserving and Shape Prior Constrained Intra-Retinal Layer Segmentation in Optical Coherence Tomography. MICCAI. Springer. 6893 370–377
Rath, R (1992). Amplitudenmessung Von Wasseroberflächenwellen Mittels Digitaler Bildanalyse. Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg
Rapp, H, Frank, M, Hamprecht, F A and Jähne, B (2008). A Theoretical and Experimental Investigation of the Systematic Errors and Statistical Uncertainties of Time-of-Flight Cameras. Int. J. Intelligent Systems Technologies and Applications. 5 402-413PDF icon Technical Report (798.23 KB)
Rapp, H (2007). Experimental And Theoretical Investigation Of Correlating Tof-Camera Systems. IWR, Fakultät für Physik und Astronomie, Universität Heidelberg. http://www.ub.uni-heidelberg.de/archiv/7666
Rapp, H, Frank, M, Hamprecht, F A and Jähne, B (2008). A theoretical and experimental investigation of the systematic errors and statistical uncertainties of time-of-flight cameras. Int. J. Intelligent Systems Technologies and Applications. 5 402--413
Rapp, H, Frank, M, Hamprecht, F A and Jähne, B (2007). A theoretical and experimental investigation of the systematic errors and statistical uncertainties of time-of-flight cameras. Proc.\ Dyn3D Workshop, Heidelberg, Sept. 11, 2007. ZESS, Univ.\ Siegen
Ramos, S, Gehrig, S, Pinggera, P, Franke, U and Rother, C (2017). Detecting unexpected obstacles for self-driving cars: Fusing deep learning and geometric modeling. IEEE Intelligent Vehicles Symposium, Proceedings. 1025–1032. http://arxiv.org/abs/1612.06573
Raisch, F (2004). Aktive Konturen zur Objektsegmentierung in stark verrauschten Bildsequenzen und zur Segmentierung von Bonddrähten in der industriellen Bildverarbeitung. Univ.\ Mannheim. http://d-nb.info/972877436
Raisch, F, Scharr, H, Kirchgeßner, N, Jähne, B, Fink, R H A and Uttenweiler, D (2002). Velocity and feature estimation of actin filaments using active contours in noisy fluorescence image sequences. Proc. 2nd IASTED Int. Conf. Visualization, Imaging and Image Processing. 645--650
Rahaman, N, Arpit, D, Baratin, A, Draxler, F, Lin, M, Hamprecht, F A, Bengio, Y and Courville, A (2018). On the spectral bias of deep neural networks. arXiv preprint arXiv:1806.08734
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)

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