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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)
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
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
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
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
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)
Rath, R (1992). Amplitudenmessung Von Wasseroberflächenwellen Mittels Digitaler Bildanalyse. Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg
Rathke, F, Schmidt, S and Schnörr, C (2011). Order Preserving and Shape Prior Constrained Intra-Retinal Layer Segmentation in Optical Coherence Tomography. MICCAI 2011, Proceedings. Springer. 6893 370-377
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
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. 18 781-794PDF icon Technical Report (4.07 MB)
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--377PDF icon Technical Report (1.12 MB)
Rathke, F and Schnörr, C (2015). A Computational Approach to Log-Concave Density Estimation. An. St. Univ. Ovidius Constanta. 23 151-166PDF icon Technical Report (1.07 MB)
Rathke, F, Hansen, K, Brefeld, U and Müller, K - R (2010). StructRank: A new approach for ligand-based virtual screening. J. Chem. Inf. Model. 51 83–92
Rathke, F, Schmidt, S and Schnörr, C (2011). Order preserving and shape prior constrained intra-retinal layer segmentation in optical coherence tomography. Medical Image Computing and Computer-Assisted Intervention (MICCAI 2011). Springer. 6893 370–377
Rathke, F, Schmidt, S and Schnörr, C (2014). Probabilistic Intra-Retinal Layer Segmentation in 3-D OCT Images Using Global Shape Regularization. Med. Image Anal. 18 781–794
Rathke, F and Schnörr, C (2015). A Computational Approach to Log-Concave Density Estimation. An. St. Univ. Ovidius Constanta. 23 151-166
Rathke, F (2015). Probabilistic Graphical Models for Medical Image Segmentation. University Heidelberg
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
Rathore, D (2016). Semantic Segmentation Using Deep Learning. University of Heidelberg
Ravindran, A (2019). Novel Deep Learning-Based Instance Segmentation Using Mutex Watershed For Microscopy Cell Images. Heidelberg University
Reinecke, H (1997). Methoden Zur Bearbeitung Und Merkmalsextrahierung Von Zeitreihen Aus Technischen Anlagen. IWR, Fakultät für Physik und Astronomie, Univ.\ Heidelberg
Reinecke, H, Fantana, N L, Haußecker, H and Jähne, B (1997). Rekonstruktion von Schreiberkurven. Mustererkennung 1997. Springer. 527--536
Reinelt, S (1994). Bestimmung Der Transfergeschwindigkeit Mittels Cft Mit Wärme Als Tracer. Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg
Reinmuth, J (2000). Zwei-Farbstoff-Technik Zur Tiefenrekonstruktion Von Gaskonzentrationen In Der Wasserseitigen Grenzschicht. Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg
Reith, S (2014). Spatio-Temporal Slope Measurement Of Short Wind Waves Under The Influence Of Surface Films At The Heidelberg Aeolotron. Institut für Umweltphysik, Universität Heidelberg, Germany. http://www.ub.uni-heidelberg.de/archiv/17697
Remme, R (2019). Instance Segmentation Via Associative Pixel Embeddings. Heidelberg University

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