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Renard, B Y, Kirchner, M, Monigatti, F, Ivanov, A R, Rappsilber, J, Winter, D, Steen, J A J, Hamprecht, F A and Steen, H (2009). When Less Can Yield More - Computational Preprocessing of MS/MS Spectra for Peptide Identification Preprocessing. Proteomics. 9 4978-4984PDF icon Technical Report (901.78 KB)
Renard, B Y, Kirchner, M, Steen, H, Steen, J A J and Hamprecht, F A (2008). NITPICK: Peak Identification for Mass Spectrometry Data. BMC Bioinformatics. 9 355PDF icon Technical Report (643.89 KB)
Renard, B Y, Timm, W, Kirchner, M, Steen, J A J, Hamprecht, F A and Steen, H (2010). Estimating the Confidence of Peptide Identifications without Decoy Databases. Analytical Chemistry. 4314-4318PDF icon Technical Report (619.11 KB)
Remme, R (2019). Instance Segmentation Via Associative Pixel Embeddings. Heidelberg University
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
Reinmuth, J (2000). Zwei-Farbstoff-Technik Zur Tiefenrekonstruktion Von Gaskonzentrationen In Der Wasserseitigen Grenzschicht. Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg
Reinelt, S (1994). Bestimmung Der Transfergeschwindigkeit Mittels Cft Mit Wärme Als Tracer. Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg
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
Ravindran, A (2019). Novel Deep Learning-Based Instance Segmentation Using Mutex Watershed For Microscopy Cell Images. Heidelberg University
Rathore, D (2016). Semantic Segmentation Using Deep Learning. University of Heidelberg
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
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 (2011). Order Preserving and Shape Prior Constrained Intra-Retinal Layer Segmentation in Optical Coherence Tomography. MICCAI 2011, Proceedings. 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 (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)
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

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