HCI Logo
Heidelberg University Logo
IWR Logo

Publications

Export 1963 results:
[ Author(Asc)] Title Type Year
Filters: Filter is   [Clear All Filters]
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 
R
B. Y. Renard, Kirchner, M., Monigatti, F., Ivanov, A. R., Rappsilber, J., Winter, D., Steen, J. A. J., Hamprecht, F. A., and Steen, H., “When Less Can Yield More - Computational Preprocessing of MS/MS Spectra for Peptide Identification Preprocessing”, Proteomics, vol. 9, pp. 4978-4984, 2009.PDF icon Technical Report (901.78 KB)
B. Y. Renard, Kirchner, M., Steen, H., Steen, J. A. J., and Hamprecht, F. A., “NITPICK: Peak Identification for Mass Spectrometry Data”, BMC Bioinformatics, vol. 9, p. 355, 2008.PDF icon Technical Report (643.89 KB)
B. Y. Renard, Timm, W., Kirchner, M., Steen, J. A. J., Hamprecht, F. A., and Steen, H., “Estimating the Confidence of Peptide Identifications without Decoy Databases”, Analytical Chemistry, pp. 4314-4318, 2010.PDF icon Technical Report (619.11 KB)
R. Remme, “Instance Segmentation via Associative Pixel Embeddings”, Heidelberg University, 2019.
S. Reith, “Spatio-temporal slope measurement of short wind waves under the influence of surface films at the Heidelberg Aeolotron”, Institut für Umweltphysik, Universität Heidelberg, Germany, 2014.
J. Reinmuth, “Zwei-Farbstoff-Technik zur Tiefenrekonstruktion von Gaskonzentrationen in der wasserseitigen Grenzschicht”, Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg, 2000.
S. Reinelt, “Bestimmung der Transfergeschwindigkeit mittels CFT mit Wärme als Tracer”, Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg, 1994.
H. Reinecke, “Methoden zur Bearbeitung und Merkmalsextrahierung von Zeitreihen aus technischen Anlagen”, IWR, Fakultät für Physik und Astronomie, Univ.\ Heidelberg, 1997.
H. Reinecke, Fantana, N. L., Haußecker, H., and Jähne, B., “Rekonstruktion von Schreiberkurven”, in Mustererkennung 1997, 1997, p. 527--536.
A. Ravindran, “Novel Deep Learning-based Instance Segmentation Using Mutex Watershed for Microscopy Cell Images”, Heidelberg University, 2019.
D. Rathore, “Semantic Segmentation Using Deep Learning”, University of Heidelberg, 2016.
F. Rathke, Schmidt, S., and Schnörr, C., “Probabilistic Intra-Retinal Layer Segmentation in 3-D OCT Images Using Global Shape Regularization”, Medical Image Analysis, vol. 18, pp. 781-794, 2014.PDF icon Technical Report (4.07 MB)
F. Rathke, Schmidt, S., and Schnörr, C., “Order Preserving and Shape Prior Constrained Intra-Retinal Layer Segmentation in Optical Coherence Tomography”, in MICCAI, 2011, vol. 6893, p. 370--377.PDF icon Technical Report (1.12 MB)
F. Rathke and Schnörr, C., “A Computational Approach to Log-Concave Density Estimation”, An. St. Univ. Ovidius Constanta, vol. 23, pp. 151-166, 2015.PDF icon Technical Report (1.07 MB)
F. Rathke, Hansen, K., Brefeld, U., and Müller, K. - R., “StructRank: A new approach for ligand-based virtual screening”, J. Chem. Inf. Model., vol. 51, pp. 83–92, 2010.
F. Rathke, Schmidt, S., and Schnörr, C., “Order preserving and shape prior constrained intra-retinal layer segmentation in optical coherence tomography”, Medical Image Computing and Computer-Assisted Intervention (MICCAI 2011), vol. 6893. Springer, pp. 370–377, 2011.
F. Rathke, Schmidt, S., and Schnörr, C., “Probabilistic Intra-Retinal Layer Segmentation in 3-D OCT Images Using Global Shape Regularization”, Med. Image Anal., vol. 18, pp. 781–794, 2014.
F. Rathke and Schnörr, C., “A Computational Approach to Log-Concave Density Estimation”, An. St. Univ. Ovidius Constanta, vol. 23, pp. 151-166, 2015.
F. Rathke, “Probabilistic Graphical Models for Medical Image Segmentation”. University Heidelberg, 2015.
F. Rathke, Desana, M., and Schnörr, C., “Locally Adaptive Probabilistic Models for Global Segmentation of Pathological OCT Scans”, MICCAI. Proceedings. pp. 177-184, 2017.PDF icon Technical Report (4.79 MB)
F. Rathke and Schnörr, C., “Fast Multivariate Log-Concave Density Estimation”, preprint: ArXiv, 2018.PDF icon Technical Report (3.54 MB)
F. Rathke and Schnörr, C., “Fast Multivariate Log-Concave Density Estimation”, Comp. Statistics & Data Analysis, vol. 140, pp. 41-58, 2019.
F. Rathke and Schnörr, C., “Fast Multivariate Log-Concave Density Estimation”, Comp. Statistics & Data Analysis, vol. 140, pp. 41–58, 2019.
F. Rathke and Schnörr, C., “Fast Multivariate Log-Concave Density Estimation”, preprint: arXiv, 2018.
F. Rathke, Desana, M., and Schnörr, C., “Locally Adaptive Probabilistic Models for Global Segmentation of Pathological OCT Scans”, in Proc. MICCAI, 2017.
F. Rathke, Schmidt, S., and Schnörr, C., “Probabilistic Intra-Retinal Layer Segmentation in 3-D OCT Images Using Global Shape Regularization”, Medical Image Analysis, vol. 18, pp. 781-794, 2014.
F. Rathke, Schmidt, S., and Schnörr, C., “Order Preserving and Shape Prior Constrained Intra-Retinal Layer Segmentation in Optical Coherence Tomography”, in MICCAI, 2011, vol. 6893, pp. 370–377.
F. Rathke, Schmidt, S., and Schnörr, C., “Order Preserving and Shape Prior Constrained Intra-Retinal Layer Segmentation in Optical Coherence Tomography”, in MICCAI 2011, Proceedings, 2011, vol. 6893, pp. 370-377.
R. Rath, “Amplitudenmessung von Wasseroberflächenwellen mittels digitaler Bildanalyse”, Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg, 1992.
H. Rapp, “Experimental and Theoretical Investigation of Correlating TOF-Camera Systems”, IWR, Fakultät für Physik und Astronomie, Universität Heidelberg, 2007.
H. Rapp, Frank, M., Hamprecht, F. A., and Jähne, B., “A theoretical and experimental investigation of the systematic errors and statistical uncertainties of time-of-flight cameras”, Int. J. Intelligent Systems Technologies and Applications, vol. 5, p. 402--413, 2008.
H. Rapp, Frank, M., Hamprecht, F. A., and Jähne, B., “A theoretical and experimental investigation of the systematic errors and statistical uncertainties of time-of-flight cameras”, in Proc.\ Dyn3D Workshop, Heidelberg, Sept. 11, 2007, 2007.
H. Rapp, Frank, M., Hamprecht, F. A., and Jähne, B., “A Theoretical and Experimental Investigation of the Systematic Errors and Statistical Uncertainties of Time-of-Flight Cameras”, Int. J. Intelligent Systems Technologies and Applications, vol. 5, pp. 402-413, 2008.PDF icon Technical Report (798.23 KB)
S. Ramos, Gehrig, S., Pinggera, P., Franke, U., and Rother, C., “Detecting unexpected obstacles for self-driving cars: Fusing deep learning and geometric modeling”, in IEEE Intelligent Vehicles Symposium, Proceedings, 2017, pp. 1025–1032.
F. Raisch, “Aktive Konturen zur Objektsegmentierung in stark verrauschten Bildsequenzen und zur Segmentierung von Bonddrähten in der industriellen Bildverarbeitung”. Univ.\ Mannheim, 2004.

Pages