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F. Raisch, Scharr, H., Kirchgeßner, N., Jähne, B., Fink, R. H. A., and Uttenweiler, D., “Velocity and feature estimation of actin filaments using active contours in noisy fluorescence image sequences”, in Proc. 2nd IASTED Int. Conf. Visualization, Imaging and Image Processing, 2002, p. 645--650.
N. Rahaman, Arpit, D., Baratin, A., Draxler, F., Lin, M., Hamprecht, F. A., Bengio, Y., and Courville, A., “On the spectral bias of deep neural networks”, arXiv preprint arXiv:1806.08734, 2018.
S. T. Radev, Mertens, U. K., Voss, A., Ardizzone, L., and Köthe, U., “BayesFlow: Learning complex stochastic models with invertible neural networks”, 2020.PDF icon PDF (5.36 MB)
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C. Proß, “Analysis of the Fetch Dependency of the Slope of Wind-Water Waves”, Institut für Umweltphysik, Universität Heidelberg, Germany, 2016.
M. Prokop, “Bestimmung Physiologischer Parameter von Pflanzen mittels Digitaler Bildverarbeitung”, IWR, Fakultät für Physik und Astronomie, Univ.\ Heidelberg, 2000.
T. Preusser, Droske, M., Garbe, C. S., Rumpf, M., and Telea, A., “A phase field method for joint denoising, edge detection, and motion estimation in image sequence processing.”, SIAM Journal of Applied Mathematics, vol. 68, pp. 599-618, 2007.
T. Prange, “Automatic Segmentation of Neurons in Electron Microscopy Data with Membrane Defects”, University of Heidelberg, 2016.
C. Popp, “Untersuchung von Austauschprozessen an der Wasseroberfläche aus Infrarot-Bildsequenzen mittels frequenzmodulierter Wärmeeinstrahlung”. Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg, 2006.
P. Pletscher, Nowozin, S., Kohli, P., and Rother, C., “Putting MAP back on the map”, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2011, vol. 6835 LNCS, pp. 111–121.
P. Pletscher, Nowozin, S., Kohli, P., and Rother, C., “Putting MAP back on the map”, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2011, vol. 6835 LNCS, pp. 111–121.
T. Platt, “Weiterentwicklung einer hochauflösenden LIF-Methode zur Messung von Sauerstoffkonzentrationsprofilen in der wasserseitigen Grenzschicht”, Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg, 2011.
P. Pinggera, Ramos, S., Gehrig, S., Franke, U., Rother, C., and Mester, R., “Lost and found: Detecting small road hazards for self-driving vehicles”, in IEEE International Conference on Intelligent Robots and Systems, 2016, vol. 2016-Novem, pp. 1099–1106.
M. Pfannmöller, Flügge, H., Benner, G., Wacker, I., Sommer, C., Hanselmann, M., Schmale, S., Schmidt, H., Hamprecht, F. A., Rabe, T., Kowalsky, W., and Schröder, R., “Visualizing a homogeneous blend in bulk heterojunction polymer solar cells by analytical electron microscopy”, Nano Letters, vol. 11, pp. 3099-3107, 2011.
S. Petra, Schnörr, C., Becker, F., and Lenzen, F., “B-SMART: Bregman-Based First-Order Algorithms for Non-Negative Compressed Sensing Problems”, in Proceedings of the 4th International Conference on Scale Space and Variational Methods in Computer Vision SSVM, 2013, pp. 110-124.
S. Petra, Popa, C., and Schnörr, C., “Extended and Constrained Cimmino-type Algorithms with Applications in Tomographic Image Reconstruction”, IWR, University of Heidelberg, 2008.
S. Petra, Popa, C., and Schnörr, C., “Enhancing Sparsity by Constraining Strategies: Constrained SIRT versus Spectral Projected Gradient Methods”, in Proc. 7th Workshop on Modelling of Environmental and Life Sciences Problems (WMM 08), Constanta, Romania, 2008.
S. Petra, Popa, C., and Schnörr, C., “Enhancing Sparsity by Constraining Strategies: Constrained SIRT versus Spectral Projected Gradient Methods”, in Proc. 7th Workshop on Modelling of Environmental and Life Sciences Problems (WMM 08), Bucharest, Romania, 2008.
S. Petra and Schnörr, C., “TomoPIV meets Compressed Sensing”, IWR, University of Heidelberg, 2009.
S. Petra, Schnörr, C., Schröder, A., and Wieneke, B., “Tomographic Image Reconstruction in Experimental Fluid Dynamics: Synopsis and Problems”, in Proc. 6th Workshop on Modelling of Environmental and Life Sciences Problems (WMM 07), Constanta, Romania, 2007.
S. Petra and Schnörr, C., “TomoPIV meets Compressed Sensing”, Pure Math. Appl., vol. 20, pp. 49 – 76, 2009.
S. Petra, Schnörr, C., and Schröder, A., “Critical Parameter Values and Reconstruction Properties of Discrete Tomography: Application to Experimental Fluid Dynamics”. 2012.
S. Petra, Schröder, A., Wieneke, B., and Schnörr, C., “On Sparsity Maximization in Tomographic Particle Image Reconstruction”, in Pattern Recognition – 30th DAGM Symposium, 2008, vol. 5096, pp. 294–303.
S. Petra, Popa, C., and Schnörr, C., “Accelerating Constrained SIRT with Applications in Tomographic Particle Image Reconstruction”, IWR, University of Heidelberg, 2009.PDF icon Technical Report (3.33 MB)
S. Petra, Popa, C., and Schnörr, C., “Extended and Constrained Cimmino-type Algorithms with Applications in Tomographic Image Reconstruction”, IWR, University of Heidelberg, 2008.PDF icon Technical Report (2.13 MB)
S. Petra and Schnörr, C., “TomoPIV meets Compressed Sensing”, IWR, University of Heidelberg, 2009.PDF icon Technical Report (646.75 KB)
S. Petra, Schnörr, C., Becker, F., and Lenzen, F., “B-SMART: Bregman-Based First-Order Algorithms for Non-Negative Compressed Sensing Problems”, in Proceedings of the 4th International Conference on Scale Space and Variational Methods in Computer Vision (SSVM) 2013, 2013, vol. 7893, pp. 110-124.PDF icon Technical Report (1.15 MB)
S. Petra and Schnörr, C., “Average Case Recovery Analysis of Tomographic Compressive Sensing”, Linear Algebra and its Applications, vol. 441, pp. 168-198, 2014.PDF icon Technical Report (1.85 MB)
S. Petra and Schnörr, C., “TomoPIV meets Compressed Sensing”, Pure Math.~Appl., vol. 20, p. 49 -- 76, 2009.PDF icon Technical Report (409.1 KB)
S. Petra, Schnörr, C., and Schröder, A., “Critical Parameter Values and Reconstruction Propertiesof Discrete Tomography: Application to Experimental FluidDynamics”, Fundamenta Informaticae, vol. 125, p. 285--312, 2013.PDF icon Technical Report (1.42 MB)
S. Petra, Schröder, A., Wieneke, B., and Schnörr, C., “On Sparsity Maximization in Tomographic Particle Image Reconstruction”, in Pattern Recognition -- 30th DAGM Symposium, 2008, vol. 5096, p. 294--303.PDF icon Technical Report (1014.71 KB)
S. Petra, Schröder, A., and Schnörr, C., “3D Tomography from Few Projections in Experimental Fluid Mechanics”, Imaging Measurement Methods for Flow Analysis, vol. 106. Springer, pp. 63-72, 2009.PDF icon Technical Report (411.51 KB)
S. Peter, Diego, F., Hamprecht, F. A., and Nadler, B., “Cost-efficient Gradient Boosting”, NIPS, poster. 2017.
S. Peter, Kirschbaum, E., Both, M., Campbell, L. A., Harvey, B. K., Heins, C., Durstewitz, D., Diego, F., and Hamprecht, F. A., “Sparse convolutional coding for neuronal assembly detection”, NIPS, poster. 2017.
S. Peter, “Spatio-Temporal Motif Deconvolution for Calcium Image Analysis”, University of Heidelberg, 2015.
S. Peter, “Machine learning under test-time budget constraints”. Heidelberg University, 2019.

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