Publications

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2009
S. Petra and Schnörr, C., TomoPIV meets Compressed Sensing, Pure Math. Appl., vol. 20, pp. 49 – 76, 2009.
2010
M. Kirchner, Renard, B. Y., Köthe, U., Pappin, D. J., Hamprecht, F. A., Steen, J. A. J., and Steen, H., Computational Protein Profile Similarity Screening for Quantitative Mass Spectrometry Experiments, Bioinformatics, vol. 26 (1), pp. 77-83, 2010.PDF icon Technical Report (380.19 KB)
J. Lellmann, Breitenreicher, D., and Schnörr, C., Fast and Exact Primal-Dual Iterations for Variational Problems in Computer Vision, in European Conference on Computer Vision (ECCV), 2010, vol. 6312, p. 494--505.PDF icon Technical Report (1.94 MB)
J. Lellmann, Breitenreicher, D., and Schnörr, C., Fast and Exact Primal-Dual Iterations for Variational Problems in Computer Vision, in European Conference on Computer Vision (ECCV), 2010, vol. 6312, pp. 494–505.
K. Börner, Hermle, J., Sommer, C., Brown, N. P., Knapp, B., Glass, B., Torralba, G., Reymann, J., Beil, N., Beneke, J., Pepperkok, R., Schneider, R., and Ludwig, T., From experimental setup to bioinformatics: An RNAi screening platform to identify host factors involved in HIV-1 replication, Biotechnology Journal, vol. 5, pp. 39-49, 2010.PDF icon Technical Report (556.09 KB)
J. H. Kappes, Schmidt, S., and Schnörr, C., MRF Inference by k-Fan Decomposition and Tight Lagrangian Relaxation, in European Conference on Computer Vision (ECCV), 2010, vol. 6313, p. 735--747.
J. H. Kappes, Schmidt, S., and Schnörr, C., MRF Inference by k-Fan Decomposition and Tight Lagrangian Relaxation, in European Conference on Computer Vision (ECCV), 2010, vol. 6313, p. 735--747.PDF icon Technical Report (1.49 MB)
J. H. Kappes, Schmidt, S., and Schnörr, C., MRF Inference by k-Fan Decomposition and Tight Lagrangian Relaxation, in European Conference on Computer Vision (ECCV), 2010, vol. 6313, pp. 735–747.
2011
S. Wanner, Sommer, C., Rocholz, R., Hamprecht, F. A., and Jähne, B., A Framework for Interactive Visualization and Classification of Dynamical Processes at the Water Surface, in 16th International Workshop on Vision, Modelling and Visualization, 2011, pp. 199-206.PDF icon Technical Report (4.67 MB)
S. Wanner, Sommer, C., Rocholz, R., Jung, M., Hamprecht, F. A., and Jähne, B., A framework for interactive visualization and classification of dynamical processes at the water surface, in 16th International Workshop on Vision, Modelling and Visualization, 2011, p. 199--206.
A. Nicola, Petra, S., Popa, C., and Schnörr, C., A general extending and constraining procedure for linear iterative methods, Int.~J.~Comp.~Math., 2011.PDF icon Technical Report (633.79 KB)
A. Nicola, Petra, S., Popa, C., and Schnörr, C., A general extending and constraining procedure for linear iterative methods, Int.~J.~Comp.~Math., 2011.PDF icon Technical Report (633.79 KB)
A. Nicola, Petra, S., Popa, C., and Schnörr, C., A general extending and constraining procedure for linear iterative methods, Int. J. Comp. Math., 2011.
A. Nicola, Petra, S., Popa, C., and Schnörr, C., A general extending and constraining procedure for linear iterative methods, Int. J. Comp. Math., 2011.
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, 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.
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.
X. Lou and Hamprecht, F. A., Structured Learning for Cell Tracking, in NIPS 2011. Proceedings, 2011, pp. 1296-1304.PDF icon Technical Report (1.41 MB)
F. Becker, Wieneke, B., Petra, S., Schröder, A., and Schnörr, C., Variational Adaptive Correlation Method for Flow Estimation, IEEE Transactions on Image Processing, vol. 21, 6, pp. 3053 - 3065, 2011.
F. Lenzen, Becker, F., Lellmann, J., Petra, S., and Schnörr, C., Variational Image Denoising with Adaptive Constraint Sets, in Proceedings of the 3nd International Conference on Scale Space and Variational Methods in Computer Vision 2011, in press, 2011, vol. 6667, pp. 206-217.
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.
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.
2012
S. Petra, Schnörr, C., and Schröder, A., Critical Parameter Values and Reconstruction Properties of Discrete Tomography: Application to Experimental Fluid Dynamics. 2012.
D. Kondermann, Abraham, S., Förstner, W., Gehrig, S., Imiya, A., Jähne, B., Klose, F., Magor, M., Mayer, H., Mester, R., Pajdla, T., Reulke, R., and Zimmer, H., On Performance Analysis of Optical Flow Algorithms, in Outdoor and Large-Scale Real-Worls Scene Analysis, Dagstuhl-Workshop 2011, 2012, vol. LNCS, pp. 329-355.
F. Becker, Wieneke, B., Petra, S., Schröder, A., and Schnörr, C., Variational Adaptive Correlation Method for Flow Estimation, IEEE Transactions on Image Processing, vol. 21, p. 3053 -- 3065, 2012.PDF icon Technical Report (18.81 MB)
F. Becker, Wieneke, B., Petra, S., Schröder, A., and Schnörr, C., Variational Adaptive Correlation Method for Flow Estimation, IEEE Transactions on Image Processing, vol. 21, pp. 3053 – 3065, 2012.
F. Lenzen, Becker, F., Lellmann, J., Petra, S., and Schnörr, C., Variational Image Denoising with Adaptive Constraint Sets, in LNCS, 2012, pp. 206-217.PDF icon Technical Report (649.03 KB)
F. Lenzen, Becker, F., Lellmann, J., Petra, S., and Schnörr, C., Variational Image Denoising with Adaptive Constraint Sets, in Proceedings of the 3rd International Conference on Scale Space and Variational Methods in Computer Vision 2011, 2012, pp. 206-217.
2013
R. Mikut, Dickmeis, T., Driever, W., Geurts, P., Hamprecht, F. A., Kausler, B. X., Ledesma-Carbayo, M., Marée, R., Mikula, K., Pantazis, P., Ronneberger, O., Santos, A., and Stotzka, R., Automated Processing of Zebrafish Imaging Data: A Survey, Zebrafish, vol. 10 (3), 2013.PDF icon Technical Report (1.73 MB)
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, 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)
F. Lenzen, Becker, F., Lellmann, J., Petra, S., and Schnörr, C., A Class of Quasi-Variational Inequalities for Adaptive Image Denoising and Decomposition, Computational Optimization and Applications (COAP), vol. 54 (2), pp. 371-398, 2013.
F. Lenzen, Becker, F., Lellmann, J., Petra, S., and Schnörr, C., A class of quasi-variational inequalities for adaptive image denoising and decomposition, Computational Optimization and Applications, vol. 54, pp. 371-398, 2013.PDF icon Technical Report (748.66 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)

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