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Journal Article

E. - M. Didden, Thorarinsdottir, T. L., Lenkoski, A., and Schnörr, C., “Shape from Texture using Locally Scaled Point Processes”, Image Anal. Stereol., vol. 34, pp. 161-170, 2015.

D. Cremers, Kohlberger, T., and Schnörr, C., “Shape Statistics in Kernel Space for Variational Image Segmentation”, Pattern Recognition, vol. 36, pp. 1929–1943, 2003.

D. Cremers, Kohlberger, T., and Schnörr, C., “Shape Statistics in Kernel Space for Variational Image Segmentation”, Pattern Recognition, vol. 36, p. 1929--1943, 2003.

Technical Report (1.67 MB)

T. Milbich, Roth, K., Brattoli, B., and Ommer, B., “Sharing Matters for Generalization in Deep Metric Learning”, IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2020.

C. Schnörr, “Signal and Image Approximation with Level-Set Constraints”, Computing, vol. 81, pp. 137-160, 2007.

C. Schnörr, “Signal and Image Approximation with Level-Set Constraints”, Computing, vol. 81, pp. 137-160, 2007.

Technical Report (506.8 KB)

M. Hanselmann, Voss, B., Renard, B. Y., Lindner, M., Köthe, U., Kirchner, M., and Hamprecht, F. A., “SIMA: Simultaneous Multiple Alignment of LC/MS Peak Lists”, Bioinformatics, vol. 27 (7), pp. 987-993, 2011.

Technical Report (2.2 MB)

B. Jähne, “SIMD-Bildverarbeitungsalgorithmen mit dem Multimedia Extension-Instruktionssatz (MMX) von Intel”, Automatisierungstechnik, vol. 10, p. 453--460, 1997.

U. Köthe, Herrmannsdörfer, F., Kats, I., and Hamprecht, F. A., “SimpleSTORM: a fast, self-calibrating reconstruction algorithm for localization microscopy”, Histochemistry and Cell Biology, vol. 141, pp. 613-627, 2014.

Technical Report (2.29 MB)

J. Yuan, Schnörr, C., and Steidl, G., “Simultaneous Optical Flow Estimation and Decomposition”, SIAM J.~Scientific Computing, vol. 29, pp. 2283-2304, 2007.

Technical Report (1.16 MB)

J. Yuan, Schnörr, C., and Steidl, G., “Simultaneous Optical Flow Estimation and Decomposition”, SIAM J. Scientific Computing, vol. 29, pp. 2283-2304, 2007.

M. Storath, Kiefer, L., and Weinmann, A., “Smoothing for signals with discontinuities using higher order Mumford-Shah models”, Numerische Mathematik, vol. 143(2), pp. 423-460, 2019.

Technical Report (1.09 MB)

F. Lenzen, Lellmann, J., Becker, F., and Schnörr, C., “Solving Quasi-Variational Inequalities for Image Restoration with Adaptive Constraint Sets”, SIAM J. Imag. Sci., vol. 7, pp. 2139–2174, 2014.

F. Lenzen, Lellmann, J., Becker, F., and Schnörr, C., “Solving Quasi-Variational Inequalities for Image Restoration with Adaptive Constraint Sets”, SIAM J.~Imag.~Sci., vol. 7, p. 2139--2174, 2014.

Technical Report (802.13 KB)

F. Lenzen, Lellmann, J., Becker, F., and Schnörr, C., “Solving QVIs for Image Restoration with Adaptive Constraint Sets”, SIAM Journal on Imaging Sciences (SIIMS), in press, 2014.

C. Rother, “Sparse Higher Order Functions of Discrete Variables–-Representation and Optimization”, research.microsoft.com, vol. 45, 2011.

D. Breitenreicher, Lellmann, J., and Schnörr, C., “Sparse Template-Based Variational Image Segmentation”, Advances in Adaptive Data Analysis, vol. 3, pp. 149-166, 2011.

D. Breitenreicher, Lellmann, J., and Schnörr, C., “Sparse Template-Based Variational Image Segmentation”, Advances in Adaptive Data Analysis, vol. 3, pp. 149-166, 2011.

Technical Report (866.28 KB)

T. Wagner, Leue, C., Wenig, M., Pfeilsticker, K., and Platt, U., “Spatial and temporal distribution of enhanced boundary layer BrO concentrations measured by the GOME instrument aboard ERS-2”, J. Geophys. Res., vol. 106, p. 24225--24235, 2001.

D. Uttenweiler, Weber, C., Jähne, B., Fink, R. H. A., and Schaar, H., “Spatiotemporal anisotropic diffusion filtering to improve signal-to-noise ratios and object restoration in fluorescence microscopic image sequences.”, J Biomed Opt, vol. 8, p. 40--47, 2003.

B. Antic and Ommer, B., “Spatio-temporal Video Parsing for Abnormality Detection”, arXiv, vol. abs/1502.06235, 2015.

Technical Report (4.61 MB)

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.

M. Jäger, Humbert, S., and Hamprecht, F. A., “Sputter Tracking for the Automatic Monitoring of Industrial Laser Welding Processes”, IEEE Transactions on Industrial Electronics, vol. 55, pp. 2177-2184, 2008.

Technical Report (1.83 MB)

D. Cremers and Schnörr, C., “Statistical Shape Knowledge in Variational Motion Segmentation”, Image and Vision Comp., vol. 21, pp. 77-86, 2003.

F. A. Hamprecht, Peter, C., Daura, X., Thiel, W., and van Gunsteren, W. F., “A strategy for analysis of (molecular) equilibrium simulations: configuration space density estimation, clustering and visualization”, Journal of Chemical Physics, vol. 114, pp. 2079-2089, 2001.

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.

U. Schurr, Schuberth, B., Aloni, R., Pradel, K. S., Schmund, D., Jähne, B., and Ullrich, C. I., “Structural and functional evidence for xylem-mediated water transport and high tranpiration in Agrobacterium tumefaciens-induced tumors of Ricinus communis”, Botanica Acta, vol. 109, p. 405--412, 1996.

X. Lou and Hamprecht, F. A., “Structured Learning from Partial Annotations”, ICML 2012. Proceedings, 2012.

Technical Report (843.45 KB)

C. Schnörr, “A Study of a Convex Variational Diffusion Approach for Image Segmentation and Feature Extraction”, J. of Math. Imag. and Vision, vol. 8, pp. 271–292, 1998.

B. Savchynskyy, Kappes, J. H., Schmidt, S., and Schnörr, C., “A Study of Nesterov's Scheme for Lagrangian Decomposition and MAP Labeling”, IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), accepted as oral presentation, pp. 1817 - 1823, 2011.

M. Bergtholdt, Kappes, J. H., Schmidt, S., and Schnörr, C., “A Study of Parts-Based Object Class Detection Using Complete Graphs”, Int. J. Comp. Vision, vol. 87, pp. 93-117, 2010.

M. Bergtholdt, Kappes, J. H., Schmidt, S., and Schnörr, C., “A Study of Parts-Based Object Class Detection Using Complete Graphs”, Int.~J.~Comp.~Vision, vol. 87, pp. 93-117, 2010.

Technical Report (2.18 MB)

X. Zhang and Garbe, C. S., “Studying dynamical processes of air-sea exchanges with air-water interface image techniques”, Recent Research Developments in Fluid Dynamics, vol. 5, p. 57--87, 2004.

M. Desana and Schnörr, C., “Sum-Product Graphical Models”, Machine Learning, vol. 109, pp. 135–173, 2020.

M. Desana and Schnörr, C., “Sum-Product Graphical Models”, Machine Learning, 2019.

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