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M. Schmidt, “How to perform camera measurements according to the EMVA 1288 Standard.”. 2008.
M. Schmidt, “Analysis, Modeling and Dynamic Optimization of 3D Time-of-Flight Imaging Systems”, vol. Dissertation. IWR, Fakultät für Physik und Astronomie, Univ. Heidelberg, 2011.
M. Schmidt, “Spatiotemporal Analysis of Range Imagery”. IWR, Fakultät für Physik und Astronomie, University of Heidelberg, 2008.
M. Schmidt, Erz, M., Zimmermann, K., and Jähne, B., “Exact modelling of time-of-flight cameras for optimal depth maps”, in International Conference on Computational Photography (ICCP) 2010, 2010.
M. Schmidt and Jähne, B., “A physical model of Time-of-Flight 3D imaging systems, including suppression of ambient light”, in 3rd Workshop on Dynamic 3-D Imaging, 2009, vol. 5742, p. 1--15.
M. Schmidt and Jähne, B., “Efficient and robust reduction of motion artifacts for 3D time-of-flight cameras”, in International Conference on 3D Imaging 2011, Liège, Belgium, 2011.
M. Schmidt, Jehle, M., and Jähne, B., “Range flow estimation based on photonic mixing device data”, Int. J. Intelligent Systems Technologies and Applications, vol. 5, p. 380--392, 2008.
M. Schmidt, Jehle, M., and Jähne, B., “Range flow estimation based on photonic mixing device data”, in Proc.\ Dyn3D Workshop, Heidelberg, Sept. 11, 2007, 2007.
M. Schmidt, Zimmermann, K., and Jähne, B., “High frame rate for 3D time-of-flight cameras by dynamic sensor calibration”, in Proceedings IEEE International Conference on Computational Photography (ICCP), 2011, p. 1--8.
S. Schmidt, Kappes, J. H., Bergtholdt, M., Pekar, V., Dries, S., Bystrov, D., and Schnörr, C., “Spine Detection and Labeling Using a Parts-Based Graphical Model”, in Proc. 20th International Conference on Information Processing in Medical Imaging (IPMI 2007), 2007, vol. 4584, pp. 122-133.PDF icon Technical Report (1.46 MB)
S. Schmidt, Savchynskyy, B., Kappes, J. H., and Schnörr, C., “Evaluation of a First-Order Primal-Dual Algorithm for MRF Energy Minimization”, in EMMCVPR, 2011, vol. 6819, pp. 89-103.PDF icon Technical Report (684.13 KB)
P. Schmidt, “Deep Learning for Bioimage Analysis”, University of Heidelberg, 2016.
U. Schmidt, Rother, C., Nowozin, S., Jancsary, J., and Roth, S., “Discriminative Non-blind Deblurring”, 2013.
S. Schmidt, Kappes, J. H., Bergtholdt, M., Pekar, V., Dries, S., Bystrov, D., and Schnörr, C., “Spine Detection and Labeling Using a Parts-Based Graphical Model”, in Proc. 20th International Conference on Information Processing in Medical Imaging (IPMI 2007), 2007, vol. 4584, pp. 122-133.
S. Schmidt, Savchynskyy, B., Kappes, J. Hendrik, and Schnörr, C., “Evaluation of a First-Order Primal-Dual Algorithm for MRF Energy Minimization”, in EMMCVPR, 2011, vol. 6819, pp. 89-103.
B. Schmitzer and Schnörr, C., “Globally Optimal Joint Image Segmentation and Shape Matching based on Wasserstein Modes”. 2014.
B. Schmitzer and Schnörr, C., “Object Segmentation by Shape Matching with Wasserstein Modes”, in Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR 2013), 2013, pp. 123-136.
B. Schmitzer and Schnörr, C., “Modelling convex shape priors and matching based on the Gromov-Wasserstein distance”, Journal of Mathematical Imaging and Vision, vol. 46, pp. 143-159, 2013.
B. Schmitzer and Schnörr, C., “Contour Manifolds and Optimal Transport”. 2013.
B. Schmitzer and Schnörr, C., “A Hierarchical Approach to Optimal Transport”, in Scale Space and Variational Methods (SSVM 2013), 2013, pp. 452-464.
B. Schmitzer and Schnörr, C., “Weakly Convex Coupling Continuous Cuts and Shape Priors”, in Scale Space and Variational Methods (SSVM 2011), 2012, pp. 423-434.
B. Schmitzer and Schnörr, C., “Globally Optimal Joint Image Segmentation and Shape Matching based on Wasserstein Modes”, J. Math. Imag. Vision, vol. 52, pp. 436–458, 2015.
B. Schmitzer and Schnörr, C., “Globally Optimal Joint Image Segmentation and Shape Matching based on Wasserstein Modes”. 2014.PDF icon Technical Report (2.9 MB)
B. Schmitzer and Schnörr, C., “Modelling convex shape priors and matching based on the Gromov-Wasserstein distance”, Journal of Mathematical Imaging and Vision, vol. 46, pp. 143-159, 2013.PDF icon Technical Report (957.78 KB)
B. Schmitzer and Schnörr, C., “Globally Optimal Joint Image Segmentation and Shape Matching based on Wasserstein Modes”, J.~Math.~Imag.~Vision, vol. 52, p. 436--458, 2015.PDF icon Technical Report (1.97 MB)
D. Schmund, Schurr, U., Jähne, B., Haußecker, H., and Geißler, P., “Plant-leaf growth studied by image sequence analysis”, Handbook of Computer Vision and Applications, vol. 3: Systems and Applications. Academic Press, pp. 719-735, 1999.
D. Schmund, “Development of an Optical Flow Based System for the Precise Measurement of Plant Growth”. IWR, Fakultät für Physik und Astronomie, Univ.\ Heidelberg, 1999.
D. Schmund, “Voruntersuchung der Einsatzmöglichkeiten digitaler Bildverarbeitung zur Analyse von Transportvorgängen und Wachstumsprozessen in Pflanzen”, University of Heidelberg, 1995.
D. Schmund, Münsterer, T., Lauer, H., Jähne, B., and Jähne, B., “The circular wind wave facilities at the University of Heidelberg”, in Air-Water Gas Transfer - Selected papers from the Third International Symposium on Air-Water Gas Transfer, 1995, p. 505--516.
D. Schmund, Schurr, U., and Jähne, B., “Optical leaf growth analysis”, Computer Vision and Applications - A Guide for Students and Practitioners. Academic Press, pp. 640-641, 2000.
D. Schmund, Stitt, M., Jähne, B., and Schurr, U., “Quantitative analysis of the local rates of growth of dicot leaves at a high temporal and spatial resolution, using image sequence analysis”, Plant Journal, vol. 16, p. 505--514, 1998.
J. Schnieders, “Investigation of Momentum Transfer across the Air-Sea Interface by Means of Active and Passive Thermography”, Interdisciplinary Center for Scientific Computing (IWR), University of Heidelberg, 2011.
C. Schnörr, “Signal and Image Approximation with Level-Set Constraints”, Computing, vol. 81, pp. 137-160, 2007.PDF icon Technical Report (506.8 KB)
C. Schnörr, “Assignment Flows”, Handbook of Variational Methods for Nonlinear Geometric Data. Springer, p. 235—260, 2020.
C. Schnörr, “Assignment Flows”, Variational Methods for Nonlinear Geometric Data and Applications. Springer, 2019.

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