Publications

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Schmidt, S, Savchynskyy, B, Kappes, J H and Schnörr, C (2011). Evaluation of a First-Order Primal-Dual Algorithm for MRF Energy Minimization. EMMCVPR. Springer. 6819 89-103PDF icon Technical Report (684.13 KB)
Schmidt, P (2016). Deep Learning For Bioimage Analysis. University of Heidelberg
Schmidt, U, Rother, C, Nowozin, S, Jancsary, J and Roth, S (2013). Discriminative Non-Blind Deblurring
Schmidt, S, Kappes, J H, Bergtholdt, M, Pekar, V, Dries, S, Bystrov, D and Schnörr, C (2007). Spine Detection and Labeling Using a Parts-Based Graphical Model. Proc. 20th International Conference on Information Processing in Medical Imaging (IPMI 2007). Springer. 4584 122-133
Schmidt, S, Savchynskyy, B, Kappes, J Hendrik and Schnörr, C (2011). Evaluation of a First-Order Primal-Dual Algorithm for MRF Energy Minimization. EMMCVPR. Springer. 6819 89-103
Schmitzer, B and Schnörr, C (2014). Globally Optimal Joint Image Segmentation and Shape Matching based on Wasserstein Modes
Schmitzer, B and Schnörr, C (2013). Object Segmentation by Shape Matching with Wasserstein Modes. Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR 2013). 123-136
Schmitzer, B and Schnörr, C (2013). Modelling convex shape priors and matching based on the Gromov-Wasserstein distance. Journal of Mathematical Imaging and Vision. 46 143-159
Schmitzer, B and Schnörr, C (2013). Contour Manifolds and Optimal Transport
Schmitzer, B and Schnörr, C (2013). A Hierarchical Approach to Optimal Transport. Scale Space and Variational Methods (SSVM 2013). 452-464
Schmitzer, B and Schnörr, C (2012). Weakly Convex Coupling Continuous Cuts and Shape Priors. Scale Space and Variational Methods (SSVM 2011). 423-434
Schmitzer, B and Schnörr, C (2015). Globally Optimal Joint Image Segmentation and Shape Matching based on Wasserstein Modes. J. Math. Imag. Vision. 52 436–458. http://link.springer.com/article/10.1007/s10851-014-0546-8
Schmitzer, B and Schnörr, C (2014). Globally Optimal Joint Image Segmentation and Shape Matching based on Wasserstein ModesPDF icon Technical Report (2.9 MB)
Schmitzer, B and Schnörr, C (2013). Modelling convex shape priors and matching based on the Gromov-Wasserstein distance. Journal of Mathematical Imaging and Vision. 46 143-159PDF icon Technical Report (957.78 KB)
Schmitzer, B and Schnörr, C (2015). Globally Optimal Joint Image Segmentation and Shape Matching based on Wasserstein Modes. J.~Math.~Imag.~Vision. 52 436--458. http://link.springer.com/article/10.1007/s10851-014-0546-8PDF icon Technical Report (1.97 MB)
Schmund, D, Schurr, U, Jähne, B, Haußecker, H and Geißler, P (1999). Plant-leaf growth studied by image sequence analysis. Handbook of Computer Vision and Applications. Academic Press. 3: Systems and Applications 719-735
Schmund, D (1999). Development of an Optical Flow Based System for the Precise Measurement of Plant Growth. IWR, Fakultät für Physik und Astronomie, Univ.\ Heidelberg
Schmund, D (1995). Voruntersuchung Der Einsatzmöglichkeiten Digitaler Bildverarbeitung Zur Analyse Von Transportvorgängen Und Wachstumsprozessen In Pflanzen. University of Heidelberg
Schmund, D, Münsterer, T, Lauer, H, Jähne, B and Jähne, B (1995). The circular wind wave facilities at the University of Heidelberg. Air-Water Gas Transfer - Selected papers from the Third International Symposium on Air-Water Gas Transfer. AEON. 505--516
Schmund, D, Schurr, U and Jähne, B (2000). Optical leaf growth analysis. Computer Vision and Applications - A Guide for Students and Practitioners. Academic Press. 640-641
Schmund, D, Stitt, M, Jähne, B and Schurr, U (1998). Quantitative analysis of the local rates of growth of dicot leaves at a high temporal and spatial resolution, using image sequence analysis. Plant Journal. 16 505--514
Schnieders, J (2011). 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
Schnörr, (2007). Signal and Image Approximation with Level-Set Constraints. Computing. 81 137-160PDF icon Technical Report (506.8 KB)
Schnörr, (2020). Assignment Flows. Handbook of Variational Methods for Nonlinear Geometric Data. Springer. 235—260. https://www.springer.com/gp/book/9783030313500
Schnörr, (2019). Assignment Flows. Variational Methods for Nonlinear Geometric Data and Applications. Springer
Schnörr, (1989). Zur Schätzung von Geschwindigkeitsvektorfeldern in Bildfolgen mit einer richtungsabhängigen Glattheitsforderung. Mustererkennung 1989, 11. DAGM-Symposium. Springer-Verlag, Hamburg. 219 294–301
Schnörr, (1996). Repräsentation von Bilddaten mit einem konvexen Variationsansatz. Mustererkennung 1996. Springer-Verlag, Berlin, Heidelberg. 21–28
Schnörr, (2000). Variational Adaptive Smoothing and Segmentation. Computer Vision and Applications: A Guide for Students and Practitioners. Academic Press, San Diego. 459–482
Schnörr, (1999). Variational Methods for Adaptive Image Smoothing and Segmentation. Handbook on Computer Vision and Applications: Signal Processing and Pattern Recognition. Academic Press, San Diego. 2 451–484
Schnörr, (1994). Bewegungssegmentation von Bildfolgen durch die Minimierung konvexer nicht-quadratischer Funktionale. Mustererkennung 1994. Technische Universität Wien. 5 178–185
Schnörr, (2007). Signal and Image Approximation with Level-Set Constraints. Computing. 81 137-160
Schnörr, (2001). Statistische Mustererkennung
Schnörr, (1998). Variational approaches to Image Segmentation and Feature Extraction. University of Hamburg, Comp. Sci. Dept., Hamburg, Germany
Schnörr, (1998). A Study of a Convex Variational Diffusion Approach for Image Segmentation and Feature Extraction. J. of Math. Imag. and Vision. 8 271–292
Schnörr, (1996). Representation Of Images By A Convex Variational Diffusion Approach. FB Informatik, Universität Hamburg

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