Export 180 results:
Author Title [ Type(Desc)] Year
Filters: Author is Schnörr, C.  [Clear All Filters]
Conference Paper
Schellewald, C and Schnörr, C (2003). Subgraph Matching with Semidefinite Programming. Proc. Int. Workshop on Combinatorial Image Analysis (IWCIA'03). Palermo, Italy
Neumann, J, Schnörr, C and Steidl, G (2004). SVM-based Feature Selection by Direct Objective Minimisation. Pattern Recognition, Proc. 26th DAGM Symposium. Springer. 3175 212-219
Kappes, J H, Petra, S, Schnörr, C and Zisler, M (2015). TomoGC: Binary Tomography by Constrained Graph Cuts. Proc. GCPR
Petra, S, Schnörr, C, Schröder, A and Wieneke, B (2007). Tomographic Image Reconstruction in Experimental Fluid Dynamics: Synopsis and Problems. Proc. 6th Workshop on Modelling of Environmental and Life Sciences Problems (WMM 07). Ed Acad Romane, Bucuresti, Constanta, Romania
Peckar, W, Schnörr, C, Rohr, K and Stiehl, H S (1997). Two-Step Parameter-Free Elastic Image Registration with Prescribed Point Displacements. Proc. 9th Int. Conf. on Image Analysis and Processing (ICIAP'97). Florence, Italy
Keuchel, J, Schnörr, C, Schellewald, C and Cremers, D (2002). Unsupervised Image Partitioning with Semidefinite Programming. Pattern Recognition, Proc. 24th DAGM Symposium. Springer, Zürich, Switzerland. 2449 141–149
Zern, A, Zisler, M, Aström, F, Petra, S and Schnörr, C (2018). Unsupervised Label Learning on Manifolds by Spatially Regularized Geometric Assignment. GCPR
Zisler, M, Zern, A, Petra, S and Schnörr, C (2019). Unsupervised Labeling by Geometric and Spatially Regularized Self-Assignment. Proc. SSVM. Springer
Schnörr, (2000). Variational Adaptive Smoothing and Segmentation. Computer Vision and Applications: A Guide for Students and Practitioners. Academic Press, San Diego. 459–482
Kohlberger, T, Mémin, E and Schnörr, C (2003). Variational Dense Motion Estimation Using the Helmholtz Decomposition. Scale Space Methods in Computer Vision. Springer. 2695 432–448
Schnörr, C and Weickert, J (2000). Variational Image Motion Computation: Theoretical Framework, Problems and Perspectives. Mustererkennung 2000. Springer, Kiel, Germany
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
Savarino, F and Schnörr, C (2019). A Variational Perspective on the Assignment Flow. Proc. SSVM. Springer
Schnörr, (1989). Zur Schätzung von Geschwindigkeitsvektorfeldern in Bildfolgen mit einer richtungsabhängigen Glattheitsforderung. Mustererkennung 1989, 11. DAGM-Symposium. Springer-Verlag, Hamburg. 219 294–301
In Collection
Schnörr, (2020). Assignment Flows. Handbook of Variational Methods for Nonlinear Geometric Data. Springer. 235—260.
Schnörr, (2019). Assignment Flows. Variational Methods for Nonlinear Geometric Data and Applications. Springer
Weber, S, Schnörr, C, Schüle, T and Hornegger, J (2005). Binary Tomography by Iterating Linear Programs. Geometric Properties from Incomplete Data. Springer
Heiler, M, Keuchel, J and Schnörr, C (2005). Semidefinite Clustering for Image Segmentation with A-priori Knowledge. Pattern Recognition, Proc. 27th DAGM Symposium. Springer. 3663 309–317
Vlasenko, A and Schnörr, C (2009). Variational Approaches for Model-Based PIV and Visual Fluid Analysis. Imaging Measurement Methods for Flow Analysis. Springer. 106 247-256
Ruhnau, P, Kohlberger, T, Nobach, H and Schnörr, C (2004). Variational Optical Flow Estimation for Particle Image Velocimetry. Proc. Lasermethoden in der Strömungsmeßtechnik. Deutsche Gesellschaft für Laser-Anemometrie GALA e.V., Karlsruhe
Schnörr, C, Schüle, T and Weber, S (2007). Variational Reconstruction with DC-Programming. Advances in Discrete Tomography and Its Applications. Birkhäuser, Boston
Journal Article
Wiehler, K, Heers, J, Schnörr, C, Stiehl, H –S and Grigat, R –R (2001). A 1D analog VLSI implementation for non-linear real-time signal preprocessing. Real–Time Imaging. 7 127–142
Schüle, T, Weber, S and Schnörr, C (2005). Adaptive Reconstruction of Discrete-Valued Objects from few Projections. Electr. Notes in Discr. Math. 20 365-384
Zern, A, Zeilmann, A and Schnörr, C (2020). Assignment Flows for Data Labeling on Graphs: Convergence and Stability. preprint: arXiv.
Keuchel, J, Schnörr, C, Schellewald, C and Cremers, D (2003). Binary Partitioning, Perceptual Grouping, and Restoration with Semidefinite Programming. 25 1364–1379
Neumann, J, Schnörr, C and Steidl, G (2005). Combined SVM-based Feature Selection and Classification. Machine Learning. 61 129-150
Schnörr, (1992). Computation of Discontinuous Optical Flow by Domain Decomposition and Shape Optimization. ijcv. 8 153–165
Lellmann, J and Schnörr, C (2011). Continuous Multiclass Labeling Approaches and Algorithms. CoRR. abs/1102.5448.
Savarino, F and Schnörr, C (2019). Continuous-Domain Assignment Flows. preprint: arXiv.
Schnörr, (1991). Determining Optical Flow for Irregular Domains by Minimizing Quadratic Functionals of a Certain Class. ijcv. 6 25–38
Schüle, T, Schnörr, C, Weber, S and Hornegger, J (2005). Discrete Tomography By Convex-Concave Regularization and D.C. Programming. Discr. Appl. Math. 151 229-243
Kohlberger, T, Schnörr, C, Bruhn, A and Weickert, J (2005). Domain decomposition for variational optical flow computation. IEEE Trans. Image Proc. 14 1125-1137
Neumann, J, Schnörr, C and Steidl, G (2005). Efficient Wavelet Adaption for Hybrid Wavelet-Large Margin Classifiers. Pattern Recognition. 38 1815-1830