Publications

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Raisch, F, Scharr, H, Kirchgeßner, N, Jähne, B, Fink, R H A and Uttenweiler, D (2002). Velocity and feature estimation of actin filaments using active contours in noisy fluorescence image sequences. Proc. 2nd IASTED Int. Conf. Visualization, Imaging and Image Processing. 645--650
Kandemir, M, Haußmann, M, Diego, F, Rajamani, K, van der Laak, J and Hamprecht, F A (2016). Variational weakly-supervised Gaussian processes. BMVC. ProceedingsPDF icon Technical Report (3.28 MB)
Esser, P, Sutter, E and Ommer, B (2018). A Variational U-Net for Conditional Appearance and Shape Generation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (short Oral). https://compvis.github.io/vunet/
Goldlücke, B and Wanner, S (2013). The Variational Structure of Disparity and Regularization of 4D Light Fields. IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Bergtholdt, M, Cremers, D and Schnörr, C (2005). Variational Segmentation with Shape Priors. Handbook of Mathematical Models in Computer Vision. Springer. 147-160
Becker, F, Lenzen, F, Kappes, J H and Schnörr, C (2013). Variational Recursive Joint Estimation of Dense Scene Structure and Camera Motion from Monocular High Speed Traffic Sequences. International Journal of Computer Vision. Springer US. 105 269--297. http://dx.doi.org/10.1007/s11263-013-0639-7PDF icon Technical Report (15.4 MB)
Becker, F, Lenzen, F, Kappes, J H and Schnörr, C (2011). Variational Recursive Joint Estimation of Dense Scene Structure and Camera Motion from Monocular High Speed Traffic Sequences. 2011 IEEE International Conference on Computer Vision ICCV. 1692-1699
Becker, F, Lenzen, F, Kappes, J H and Schnörr, C (2011). Variational Recursive Joint Estimation of Dense Scene Structure and Camera Motion from Monocular High Speed Traffic Sequences. 2011 IEEE International Conference on Computer Vision (ICCV). 1692 -- 1699PDF icon Technical Report (4.9 MB)
Becker, F, Lenzen, F, Kappes, J H and Schnörr, C (2013). Variational Recursive Joint Estimation of Dense Scene Structure and Camera Motion from Monocular High Speed Traffic Sequences. International Journal of Computer Vision. 105 (3) 269-297
Schnörr, C, Schüle, T and Weber, S (2007). Variational Reconstruction with DC-Programming. Advances in Discrete Tomography and Its Applications. Birkhäuser
Ruhnau, P, Kohlberger, T, Nobach, H and Schnörr, C (2005). Variational Optical Flow Estimation for Particle Image Velocimetry. Experiments in Fluids. 38 21--32PDF icon Technical Report (1.21 MB)
Weickert, J and Schnörr, C (2001). Variational Optic Flow Computation with a Spatio-Temporal Smoothness Constraint. J.~Math.~Imaging and Vision. 14 245--255
Bruhn, A, Weickert, J, Feddern, C, Kohlberger, T and Schnörr, C (2003). Variational Optic Flow Computation In Real-Time. Dept.~Math.~and Comp.~Science
Bruhn, A, Weickert, J, Feddern, C, Kohlberger, T and Schnörr, C (2005). Variational optic flow computation in real-time. IEEE Trans.~Image Proc. 14 608--615
Schnörr, (1999). Variational Methods for Adaptive Image Smoothing and Segmentation. Handbook on Computer Vision and Applications: Signal Processing and Pattern Recognition. Academic Press. 2 451--484
Wanner, S and Goldlücke, B (2014). Variational light field analysis for disparity estimation and super-resolution. IEEE Trans. Pattern Analysis Machine Intelligence. 36 606--619
Swoboda, P and Schnörr, C (2013). Variational Image Segmentation and Cosegmentation with the Wasserstein Distance. Energy Minimization Methods in Computer Vision and Pattern Recognition. Springer. 8081 321--334PDF icon Technical Report (8.06 MB)
Schnörr, C and Weickert, J (2000). Variational Image Motion Computation: Theoretical Framework, Problems and Perspectives. Mustererkennung 2000. Springer
Lenzen, F, Becker, F, Lellmann, J, Petra, S and Schnörr, C (2011). Variational Image Denoising with Adaptive Constraint Sets. Proceedings of the 3nd International Conference on Scale Space and Variational Methods in Computer Vision 2011, in press. Springer. 6667 206-217
Lenzen, F, Becker, F, Lellmann, J, Petra, S and Schnörr, C (2012). Variational Image Denoising with Adaptive Constraint Sets. LNCS. Springer. 206-217PDF icon Technical Report (649.03 KB)
(2005). Variational, Geometric and Level Sets in Computer Vision (VLSM'05). Springer. 3752
Heitz, D, Mémin, E and Schnörr, C (2010). Variational fluid flow measurements from image sequences: synopsis and perspectives. Exp.~Fluids. 48 369-393PDF icon Technical Report (1.91 MB)
Ruhnau, P, Stahl, A and Schnörr, C (2007). Variational Estimation of Experimental Fluid Flows with Physics-Based Spatio-Temporal Regularization. Measurement Science and Technology. 18 755-763PDF icon Technical Report (842.06 KB)
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
Becker, F, Wieneke, B, Yuan, J and Schnörr, C (2008). Variational Correlation Approach to Flow Measurement with Window Adaption. 14th International Symposium on Applications of Laser Techniques to Fluid Mechanics. 1.1.3PDF icon Technical Report (3.37 MB)
Becker, F, Wieneke, B, Yuan, J and Schnörr, C (2008). Variational Correlation Approach to Flow Measurement with Window Adaption. 14th International Symposium on Applications of Laser Techniques to Fluid Mechanics. 1.1.8
Becker, F (2009). Variational Correlation and Decomposition Methods for Particle Image Velocimetry. Heidelberg University, Faculty of Mathematics and Computer Sciences. http://www.ub.uni-heidelberg.de/archiv/9766/
Haußmann, M, Hamprecht, F A and Kandemir, M (2017). Variational Bayesian Multiple Instance Learning with Gaussian Processes. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 6570-6579PDF icon Technical Report (1.29 MB)
Schnörr, (1998). Variational approaches to Image Segmentation and Feature Extraction. University of Hamburg, Comp.~Sci.~Dept.
Vlasenko, A and Schnörr, C (2009). Variational Approaches for Model-Based PIV and Visual Fluid Analysis. Imaging Measurement Methods for Flow Analysis. Springer. 106 247-256PDF icon Technical Report (3.39 MB)
Schnörr, C, Sprengel, R and Neumann, B (1996). A Variational Approach to the Design of Early Vision Algorithms. Computing Suppl. 11 149-165
Telea, A, Preußer, T, Garbe, C S, Droske, M and Rumpf, M (2006). A variational approach to joint denoising, edge detection and motion estimation. Proceedings of the 28th DAGM Symposium on Pattern Recognition. 525--535. http://numod.ins.uni-bonn.de/research/papers/public/PrDrGa06.pdf
Becker, F, Wieneke, B, Yuan, J and Schnörr, C (2008). A Variational Approach to Adaptive Correlation for Motion Estimation in Particle Image Velocimetry. Pattern Recognition -- 30th DAGM Symposium. Springer Verlag. 5096 335--344PDF icon Technical Report (1.82 MB)
Becker, F, Wieneke, B, Yuan, J and Schnörr, C (2008). A Variational Approach to Adaptive Correlation for Motion Estimation in Particle Image Velocimetry". Pattern Recognition -- 30th DAGM Symposium. 5096 335-344
Ruhnau, P, Gütter, C, Putze, T and Schnörr, C (2005). A variational approach for particle tracking velocimetry. Meas.~Science and Techn. 16 1449-1458

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