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N. Paragios, Faugeras, O., Chan, T., and Schnörr, C., Eds., “Variational, Geometric and Level Sets in Computer Vision (VLSM'05)”, lncs, vol. 3752. Springer, Beijing, China, 2005.
F. Lenzen, Becker, F., Lellmann, J., Petra, S., and Schnörr, C., “Variational Image Denoising with Adaptive Constraint Sets”, in LNCS, 2012, pp. 206-217.PDF icon Technical Report (649.03 KB)
F. Lenzen, Becker, F., Lellmann, J., Petra, S., and Schnörr, C., “Variational Image Denoising with Adaptive Constraint Sets”, in Proceedings of the 3rd International Conference on Scale Space and Variational Methods in Computer Vision 2011, 2012, pp. 206-217.
F. Lenzen, Becker, F., Lellmann, J., Petra, S., and Schnörr, C., “Variational Image Denoising with Adaptive Constraint Sets”, in Proceedings of the 3nd International Conference on Scale Space and Variational Methods in Computer Vision 2011, in press, 2011, vol. 6667, pp. 206-217.
C. Schnörr and Weickert, J., “Variational Image Motion Computation: Theoretical Framework, Problems and Perspectives”, in Mustererkennung 2000, Kiel, Germany, 2000.
P. Swoboda and Schnörr, C., “Variational Image Segmentation and Cosegmentation with the Wasserstein Distance”, in Energy Minimization Methods in Computer Vision and Pattern Recognition, 2013, vol. 8081, pp. 321–334.
P. Swoboda and Schnörr, C., “Variational Image Segmentation and Cosegmentation with the Wasserstein Distance”, in Energy Minimization Methods in Computer Vision and Pattern Recognition, 2013, vol. 8081, p. 321--334.PDF icon Technical Report (8.06 MB)
S. Wanner and Goldlücke, B., “Variational light field analysis for disparity estimation and super-resolution”, IEEE Trans. Pattern Analysis Machine Intelligence, vol. 36, p. 606--619, 2014.
C. Schnörr, “Variational Methods for Adaptive Image Smoothing and Segmentation”, in Handbook on Computer Vision and Applications: Signal Processing and Pattern Recognition, San Diego, 1999, vol. 2, pp. 451–484.
A. Bruhn, Weickert, J., Feddern, C., Kohlberger, T., and Schnörr, C., “Variational optic flow computation in real-time”, IEEE Trans. Image Proc., vol. 14, pp. 608–615, 2005.
A. Bruhn, Weickert, J., Feddern, C., Kohlberger, T., and Schnörr, C., “Variational Optic Flow Computation in Real-Time”, Dept. Math. and Comp. Science, Saarland University, Germany, 89, 2003.
J. Weickert and Schnörr, C., “Variational Optic Flow Computation with a Spatio-Temporal Smoothness Constraint”, J. Math. Imaging and Vision, vol. 14, pp. 245–255, 2001.
P. Ruhnau, Kohlberger, T., Nobach, H., and Schnörr, C., “Variational Optical Flow Estimation for Particle Image Velocimetry”, Proc. Lasermethoden in der Strömungsmeßtechnik. Deutsche Gesellschaft für Laser-Anemometrie GALA e.V., Karlsruhe, 2004.
P. Ruhnau, Kohlberger, T., Nobach, H., and Schnörr, C., “Variational Optical Flow Estimation for Particle Image Velocimetry”, Experiments in Fluids, vol. 38, pp. 21–32, 2005.
P. Ruhnau, Kohlberger, T., Nobach, H., and Schnörr, C., “Variational Optical Flow Estimation for Particle Image Velocimetry”, Experiments in Fluids, vol. 38, p. 21--32, 2005.PDF icon Technical Report (1.21 MB)
F. Savarino and Schnörr, C., “A Variational Perspective on the Assignment Flow”, in Proc. SSVM, 2019.
C. Schnörr, Schüle, T., and Weber, S., “Variational Reconstruction with DC-Programming”, Advances in Discrete Tomography and Its Applications. Birkhäuser, Boston, 2007.
F. Becker, Lenzen, F., Kappes, J. H., and Schnörr, C., “Variational Recursive Joint Estimation of Dense Scene Structure and Camera Motion from Monocular High Speed Traffic Sequences”, International Journal of Computer Vision, vol. 105, no. 3, p. 269--297, 2013.PDF icon Technical Report (15.4 MB)
F. Becker, Lenzen, F., Kappes, J. H., and Schnörr, C., “Variational Recursive Joint Estimation of Dense Scene Structure and Camera Motion from Monocular High Speed Traffic Sequences”, in 2011 IEEE International Conference on Computer Vision (ICCV), 2011, p. 1692 -- 1699.PDF icon Technical Report (4.9 MB)
F. Becker, Lenzen, F., Kappes, J. H., and Schnörr, C., “Variational Recursive Joint Estimation of Dense Scene Structure and Camera Motion from Monocular High Speed Traffic Sequences”, International Journal of Computer Vision, vol. 105, pp. 269–297, 2013.
F. Becker, Lenzen, F., Kappes, J. H., and Schnörr, C., “Variational Recursive Joint Estimation of Dense Scene Structure and Camera Motion from Monocular High Speed Traffic Sequences”, in 2011 IEEE International Conference on Computer Vision (ICCV), 2011, pp. 1692 – 1699.
F. Becker, Lenzen, F., Kappes, J. H., and Schnörr, C., “Variational Recursive Joint Estimation of Dense Scene Structure and Camera Motion from Monocular High Speed Traffic Sequences”, International Journal of Computer Vision, vol. 105 (3), pp. 269-297, 2013.
F. Becker, Lenzen, F., Kappes, J. H., and Schnörr, C., “Variational Recursive Joint Estimation of Dense Scene Structure and Camera Motion from Monocular High Speed Traffic Sequences”, in 2011 IEEE International Conference on Computer Vision ICCV, 2011, pp. 1692-1699.
M. Bergtholdt, Cremers, D., and Schnörr, C., “Variational Segmentation with Shape Priors”, Handbook of Mathematical Models in Computer Vision. Springer, pp. 147-160, 2005.
B. Goldlücke and Wanner, S., “The Variational Structure of Disparity and Regularization of 4D Light Fields”, in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013.
P. Esser, Sutter, E., and Ommer, B., “A Variational U-Net for Conditional Appearance and Shape Generation”, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (short Oral), 2018.
M. Kandemir, Haußmann, M., Diego, F., Rajamani, K., van der Laak, J., and Hamprecht, F. A., “Variational weakly-supervised Gaussian processes”, BMVC. Proceedings. 2016.PDF icon Technical Report (3.28 MB)
F. Raisch, Scharr, H., Kirchgeßner, N., Jähne, B., Fink, R. H. A., and Uttenweiler, D., “Velocity and feature estimation of actin filaments using active contours in noisy fluorescence image sequences”, in Proc. 2nd IASTED Int. Conf. Visualization, Imaging and Image Processing, 2002, p. 645--650.
F. Weißer, “Verdunstungsmessungen in einem ringförmigen Wind-Wasser-Kanal mit Hilfe von Psychochrometern und einem WLD-System”, Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg, 1980.
W. Mischler, Rocholz, R., and Weissbach, T., “Vereinfachung der Massenbilanz im Blasentank für luftseitige Messung”, Institut für Umweltphysik, 2011.
J. Strobel, Görlitz, L., and Staudacher, M., “Verfahren und Prüfkörper zur Bestimmung der Reinigungswirkung in einem Ultraschallbild”. 2005.
B. Jähne, “Vergeichende Analyse moderner Bildsensoren für die optische Messtechnik”, in Sensoren und Messsysteme 2004, 2004, vol. 1829, p. 317--324.
R. Beranek, “Vermessung von Blickpunkten durch automatische Bildanalyse für ergonomische Fragestellungen bei der Fahrzeugkonstruktion”, IWR, Fakultät für Physik und Astronomie, Univ.\ Heidelberg, 1996.
G. Balschbach, “Verschiedene Verfahren zur Visualisierung und Größenbestimmung von Gasblasen in Wasser”, Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg, 1994.
B. Antic and Ommer, B., “Video Parsing for Abnormality Detection”, in Proceedings of the IEEE International Conference on Computer Vision, 2011, p. 2415--2422.PDF icon Technical Report (990.21 KB)

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