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Rhemann, C, Rother, C, Wang, J, Gelautz, M, Kohli, P and Rott, P (2009). A perceptually motivated online benchmark for image matting. 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009. 2009 IEEE 1826–1833
Munder, S, Schnörr, C and Gavrila, D M (2008). Pedestrian Detection and Tracking Using a Mixture of View-Based Shape-Texture Models. IEEE Trans. Intell. Transp. Systems. 9 333-343
Boppel, S (2008). Peak Identification For Liquid Chromatography And Mass Spectrometry. University of Heidelberg
Weickert, J and Schnörr, C (2000). PDE–Based Preprocessing of Medical Images. Künstliche Intelligenz. 3 5–10
Sauer, P (2008). Pattern Recognition On Statistically Textured Surfaces. University of Heidelberg
Menze, B H (2007). Pattern Recognition in the Quantitative Analysis of Vector-Valued Image Data: Diagnostic Systems and Applications. University of Heidelberg
(2007). Pattern Recognition, 29Th Dagm Symposium, Heidelberg, Germany, September 12-14, 2007, Proceedings. Springer
Schnörr, C and Jähne, B (2007). Pattern Recognition, 29th DAGM Symposium, Heidelberg, Germany, September 12-14. Springer. 4713
(2007). Pattern Recognition – 29th DAGM Symposium. LCNS. Springer. 4713
Bleyer, M, Rhemann, C and Rother, C (2011). PatchMatch Stereo - Stereo Matching with Slanted Support Windows. 14.1–14.11
Kräuter, C, Richter, K E, Mesarchaki, E, Rocholz, R, Williams, J and Jähne, B (2012). Partitioning of the Trasfer Resistance between Air and Water. SOLAS Open Science Conference, Washington State, USA
Humbert, S (2005). Partikel-Verfolgung Beim Laserschwei\Dfen Mit Dem Kalman-Filter. University of Heidelberg
Engelmann, D, Stöhr, M, Garbe, C S, Hering, F, Jähne, B, Haußecker, H and Geißler, P (1999). Particle-tracking velocimetry. Handbook of Computer Vision and Applications. Academic Press. 3: Systems and Applications 663-697
Engelmann, D, Stöhr, M, Garbe, C S, Hering, F and Jähne, B (2000). Particle-tracking velocimetry. Computer Vision and Applications - A Guide for Students and Practitioners. Academic Press. 646-647
Hering, F, Wierzimok, D, Leue, C and Jähne, B (1998). Particle tracking velocimetry beneath water waves. part II : water waves. Exp. Fluids. 24 10-16
Hering, F, Wierzimok, D, Leue, C and Jähne, B (1997). Particle tracking velocimetry beneath water waves. Part I: visualization and tracking algorithms. Exp. Fluids. 23 472--482
Hering, F, Merle, M, Wierzimok, D and Jähne, B (1995). Particle tracking in space time sequences. Computer Analysis of Images and Patterns. Springer. 970 294--301
Marxen, M (1998). Particle Image Velocimetry In Strömungen Mit Starken Geschwindigkeitsgradienten. IWR, Fakultät für Physik und Astronomie, Univ.\ Heidelberg
Swoboda, P, Savchynskyy, B, Kappes, J H and Schnörr, C (2013). Partial Optimality via Iterative Pruning for the Potts Model. Scale Space and Variational Methods (SSVM 2013)PDF icon Technical Report (159.71 KB)
Swoboda, P, Savchynskyy, B, Kappes, J H and Schnörr, C (2013). Partial Optimality via Iterative Pruning for the Potts Model. Scale Space and Variational Methods (SSVM 2013)
Swoboda, P, Savchynskyy, B, Kappes, J H and Schnörr, C (2013). Partial Optimality via Iterative Pruning for the Potts Model. Proceedings of the 4th International Conference on Scale Space and Variational Methods in Computer Vision SSVM. 477-488
Kohli, P, Shekhovtsov, A, Rother, C, Kolmogorov, V and Torr, P (2008). On partial optimality in multi-label MRFs. Proceedings of the 25th International Conference on Machine Learning. 480–487
Swoboda, P, Savchynskyy, B, Kappes, J H and Schnörr, C (2014). Partial Optimality by Pruning for MAP-inference with General Graphical Models. IEEE Conference on Computer Vision and Pattern Recognition 2014PDF icon Technical Report (703.34 KB)
Swoboda, P, Shekhovtsov, A, Kappes, J Hendrik, Schnörr, C and Savchynskyy, B (2016). Partial Optimality by Pruning for MAP-Inference with General Graphical Models. IEEE Transactions on Pattern Analysis and Machine Intelligence. IEEE Computer Society. 38 1370–1382
Swoboda, P, Shekhovtsov, A, Kappes, J H, Schnörr, C and Savchynskyy, B (2016). Partial Optimality by Pruning for MAP-Inference with General Graphical Models. IEEE Trans. Patt. Anal. Mach. Intell. 38 1370–1382
Swoboda, P, Savchynskyy, B, Kappes, J H and Schnörr, C (2014). Partial Optimality by Pruning for MAP-inference with General GraphicalModels. CVPR. Proceedings. 1170-1177
Swoboda, P, Savchynskyy, B, Kappes, J H and Schnörr, C (2014). Partial Optimality by Pruning for MAP-inference with General Graphical Models. IEEE Conference on Computer Vision and Pattern Recognition 2014
Monroy, A, Kröger, T, Arnold, M and Ommer, B (2011). Parametric Object Detection for Iconographic Analysis. Scientific Computing & Cultural Heritage. http://www.academia.edu/9439693/Parametric_Object_Detection_for_Iconographic_Analysis
Bodnariuc, E, Petra, S, Poelma, C and Schnörr, C (2016). Parametric Dictionary-Based Velocimetry for Echo PIV. Proc. CGPR
Jähne, B, Münnich, K O, Bösinger, R, Dutzi, A, Huber, W A and Libner, P (1987). On the parameters influencing air-water gas exchange. J. Geophys. Res. 92 1937--1950
Jähne, B, Fischer, K H, Ilmberger, J, Libner, P, Weiss, W, Imboden, D, Lemnin, U and Jaquet, J M (1984). Parameterization of air/lake gas exchange. Gas transfer at water surfaces. Reidel. 469--476
Peckar, W, Schnörr, C, Rohr, K and Stiehl, H –S (1999). Parameter-Free Elastic Deformation Approach for 2D and 3D Registration Using Prescribed Displacements. J. Math. Imaging and Vision. 10 143–162
Garbe, C S and Ommer, B (2013). Parameter Estimation in Image Processing and Computer Vision. Model Based Parameter Estimation: Theory and Applications. Springer. 311--334PDF icon Technical Report (928 KB)
Heers, J, Schnörr, C and Stiehl, H –S (1998). Parallele und global konvergente iterative Minimierung nichtlinearer Variationsansätze zur adaptiven Glättung und Segmentation von Bildern. Mustererkennung 1998. Springer, Heidelberg
Kohlberger, T, Schnörr, C, Bruhn, A and Weickert, J (2004). Parallel Variational Motion Estimation by Domain Decomposition and Cluster Computing. Computer Vision – ECCV 2004. Springer. 3024 205-216

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