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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

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

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 Graphical Models. IEEE Conference on Computer Vision and Pattern Recognition 2014

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 (2013). Partial Optimality via Iterative Pruning for the Potts Model. Scale Space and Variational Methods (SSVM 2013)

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. Proceedings of the 4th International Conference on Scale Space and Variational Methods in Computer Vision SSVM. 477-488

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)

Marxen, M (1998). Particle Image Velocimetry In Strömungen Mit Starken Geschwindigkeitsgradienten. IWR, Fakultät für Physik und Astronomie, Univ.\ Heidelberg

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

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, Wierzimok, D, Leue, C and Jähne, B (1998). Particle tracking velocimetry beneath water waves. part II : water waves. Exp. Fluids. 24 10-16

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

Humbert, S (2005). Partikel-Verfolgung Beim Laserschwei\Dfen Mit Dem Kalman-Filter. University of Heidelberg

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

Bleyer, M, Rhemann, C and Rother, C (2011). PatchMatch Stereo - Stereo Matching with Slanted Support Windows. 14.1–14.11

(2007). Pattern Recognition – 29th DAGM Symposium. LCNS. Springer. 4713

Schnörr, C and Jähne, B (2007). Pattern Recognition, 29th DAGM Symposium, Heidelberg, Germany, September 12-14. Springer. 4713

(2007). Pattern Recognition, 29Th Dagm Symposium, Heidelberg, Germany, September 12-14, 2007, Proceedings. Springer

Menze, B H (2007). Pattern Recognition in the Quantitative Analysis of Vector-Valued Image Data: Diagnostic Systems and Applications. University of Heidelberg

Sauer, P (2008). Pattern Recognition On Statistically Textured Surfaces. University of Heidelberg

Weickert, J and Schnörr, C (2000). PDE–Based Preprocessing of Medical Images. Künstliche Intelligenz. 3 5–10

Boppel, S (2008). Peak Identification For Liquid Chromatography And Mass Spectrometry. University of Heidelberg

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

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. www.alphamatting.com.

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. www.alphamatting.com.

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

Kondermann, D, Abraham, S, Förstner, W, Gehrig, S, Imiya, A, Jähne, B, Klose, F, Magor, M, Mayer, H, Mester, R, Pajdla, T, Reulke, R and Zimmer, H (2012). On Performance Analysis of Optical Flow Algorithms. Outdoor and Large-Scale Real-Worls Scene Analysis, Dagstuhl-Workshop 2011. Springer. LNCS 329-355

Jähne, B and Haußecker, H (2000). Performance characteristics of low-level motion estimation in spatiotemporal images. Performance Characterization in Computer Vision. Kluwer, Dordrecht. pp. 139-152

Jähne, B and Förstner, W (1997). Performance characteristics of low-level motion estimators in spatiotemporal images. DAGM-Workshop Performance Characteristics and Quality of Computer Vision Algorithms, Braunschweig, September 18, 1997. Institute of Photogrammetry, Univ.\ Bonn

Schellewald, C, Roth, S and Schnörr, C (2002). Performance Evaluation Of A Convex Relaxation Approach To The Quadratic Assignment Of Relational Object Views. Dept. Math. and Comp. Science, University of Mannheim, Germany

Mund, J, Zouhar, A, Meyer, L, Fricke, H and Rother, C (2015). Performance evaluation of LiDAR point clouds towards automated FOD detection on airport aprons. Proceedings of ATACCS 2015 - 5th International Conference on Application and Theory of Automation in Command and Control Systems. 85–94

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