Publications

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Neigel, P (2017). Self-Similarity Based Detection Of Temporal Motifs In Multivariate Signals. Heidelberg University
Netzsch, T (1995). Dreidimensionale Particle Tracking Velocimetry. IWR, Fakultät für Physik und Astronomie, Univ.\ Heidelberg. http://d-nb.info/945379838
Netzsch, T and Jähne, B (1995). A high-performance system for 3-dimensional particle tracking velocimetry in turbulent flow research using image sequences. Proc. ISPRS Intercommission workshop From Pixels to Sequences, Zürich, March 22-24. Risc Books. 30 Part 5W1 202--207
Netzsch, T and Jähne, B (1992). Dreidimensionale Messung turbulenter Strömung mit Bildverarbeitung. Proc. 14. DAGM-Symposium zur Mustererkennung. Springer. 150--153
Netzsch, T and Jähne, B (1993). Ein schnelles Verfahren zur Lösung des Stereokorrepondenzproblems bei der 3D-Particle Tracking Velocimetry. Proc. 15. DAGM-Symposium Mustererkennung
Neufeld, A, Berger, J, Becker, F, Lenzen, F and Schnörr, C (2015). Estimating Vehicle Ego-Motion and Piecewise Planar Scene Structure from Optical Flow in a Continuous Framework. 37th German Conference on Pattern Recognition
Neumann, J, Schnörr, C and Steidl, G (2003). Feasible Adaption Criteria for Hybrid Wavelet – Large Margin Classifiers. Proc. Computer Analysis of Images and Patterns (CAIP'03). Springer. 2756 588–595
Neumann, J, Schnörr, C and Steidl, G (2005). Combined SVM-based Feature Selection and Classification. Machine Learning. 61 129-150
Neumann, J, Schnörr, C and Steidl, G (2005). Efficient Wavelet Adaption for Hybrid Wavelet-Large Margin Classifiers. Pattern Recognition. 38 1815-1830
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
Neumann, J, Schnörr, C and Steidl, G (2003). Effectively Finding The Optimal Wavelet For Hybrid Wavelet - Large Margin Signal Classification. Dept. Math. and Comp. Science, University of Mannheim, Germany
Nguyen, M Hoai, Torresani, L, De La Torre, F and Rother, C (2009). Weakly supervised discriminative localization and classification: A joint learning process. Proceedings of the IEEE International Conference on Computer Vision. 1925–1932
Nguyen, M Hoai, Torresani, L, De La Torre, F and Rother, C (2009). Weakly supervised discriminative localization and classification: A joint learning process. Proceedings of the IEEE International Conference on Computer Vision. 1925–1932
Nickisch, H, Rother, C, Kohli, P and Rhemann, C (2010). Learning an Interactive Segmentation System - Supplemental Material
Nicola, A, Petra, S, Popa, C and Schnörr, C (2009). On A General Extending And Constraining Procedure For Linear Iterative Methods. IWR, University of Heidelberg. http://www.ub.uni-heidelberg.de/archiv/9761PDF icon Technical Report (799.47 KB)
Nicola, A, Petra, S, Popa, C and Schnörr, C (2011). A general extending and constraining procedure for linear iterative methods. Int.~J.~Comp.~Math. http://dx.doi.org/10.1080/00207160.2011.634002PDF icon Technical Report (633.79 KB)
Nicola, A, Petra, S, Popa, C and Schnörr, C (2009). On A General Extending And Constraining Procedure For Linear Iterative Methods. IWR, University of Heidelberg. http://www.ub.uni-heidelberg.de/archiv/9761
Nicola, A, Petra, S, Popa, C and Schnörr, C (2011). A general extending and constraining procedure for linear iterative methods. Int. J. Comp. Math. http://dx.doi.org/10.1080/00207160.2011.634002
Niegel, D (2010). Messung Konvektionsgetriebener Transfergeschwindigkeit Von Sauerstoff An Der Luft-Wasser-Grenzfläche. Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg
Nielsen, R (2004). Gasaustausch - Entwicklung und Ergebnis eines schnellen Massenbilanzverfahrens zur Messung der Austauschparameter. Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg. http://www.ub.uni-heidelberg.de/archiv/5032
Noffz, K - H, Lay, R, Männer, R, Jähne, B, Jähne, B, Geißler, P and Haußecker, H (1999). Field Programmable Gate Array image processing. Handbook of Computer Vision and Applications. Academic Press. 3: Systems and Applications
Nowozin, S and Sharp, T (2011). Supplementary Material : Decision Tree Fields. Iccv
Nowozin, S, Rother, C, Bagon, S, Sharp, T, Yao, B and Kohli, P (2011). Decision tree fields. Proceedings of the IEEE International Conference on Computer Vision. 1668–1675
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Ommer, B (2013). The Role of Shape in Visual Recognition. Shape Perception in Human Computer Vision: An Interdisciplinary Perspective. Springer. 373--385PDF icon Technical Report (8.18 MB)
Ommer, B and Buhmann, J M (2010). Learning the Compositional Nature of Visual Object Categories for Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence. IEEE. 32 501--516PDF icon Technical Report (2.78 MB)
Ommer, B, Mader, T and Buhmann, J M (2009). Seeing the Objects Behind the Dots: Recognition in Videos from a Moving Camera. International Journal of Computer Vision. Springer. 83 57--71PDF icon Technical Report (9.61 MB)
Ommer, B and Malik, J (2009). Multi-scale Object Detection by Clustering Lines. Proceedings of the IEEE International Conference on Computer Vision. IEEE. 484--491PDF icon Technical Report (3.18 MB)
Ommer, B (2008). Seeing The Objects Behind The Parts: Learning Compositional Models For Visual Recognition. VDM Verlag. http://www.amazon.com/Seeing-Objects-Behind-Parts-Compositional/dp/3639021444/ref=sr_1_1?ie=UTF8&s=books&qid=1232659136&sr=1-1
Ommer, B and Buhmann, J M (2007). Compositional Object Recognition, Segmentation, and Tracking in Video. Proceedings of the International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition. Springer. 4679 318--333PDF icon Technical Report (2.78 MB)
Ommer, B and Buhmann, J M (2007). Learning the Compositional Nature of Visual Objects. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. IEEE. 1--8PDF icon Technical Report (2.78 MB)
Ommer, B, Sauter, M and M., B J (2006). Learning Top-Down Grouping of Compositional Hierarchies for Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Workshop on Perceptual Organization in Computer Vision. IEEE. 194--194PDF icon Technical Report (358.98 KB)
Ommer, B and Buhmann, J M (2006). Learning Compositional Categorization Models. Proceedings of the European Conference on Computer Vision. Springer. 3953 316--329PDF icon Technical Report (1.35 MB)
Ommer, B and Buhmann, J M (2005). Object Categorization by Compositional Graphical Models. Proceedings of the International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition. Springer. 3757 235--250PDF icon Technical Report (2.07 MB)
Ommer, B and Buhmann, J M (2003). A Compositionality Architecture for Perceptual Feature Grouping. Proceedings of the International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition. Springer. 2683 275--290PDF icon Technical Report (2.89 MB)
Ozlu, N, Monigatti, F, Renard, B Y, Field, C M, Steen, H, Mitchison, T J and Steen, J J (2009). Binding partner switching on microtubules and aurora-B in the mitosis to cytokinesis transition. Molecular & Cellular Proteomics

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