Publications

Export 1925 results:
Author [ Title(Desc)] Type Year
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z 
N
Sprengel, R and Schnörr, C (1993). Nichtlineare Diffusion zur Integration visueller Daten - Anwendung auf Kernspintomogramme. Mustererkennung 1993, 15. DAGM-Symposium. Springer Verlag. 134–141
Renard, B Y, Kirchner, M, Steen, H, Steen, J A J and Hamprecht, F A (2008). NITPICK: Peak Identification for Mass Spectrometry Data. BMC Bioinformatics. 9 355PDF icon Technical Report (643.89 KB)
Jähne, B and Schwarzbauer, M (2016). Noise equalisation and quasi loss-less image data compression – or how many bits needs an image sensor?. tm – Technisches Messen. 83 16–24
Zisler, M, Kappes, J H, Schnörr, C, Petra, S and Schnörr, C (2016). Non-Binary Discrete Tomography by Continuous Non-Convex Optimization. IEEE Comp. Imaging. 2 335-347
Garbe, C S, Krajsek, K, Pavlov, P, Andres, B, Mühlich, M, Stuke, I, Mota, C, Böhme, M, Haker, M, Schuchert, T, Scharr, H, Aach, T and Barth, E (2008). Nonlinear Analysis of Multi-Dimensional Signals. Mathematical Methods in Signal Processing and Digital Image Analysis. Springer. 231-288PDF icon Technical Report (7.11 MB)
Garbe, C S, Krajsek, K, Pavlov, P, Andres, B, Mühlich, M, Stuke, I, Mota, C, Böhme, M, Haker, M, Schucher, T, Scharr, H, Aach, T and Barth, E (2008). Nonlinear analysis of multi-dimensional signals: local adaptive estimation of complex motion and orientation patterns. Mathematical Methods in Time Series Analysis and Digital Image Processing. Springer. 231-288
Schnörr, C and Sprengel, R (1994). A Nonlinear Regularization Approach to Early Vision. Biol. Cybernetics. 72 141–149
Cremers, D, Kohlberger, T and Schnörr, C (2002). Nonlinear Shape Statistics in Mumford-Shah Based Segmentation. Computer Vision -- ECCV 2002). Springer Verlag. 2351 93--108PDF icon Technical Report (636.58 KB)
Cremers, D, Kohlberger, T and Schnörr, C (2002). Nonlinear Shape Statistics in Mumford-Shah Based Segmentation. Computer Vision – ECCV 2002). Springer Verlag. 2351 93–108
Cremers, D, Kohlberger, T and Schnörr, C (2001). Nonlinear Shape Statistics via Kernel Spaces. Mustererkennung 2001. Springer. 2191 269--276PDF icon Technical Report (324.55 KB)
Cremers, D, Kohlberger, T and Schnörr, C (2001). Nonlinear Shape Statistics via Kernel Spaces. Mustererkennung 2001. Springer, Munich, Germany. 2191 269–276
Sigg, C, Fischer, B, Ommer, B, Roth, V and Buhmann, J M (2007). Nonnegative CCA for Audiovisual Source Separation. International Workshop on Machine Learning for Signal Processing. IEEE. 253--258PDF icon Technical Report (1.27 MB)
Jancsary, J, Nowozin, S and Rother, C (2012). Non-parametric crfs for image labeling. NIPS Workshop Modern Nonparametric Methods in Machine Learning. 1–5. http://www.nowozin.net/sebastian/papers/jancsary2012nonparametriccrf.pdf
Márquez-Neila, P, Kohli, P, Rother, C and Baumela, L (2014). Non-parametric higher-order random fields for image segmentation. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 8694 LNCS 269–284
Restle, J, Hissmann, M and Hamprecht, F A (2004). Nonparametric Smoothing of Height maps using ``Confidence'' values. Optical Engineering. 43 866-871PDF icon Technical Report (1.07 MB)
Peckar, W, Schnörr, C, Rohr, K and Stiehl, H S (1998). Non-Rigid Image Registration Using a Parameter-Free Elastic Model. 9th British Machine Vision Conference (BMVC`98). Southampton/UK. 134–143
Bell, P, Schlecht, J and Ommer, B (2013). Nonverbal Communication in Medieval Illustrations Revisited by Computer Vision and Art History. Visual Resources Journal, Special Issue on Digital Art History. Taylor & Francis. 29 26--37. http://www.tandfonline.com/doi/abs/10.1080/01973762.2013.761111
Esser, P, Rombach, R and Ommer, B (2020). A Note on Data Biases in Generative Models. NeurIPS 2020 Workshop on Machine Learning for Creativity and Design. https://arxiv.org/abs/2012.02516
Mbock, K (2009). A Novel Algorithm For Motion Estimation With Explicit Consideration Of Perturbations. University of Heidelberg
Ravindran, A (2019). Novel Deep Learning-Based Instance Segmentation Using Mutex Watershed For Microscopy Cell Images. Heidelberg University
Schimpf, U, Garbe, C S and Jähne, B (2002). Novel insights into heat transfer across the aqueous boundary layer by infrared imagery and its application to air-sea exchange processes. Proceedings of Eurotherm 71 on Visualization, Imaging and Data Analysis In Convective Heat and Mass Transfer
Stapf, J (2015). Novel learning-based techniques for dense fluid motion measurements. IWR, Fakultät für Physik und Astronomie, Univ.\ Heidelberg. http://www.ub.uni-heidelberg.de/archiv/18116
Kirschbaum, E (2019). Novel Machine Learning Approaches for Neurophysiological Data Analysis. Heidelberg University
Jehle, M and Jähne, B (2006). A novel method for spatio-temporal analysis of flows within the water-side viscous boundary layer. 12th Intern. Symp. on Flow Visualization, Göttingen, 10--14. September 2006
Jehle, M and Jähne, B (2008). A novel method for three-dimensional three-component analysis of flow close to free water surfaces. Exp. Fluids. 44 469--480
Voss, B (2012). Novel Single Camera Techniques for 3D3C Lagrangian Trajectory Measurements in Interfacial Flows. IWR, Fakultät für Physik und Astronomie, Univ.\ Heidelberg. http://www.ub.uni-heidelberg.de/archiv/13362
Voss, B and Garbe, C S (2010). Novel strategy for water sided interfacial 3D3Cflow-visualization using a single camera. 14th International Symposium on Flow Visualization. D1-018
Hering, F, Balschbach, G, Jähne, B and Waldhäusl, P (1996). A novel system for the combined measurement of wave- and flow-fields beneath wind induced water waves. Proc. 18th Int. Congr. for Photogrammetry and Remote Sensing. 31 231--236. http://www.isprs.org/proceedings/XXXI/congress/part5/
Leue, C, Wenig, M, Platt, U, Jähne, B and Haußecker, H (2000). NOX Emissions Retrieved from Satellite Images. Computer Vision and Applications. A Guide for Students and Practitioners. Academic Press. 654--655
Savarino, F, Hühnerbein, R, Aström, F, Recknagel, J and Schnörr, C (2017). Numerical Integration of Riemannian Gradient Flows for Image Labeling. Proc. SSVM. Springer. 10302
Scharr, H, Körkel, S and Jähne, B (1997). Numerische Isotropieoptimierung von FIR-Filtern mittels Querglättung. Proceedings of the 19th DAGM Symposium on Pattern Recognition, Braunschweig. 199--208
Schulzke, E (2001). Numerische Simulation Von Elementaren Kalziumfreisetzungsereignissen Und Photolytische Ca^2+-Freisetzung Aus Käfigmolekülen Mittels Picosekundenlaser. IWR, Fakultät für Physik und Astronomie, Univ.\ Heidelberg
O
Scheuermann, T, Pfundt, G, Eyerer, P and Jähne, B (1995). Oberflächenkonturvermessung mikroskopischer Objekte durch Projektion statistischer Rauschmuster. Proc. 17. DAGM-Symposium Mustererkennung, Bielefeld, 13.-15. September 1995. 319--326
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)
Vicente, S, Rother, C and Kolmogorov, V (2011). Object cosegmentation. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2217–2224

Pages