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D. Cremers, Kohlberger, T., and Schnörr, C., “Nonlinear Shape Statistics via Kernel Spaces”, in Mustererkennung 2001, 2001, vol. 2191, p. 269--276.

Technical Report (324.55 KB)

D. Cremers, Kohlberger, T., and Schnörr, C., “Nonlinear Shape Statistics via Kernel Spaces”, in Mustererkennung 2001, Munich, Germany, 2001, vol. 2191, pp. 269–276.

C. Sigg, Fischer, B., Ommer, B., Roth, V., and Buhmann, J. M., “Nonnegative CCA for Audiovisual Source Separation”, in International Workshop on Machine Learning for Signal Processing, 2007, p. 253--258.

Technical Report (1.27 MB)

J. Jancsary, Nowozin, S., and Rother, C., “Non-parametric crfs for image labeling”, in NIPS Workshop Modern Nonparametric Methods in Machine Learning, 2012, pp. 1–5.

P. Márquez-Neila, Kohli, P., Rother, C., and Baumela, L., “Non-parametric higher-order random fields for image segmentation”, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2014, vol. 8694 LNCS, pp. 269–284.

J. Restle, Hissmann, M., and Hamprecht, F. A., “Nonparametric Smoothing of Height maps using ``Confidence'' values”, Optical Engineering, vol. 43, pp. 866-871, 2004.

Technical Report (1.07 MB)

W. Peckar, Schnörr, C., Rohr, K., and Stiehl, H. S., “Non-Rigid Image Registration Using a Parameter-Free Elastic Model”, in 9th British Machine Vision Conference (BMVC`98), Southampton/UK, 1998, pp. 134–143.

P. Bell, Schlecht, J., and Ommer, B., “Nonverbal Communication in Medieval Illustrations Revisited by Computer Vision and Art History”, Visual Resources Journal, Special Issue on Digital Art History, vol. 29, p. 26--37, 2013.

P. Esser, Rombach, R., and Ommer, B., “A Note on Data Biases in Generative Models”, in NeurIPS 2020 Workshop on Machine Learning for Creativity and Design, 2020.

K. Mbock, “A Novel Algorithm for Motion Estimation with Explicit Consideration of Perturbations”, University of Heidelberg, 2009.

A. Ravindran, “Novel Deep Learning-based Instance Segmentation Using Mutex Watershed for Microscopy Cell Images”, Heidelberg University, 2019.

U. Schimpf, Garbe, C. S., and Jähne, B., “Novel insights into heat transfer across the aqueous boundary layer by infrared imagery and its application to air-sea exchange processes”, in Proceedings of Eurotherm 71 on Visualization, Imaging and Data Analysis In Convective Heat and Mass Transfer, 2002.

J. Stapf, “Novel learning-based techniques for dense fluid motion measurements”. IWR, Fakultät für Physik und Astronomie, Univ.\ Heidelberg, 2015.

E. Kirschbaum, “Novel Machine Learning Approaches for Neurophysiological Data Analysis”. Heidelberg University, 2019.

M. Jehle and Jähne, B., “A novel method for spatio-temporal analysis of flows within the water-side viscous boundary layer”, in 12th Intern. Symp. on Flow Visualization, Göttingen, 10--14. September 2006, 2006.

M. Jehle and Jähne, B., “A novel method for three-dimensional three-component analysis of flow close to free water surfaces”, Exp. Fluids, vol. 44, p. 469--480, 2008.

B. Voss, “Novel Single Camera Techniques for 3D3C Lagrangian Trajectory Measurements in Interfacial Flows”. IWR, Fakultät für Physik und Astronomie, Univ.\ Heidelberg, 2012.

B. Voss and Garbe, C. S., “Novel strategy for water sided interfacial 3D3Cflow-visualization using a single camera”, in 14th International Symposium on Flow Visualization, 2010, pp. D1-018.

F. Hering, Balschbach, G., Jähne, B., and Waldhäusl, P., “A novel system for the combined measurement of wave- and flow-fields beneath wind induced water waves”, in Proc. 18th Int. Congr. for Photogrammetry and Remote Sensing, 1996, vol. 31, p. 231--236.

C. Leue, Wenig, M., Platt, U., Jähne, B., and Haußecker, H., “NOX Emissions Retrieved from Satellite Images”, Computer Vision and Applications. A Guide for Students and Practitioners. Academic Press, p. 654--655, 2000.

F. Savarino, Hühnerbein, R., Aström, F., Recknagel, J., and Schnörr, C., “Numerical Integration of Riemannian Gradient Flows for Image Labeling”, in Proc. SSVM, 2017, vol. 10302.

H. Scharr, Körkel, S., and Jähne, B., “Numerische Isotropieoptimierung von FIR-Filtern mittels Querglättung”, in Proceedings of the 19th DAGM Symposium on Pattern Recognition, Braunschweig, 1997, p. 199--208.

E. Schulzke, “Numerische Simulation von elementaren Kalziumfreisetzungsereignissen und photolytische Ca^2+-Freisetzung aus Käfigmolekülen mittels Picosekundenlaser”, IWR, Fakultät für Physik und Astronomie, Univ.\ Heidelberg, 2001.

O

T. Scheuermann, Pfundt, G., Eyerer, P., and Jähne, B., “Oberflächenkonturvermessung mikroskopischer Objekte durch Projektion statistischer Rauschmuster”, in Proc. 17. DAGM-Symposium Mustererkennung, Bielefeld, 13.-15. September 1995, 1995, p. 319--326.

B. Ommer and Buhmann, J. M., “Object Categorization by Compositional Graphical Models”, in Proceedings of the International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition, 2005, vol. 3757, p. 235--250.

Technical Report (2.07 MB)

S. Vicente, Rother, C., and Kolmogorov, V., “Object cosegmentation”, in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2011, pp. 2217–2224.

S. Zheng, Prisacariu, V. Adrian, Averkiou, M., Cheng, M. Ming, Mitra, N. J., Shotton, J., Torr, P. H. S., and Rother, C., “Object proposals estimation in depth image using compact 3D shape manifolds”, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2015, vol. 9358, pp. 196–208.

N. Ufer, Lang, S., and Ommer, B., “Object Retrieval and Localization in Large Art Collections Using Deep Multi-style Feature Fusion and Iterative Voting”, IEEE European Conference on Computer Vision (ECCV), VISART Workshop . 2020.

Paper (1.03 MB)

B. Schmitzer and Schnörr, C., “Object Segmentation by Shape Matching with Wasserstein Modes”, in Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR 2013), 2013, pp. 123-136.

M. Bleyer, Rother, C., Kohli, P., Scharstein, D., and Sinha, S., “Object stereo Joint stereo matching and object segmentation”, in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2011, pp. 3081–3088.

V. Ulman, Maška, M., Magnusson, K. E. G., Ronneberger, O., Haubold, C., Harder, N., Matula, P., Matula, P., Svoboda, D., Radojevic, M., Smal, I., Rohr, K., Jaldén, J., Blau, H. M., Dzyubachyk, O., Lelieveldt, B., Xiao, P., Li, Y., Cho, S. - Y., Dufour, A., Olivo-Marin, J. C., Reyes-Aldasoro, C. C., Solis-Lemus, J. A., Bensch, R., Brox, T., Stegmaier, J., Mikut, R., Wolf, S., Hamprecht, F. A., Esteves, T., Quelhas, P., Demirel, Ö., Malström, L., Jug, F., Tomančák, P., Meijering, E., Muñoz-Barrutia, A., Kozubek, M., and Ortiz-de-Solorzano, C., “An Objective Comparison of Cell Tracking Algorithms”, Nature Methods, vol. 14, no. 12, pp. 1141-1152, 2017.

Technical Report (4.24 MB)

F. O. Kaster, Kassemeyer, S., Merkel, B., Nix, O., and Hamprecht, F. A., “An object-oriented library for systematic training and comparison of classifiers for computer-assisted tumor diagnosis from MRSI measurements”, in Bildverarbeitung für die Medizin 2010 -- Algorithmen, Systeme, Anwendungen, 2010, pp. 97-101.

Technical Report (1.12 MB)

F. O. Kaster, Merkel, B., Nix, O., and Hamprecht, F. A., “An object-oriented library for systematic training and comparison of classifiers for computer-assisted tumor diagnosis from MRSI measurements”, Computer Science - Research and Development, vol. 26, pp. 65-85, 2011.

Technical Report (808.16 KB)

S. Mann, “Objektbasierte Bildfolgenanalyse zur Bewegungsbestimmung im in vitro Motility Assay unter Verwendung eines Strukturtensorverfahrens”, IWR, Fakultät für Physik und Astronomie, Univ.\ Heidelberg, 1998.

B. Jähne, “Objektive Kriterien unterstützen die anwendungsorientierte Auswahl einer Kamera”. 2009.

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