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J. Neumann, Schnörr, C., and Steidl, G., “SVM-based Feature Selection by Direct Objective Minimisation”, in Pattern Recognition, Proc. 26th DAGM Symposium, 2004, vol. 3175, pp. 212-219.

J. Neumann, Schnörr, C., and Steidl, G., “Effectively Finding the Optimal Wavelet for Hybrid Wavelet - Large Margin Signal Classification”, Dept. Math. and Comp. Science, University of Mannheim, Germany, 5, 2003.

M. Hoai Nguyen, Torresani, L., De La Torre, F., and Rother, C., “Weakly supervised discriminative localization and classification: A joint learning process”, in Proceedings of the IEEE International Conference on Computer Vision, 2009, pp. 1925–1932.

M. Hoai Nguyen, Torresani, L., De La Torre, F., and Rother, C., “Weakly supervised discriminative localization and classification: A joint learning process”, in Proceedings of the IEEE International Conference on Computer Vision, 2009, pp. 1925–1932.

H. Nickisch, Rother, C., Kohli, P., and Rhemann, C., “Learning an Interactive Segmentation System - Supplemental Material”, 2010.

A. Nicola, Petra, S., Popa, C., and Schnörr, C., “On a general extending and constraining procedure for linear iterative methods”, IWR, University of Heidelberg, 2009.

Technical Report (799.47 KB)

A. Nicola, Petra, S., Popa, C., and Schnörr, C., “A general extending and constraining procedure for linear iterative methods”, Int.~J.~Comp.~Math., 2011.

Technical Report (633.79 KB)

A. Nicola, Petra, S., Popa, C., and Schnörr, C., “On a general extending and constraining procedure for linear iterative methods”, IWR, University of Heidelberg, 2009.

A. Nicola, Petra, S., Popa, C., and Schnörr, C., “A general extending and constraining procedure for linear iterative methods”, Int. J. Comp. Math., 2011.

D. Niegel, “Messung konvektionsgetriebener Transfergeschwindigkeit von Sauerstoff an der Luft-Wasser-Grenzfläche”, Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg, 2010.

R. Nielsen, “Gasaustausch - Entwicklung und Ergebnis eines schnellen Massenbilanzverfahrens zur Messung der Austauschparameter”. Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg, 2004.

K. - H. Noffz, Lay, R., Männer, R., Jähne, B., Jähne, B., Geißler, P., and Haußecker, H., “Field Programmable Gate Array image processing”, Handbook of Computer Vision and Applications, vol. 3: Systems and Applications. Academic Press, 1999.

S. Nowozin and Sharp, T., “Supplementary Material : Decision Tree Fields”, Iccv, 2011.

S. Nowozin, Rother, C., Bagon, S., Sharp, T., Yao, B., and Kohli, P., “Decision tree fields”, in Proceedings of the IEEE International Conference on Computer Vision, 2011, pp. 1668–1675.

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B. Ommer, “The Role of Shape in Visual Recognition”, in Shape Perception in Human Computer Vision: An Interdisciplinary Perspective, Springer, 2013, p. 373--385.

Technical Report (8.18 MB)

B. Ommer and Buhmann, J. M., “Learning the Compositional Nature of Visual Object Categories for Recognition”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, p. 501--516, 2010.

Technical Report (2.78 MB)

B. Ommer, Mader, T., and Buhmann, J. M., “Seeing the Objects Behind the Dots: Recognition in Videos from a Moving Camera”, International Journal of Computer Vision, vol. 83, p. 57--71, 2009.

Technical Report (9.61 MB)

B. Ommer and Malik, J., “Multi-scale Object Detection by Clustering Lines”, in Proceedings of the IEEE International Conference on Computer Vision, 2009, p. 484--491.

Technical Report (3.18 MB)

B. Ommer, Seeing the Objects Behind the Parts: Learning Compositional Models for Visual Recognition. VDM Verlag, 2008.

B. Ommer and Buhmann, J. M., “Compositional Object Recognition, Segmentation, and Tracking in Video”, in Proceedings of the International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition, 2007, vol. 4679, p. 318--333.

Technical Report (2.78 MB)

B. Ommer and Buhmann, J. M., “Learning the Compositional Nature of Visual Objects”, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2007, p. 1--8.

Technical Report (2.78 MB)

B. Ommer, Sauter, M., and M., B. J., “Learning Top-Down Grouping of Compositional Hierarchies for Recognition”, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Workshop on Perceptual Organization in Computer Vision, 2006, p. 194--194.

Technical Report (358.98 KB)

B. Ommer and Buhmann, J. M., “Learning Compositional Categorization Models”, in Proceedings of the European Conference on Computer Vision, 2006, vol. 3953, p. 316--329.

Technical Report (1.35 MB)

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)

B. Ommer and Buhmann, J. M., “A Compositionality Architecture for Perceptual Feature Grouping”, in Proceedings of the International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition, 2003, vol. 2683, p. 275--290.

Technical Report (2.89 MB)

N. Ozlu, Monigatti, F., Renard, B. Y., Field, C. M., Steen, H., Mitchison, T. J., and Steen, J. J., “Binding partner switching on microtubules and aurora-B in the mitosis to cytokinesis transition”, Molecular & Cellular Proteomics, 2009.

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N. Pandey, “Weakly Supervised Semantic Segmentation”, Heidelberg University, 2019.

C. Pape, “Automatic Segmentation of Neurites from Anisotropic EM-Imaging”, University of Heidelberg, 2016.

C. Pape, Beier, T., Li, P., Jain, V., Brock, D. D., and Kreshuk, A., “Solving Large Multicut Problems for Connectomics via Domain Decomposition”, Bioimage Computing Workshop. ICCV. pp. 1-10, 2017.

C. Pape, “Scalable Instance Segmentation for Microscopy”. Heidelberg University, 2021.

C. Pape, Remme, R., Wolny, A., Olberg, S., Wolf, S., Cerrone, L., Cortese, M., Klaus, S., Lucic, B., Ullrich, S., Anders-Össwein, M., Wolf, S., Cerikan, B., Neufeldt, C. J., Ganter, M., Schnitzler, P., Merle, U., Lusic, M., Boulant, S., Stanifer, M., Bartenschlager, R., Hamprecht, F. A., Kreshuk, A., Tischer, C., Kräusslich, H. - G., Müller, B., and Laketa, V., “Microscopy-based assay for semi-quantitative detection of SARS-CoV-2 specific antibodies in human sera”, BioEssays, vol. 43, no. 3, 2021.

M. Papst, “Development of a method for quantitative imaging of air-water gas exchange”, Institut für Umweltphysik, Universität Heidelberg, Germany, 2019.

N. Paragios, Faugeras, O., Chan, T., and Schnörr, C., Eds., “Variational, Geometric and Level Sets in Computer Vision (VLSM'05)”, lncs, vol. 3752. Springer, Beijing, China, 2005.

P. Pavlov, “Analysis of Motion in Scale Space”. IWR, Fakultät für Mathematik und Informatik, Univ.\ Heidelberg, 2008.

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.

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