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D. Breitenreicher and Schnörr, C., “Robust 3D object registration without explicit correspondence using geometric integration”, Machine Vision and Applications, vol. 21, pp. 601-611, 2010.

A. Vianello, “Robust 3D Surface Reconstruction from Light Fields”, vol. Dissertation. IWR, Univ. Heidelberg, 2017.

A. Vianello, Ackermann, J., Diebold, M., and Jähne, B., “Robust Hough transform based 3D reconstruction from circular light fields”, in Conference on Computer Vision and Pattern Recognition (CVPR), 2018.

B. Y. Renard, “Robust Methods for the Proteomic Data Analysis Pipeline”. University of Heidelberg, 2010.

B. Antic and Ommer, B., “Robust Multiple-Instance Learning with Superbags”, in Proceedings of the Aian Conference on Computer Vision (ACCV) (Oral), 2012, p. 242--255.

Technical Report (319.58 KB)

T. König, Menze, B. H., Kirchner, M., Monigatti, F., Parker, K. C., Patterson, T., Steen, J. J., Hamprecht, F. A., and Steen, H., “Robust Prediction of the MASCOT Score for an Improved Quality Assessment in Mass Spectrometric Proteomics”, Journal of Proteome Research, vol. 7, pp. 3708-3717, 2008.

Technical Report (1.16 MB)

X. He, Wang, H., Zhang, F., Wang, G., and Zhou, K., “Robust Simulation of Small-Scale Thin Features in SPH-based Free Surface Flows”, Life.Kunzhou.Net, vol. 1, pp. 1–8, 2014.

J. Li, “Robust Single Object Tracking via Fully Convolutional Siamese Networks”, Heidelberg University, 2019.

F. Hering, Merle, M., Wierzimok, D., and Jähne, B., “A robust technique for tracking particles over long image sequences”, in Proc. ISPRS Intercommission Workshop `From Pixels to Sequences', Zurich, March 22 - 24, 1995, In Int'l Arch. of Photog. and Rem. Sens., 1995, vol. XXX-5W1, p. 74--79.

B. Jähne, Haußecker, H., Hering, F., Balschbach, G., Klinke, J., Lell, M., Schmund, D., Schultz, M., Schurr, U., Stitt, M., and Platt, U., “The role of active vision in exploring growth, transport, and exchange processes”, in Aktives Sehen in technischen und biologischen Systemen, Workshop der GI-Fachgruppe 1.0.4. Bildverstehen Hamburg, 3--4. December 1996, 1996, vol. 4, p. 194--202.

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)

N. Kirchgeßner, Spies, H., Scharr, H., and Schurr, U., “Root Growth Analysis in Physiological Coordinates”, in International Conference on Image Analysis and Processing (ICIAP'01), 2001.

N. Kirchgeßner, Spies, H., Scharr, H., and Schurr, U., “Root Growth Measurements in Object Coordinates”, in Proceedings of the 23th DAGM Symposium on Pattern Recognition, 2001.

B. Andres, Köthe, U., Kröger, T., and Hamprecht, F. A., “Runtime-Flexible Multi-dimensional Views and Arrays for C++98 and C++0x”, ArXiv e-prints, 2010.

Technical Report (415.54 KB)

S

K. Roth, Milbich, T., Ommer, B., Cohen, J. Paul, and Ghassemi, M., “S2SD: Simultaneous Similarity-based Self-Distillation for Deep Metric Learning”, Proceedings of International Conference on Machine Learning (ICML). 2021.

M. Haußmann, Hamprecht, F. A., and Kandemir, M., “Sampling-Free Variational Inference of Bayesian Neural Networks by Variance Backpropagation”, UAI. Proceedings. pp. 563-573, 2019.

Technical Report (1.04 MB)

O. Friedrich, Weber, C., Both, M., von Wegner, F., Chamberlain, J. S., Garbe, C. S., and Fink, R. H. A., “Sarcomere Structure and Motor-Protein Function in an Animal Model of Duchenne Muscular Dystrophy (mdx mouse)”, in 87th Annual Meeting of the German Physiological Society, 2008.

C. Haubold, “Scalable Inference for Multi-Target Tracking on Proliferating Cells”. University of Heidelberg, 2017.

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

M. Geese, Ruhnau, P., and Jähne, B., “Scene based maximum likelihood PRNU and DSNU non uniformity correction”, in Forum Bildverarbeitung, 2012, p. 71--82.

A. Mansfield, Gehler, P., Van Gool, L., and Rother, C., “Scene carving: Scene consistent image retargeting”, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2010, vol. 6311 LNCS, pp. 143–156.

K. E. Richter, Rocholz, R., and Jähne, B., “The Schmidt Number Dependency of Air-Sea Gas Exchange with Varying Surfactant Coverage”, in SOLAS Open Science Conference, Washington State, USA, 2012.

L. Görlitz, Singh, M., and Schützbach, P., “Schnelle 3D-Vermessung von Partikeln in Rasterelektronenmiskroskopen mit Hilfe eines Rücksteuerdetektors”. 2007.

J. Berger, Neufeld, A., Becker, F., Lenzen, F., and Schnörr, C., “Second Order Minimum Energy Filtering on SE(3) with Nonlinear Measurement Equations”, in Scale Space and Variational Methods in Computer Vision (SSVM 2015), 2015.

Technical Report (364.01 KB)

J. Berger, Neufeld, A., Becker, F., Lenzen, F., and Schnörr, C., “Second Order Minimum Energy Filtering on SE(3) with Nonlinear Measurement Equations”, in Scale Space and Variational Methods in Computer Vision (SSVM 2015), 2015.

J. Berger, Lenzen, F., Becker, F., Neufeld, A., and Schnörr, C., “{Second-Order Recursive Filtering on the Rigid-Motion Lie Group SE(3) Based on Nonlinear Observations”, J. Math. Imag. Vision, vol. 58, pp. 102–129, 2017.

J. Berger, Lenzen, F., Becker, F., Neufeld, A., and Schnörr, C., “Second-Order Recursive Filtering on the Rigid-Motion Lie Group SE(3) Based on Nonlinear Observations”. 2015.

J. Berger, Lenzen, F., Becker, F., Neufeld, A., and Schnörr, C., “Second-Order Recursive Filtering on the Rigid-Motion Lie Group SE(3) Based on Nonlinear Observations”. 2015.

Technical Report (4.42 MB)

C. N. Straehle, Köthe, U., Briggman, K., Denk, W., and Hamprecht, F. A., “Seeded watershed cut uncertainty estimators for guided interactive segmentation”, in CVPR 2012. Proceedings, 2012, pp. 765 - 772.

Technical Report (2.84 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, Seeing the Objects Behind the Parts: Learning Compositional Models for Visual Recognition. VDM Verlag, 2008.

L. Kostrykin, Schnörr, C., and Rohr, K., “Segmentation of Cell Nuclei Using Intensity-Based Model Fitting and Sequential Convex Programming”, in Proc. ISBI, 2018.

P. Markowsky, Reith, S., Zuber, T. E., König, R., Rohr, K., and Schnörr, C., “Segmentation of cell structure using model-based set covering with iterative reweighting”, in Proc. ISBI, 2017.

B. Andres, Köthe, U., Helmstaedter, M., Denk, W., and Hamprecht, F. A., “Segmentation of SBFSEM Volume Data of Neural Tissue by Hierarchical Classification”, in Pattern Recognition. 30th DAGM Symposium Munich, Germany, June 10-13, 2008. Proceedings, 2008, vol. 5096, pp. 142-152.

Technical Report (1.21 MB)

C. Schnörr, “Segmentation of Visual Motion by Minimizing Convex Non-Quadratic Functionals”, in 12th Int. Conf. on Pattern Recognition, Jerusalem, Israel, 1994.

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