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Breitenreicher, D and Schnörr, C (2010). Robust 3D object registration without explicit correspondence using geometric integration. Machine Vision and Applications. 21 601-611. http://www.springerlink.com/content/g20710062l014241/

Vianello, A (2017). Robust 3D Surface Reconstruction from Light Fields. IWR, Univ. Heidelberg. Dissertation

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

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

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

Technical Report (319.58 KB)

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

Technical Report (1.16 MB)

He, X, Wang, H, Zhang, F, Wang, G and Zhou, K (2014). Robust Simulation of Small-Scale Thin Features in SPH-based Free Surface Flows. Life.Kunzhou.Net. 1 1–8. http://doi.acm.org/10.1145/XXXXXXX.YYYYYYY http://life.kunzhou.net/2013/SPHsurfacetension.pdf

Li, J (2019). Robust Single Object Tracking Via Fully Convolutional Siamese Networks. Heidelberg University

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

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

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

Technical Report (8.18 MB)

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

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

Andres, B, Köthe, U, Kröger, T and Hamprecht, F A (2010). Runtime-Flexible Multi-dimensional Views and Arrays for C++98 and C++0x. ArXiv e-prints. http://arxiv.org/abs/1008.2909v1

Technical Report (415.54 KB)

S

Roth, K, Milbich, T, Ommer, B, Cohen, J Paul and Ghassemi, M (2021). S2SD: Simultaneous Similarity-based Self-Distillation for Deep Metric Learning. Proceedings of International Conference on Machine Learning (ICML). https://arxiv.org/abs/2009.08348

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

Technical Report (1.04 MB)

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

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

Pape, C (2021). Scalable Instance Segmentation for Microscopy. Heidelberg University

Geese, M, Ruhnau, P and Jähne, B (2012). Scene based maximum likelihood PRNU and DSNU non uniformity correction. Forum Bildverarbeitung. KIT Scientific Publishing. 71--82. http://digbib.ubka.uni-karlsruhe.de/volltexte/1000030440

Mansfield, A, Gehler, P, Van Gool, L and Rother, C (2010). Scene carving: Scene consistent image retargeting. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 6311 LNCS 143–156. www.fujifilm.com/products/3d/camera/finepix_

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

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

Berger, J, Neufeld, A, Becker, F, Lenzen, F and Schnörr, C (2015). Second Order Minimum Energy Filtering on SE(3) with Nonlinear Measurement Equations. Scale Space and Variational Methods in Computer Vision (SSVM 2015). Springer International Publishing. http://dx.doi.org/10.1007/978-3-319-18461-6_32

Technical Report (364.01 KB)

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

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

Berger, J, Lenzen, F, Becker, F, Neufeld, A and Schnörr, C (2015). Second-Order Recursive Filtering on the Rigid-Motion Lie Group SE(3) Based on Nonlinear Observations. http://arxiv.org/abs/1507.06810

Berger, J, Lenzen, F, Becker, F, Neufeld, A and Schnörr, C (2015). Second-Order Recursive Filtering on the Rigid-Motion Lie Group SE(3) Based on Nonlinear Observations. http://arxiv.org/abs/1507.06810

Technical Report (4.42 MB)

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

Technical Report (2.84 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--71

Technical Report (9.61 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

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

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

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

Technical Report (1.21 MB)

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

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