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T
Berthe, A, Kondermann, D, Christensen, C, Goubergrits, L, Garbe, C S, Affeld, K and Kertzscher, U (2010). Three-dimensional, three-component wall-PIV. Exp. Fluids. 48 online
Goldlücke, B, Strekalovskiy, E and Cremers, D (2013). Tight convex relaxations for vector-valued labeling. SIAM Journal on Imaging Sciences
Gottfried, J - M, Nair, R, Meister, S, Garbe, C S and Kondermann, D (2014). Time of flight motion compensation revisited. International Conference on Image Processing, ICIP 2014
Gottfried, J - M, Nair, R, Meister, S, Garbe, C S and Kondermann, D (2014). Time of flight motion compensation revisited. International Conference on Image Processing, ICIP 2014
Gianniotis, N, Tiño, P, Spreckley, S and Raychaudhury, S (2009). Topographic Mapping of Astronomical Light Curves via a Physically Inspired Probabilistic Model. Proceedings of ICANN 2009. 567-576
Haußecker, H, Garbe, C S, Spies, H and Jähne, B (1999). A total least squares framework for low-level analysis of dynamic scenes and processes. Proceedings of the 21th DAGM Symposium on Pattern Recognition. Springer. 240--249
Esposito, M, Hennersperger, C, Göbl, R, Demaret, L, Storath, M, Navab, N, Baust, M and Weinmann, A (2019). Total variation regularization of pose signals with an application to 3D freehand ultrasound. IEEE Transactions on Medical Imaging. 38(10) 2245-2258
Hanselmann, M, Köthe, U, Kirchner, M, Renard, B Y, Amstalden, E R, Glunde, K, Heeren, R M A and Hamprecht, F A (2009). Towards Digital Staining using Imaging Mass Spectrometry and Random Forests. Journal of Proteome Research. 8 3558-3567PDF icon Technical Report (1.47 MB)
Maier-Hein, L, Franz, A M, Fangerau, M, Schmidt, M, Seitel, A, Mersmann, S, Kilgus, T, Groch, A, Yung, K, Santos, T R dos and Meinzer, H - P (2011). Towards mobile augmented reality for on-patient visualization of medical images. Bildverarbeitung für die Medizin (2011). Springer. 389--393
Jähne, B and Garbe, C S (2003). Towards objective performance analysis for estimation of complex motion: analytic motion modeling, filter optimization, and test sequences. In Proceedings of IEEE International Conference on Image Processing. ./pdf/2003/barth_ICIP2003.pdf:PDF
Cremers, D, Sochen, N and Schnörr, C (2003). Towards Recognition-Based Variational Segmentation Using Shape Priors and Dynamic Labeling. Scale Space Methods in Computer Vision. Springer. 2695 388--400PDF icon Technical Report (451.82 KB)
Cremers, D, Sochen, N and Schnörr, C (2003). Towards Recognition-Based Variational Segmentation Using Shape Priors and Dynamic Labeling. Scale Space Methods in Computer Vision. Springer. 2695 388–400
Fiaschi, L, Diego, F, Grosser, K - H, Schiegg, M, Köthe, U, Zlatic, M and Hamprecht, F A (2014). Tracking indistinguishable translucent objects over time using weakly supervised structured learning. CVPR. Proceedings. 2736 - 2743PDF icon Technical Report (1.47 MB)
Jähne, (2007). Transport At The Air Sea Interface --- Measurements, Models And Parameterizations. Springer. http://hci.iwr.uni-heidelberg.de/publications/dip/2007/TASI/index.html
Garbe, C S, Flatow, F, Klinger, M, Schepanski, K, Tegen, I and Rannacher, R (2009). Transport of dust across the Sahara from satellite image sequence analysis. Eos Transactions. 90 EP21A-0565
Garbe, C S, Flatow, F, Klinger, M, Schepanski, K, Tegen, I and Rannacher, R (2009). Transport of Sahara dust into the Atlantic Ocean from satellite image sequence analysis. SOLAS Open Science Conference. 27
Glocker, B, T. Heibel, H, Navab, N, Kohli, P and Rother, C (2010). TriangleFlow: Optical flow with triangulation-based higher-order likelihoods. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 6313 LNCS 272–285. http://vision.middlebury.edu/flow/
Hader, S and Hamprecht, F A (2004). Two-Stage Classification with Automatic Feature Selection for an Industrial Application. Classification, the ubiquitous challenge: Proceedings of GfKl 2004. Springer. 137-144PDF icon Technical Report (518.16 KB)
U
Brachmann, E, Michel, F, Krull, A, Yang, M Ying, Gumhold, S and Rother, C (2016). Uncertainty-Driven 6D Pose Estimation of Objects and Scenes from a Single RGB Image. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2016-Decem 3364–3372
Brachmann, E, Michel, F, Krull, A, Yang, M Ying, Gumhold, S and Rother, C (2016). Uncertainty-Driven 6D Pose Estimation of Objects and Scenes from a Single RGB Image. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2016-Decem 3364–3372
Richmond, D, Kainmueller, D, Glocker, B, Rother, C and Myers, G (2015). Uncertainty-driven forest predictors for vertebra localization and segmentation. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 9349 653–660
Milbich, T, Ghori, O and Ommer, B (2020). Unsupervised Representation Learning by Discovering Reliable Image Relations. Pattern Recognition. 102. http://arxiv.org/abs/1911.07808
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Ruhnau, P, Gütter, C, Putze, T and Schnörr, C (2005). A variational approach for particle tracking velocimetry. Meas. Science and Techn. 16 1449-1458
Telea, A, Preußer, T, Garbe, C S, Droske, M and Rumpf, M (2006). A variational approach to joint denoising, edge detection and motion estimation. Proceedings of the 28th DAGM Symposium on Pattern Recognition. 525--535. http://numod.ins.uni-bonn.de/research/papers/public/PrDrGa06.pdf
Kohlberger, T, Mémin, E and Schnörr, C (2003). Variational Dense Motion Estimation Using the Helmholtz Decomposition. Scale Space Methods in Computer Vision. Springer. 2695 432–448
Wanner, S and Goldlücke, B (2014). Variational light field analysis for disparity estimation and super-resolution. IEEE Trans. Pattern Analysis Machine Intelligence. 36 606--619
Schnörr, (1999). Variational Methods for Adaptive Image Smoothing and Segmentation. Handbook on Computer Vision and Applications: Signal Processing and Pattern Recognition. Academic Press, San Diego. 2 451–484
Goldlücke, B and Wanner, S (2013). The Variational Structure of Disparity and Regularization of 4D Light Fields. IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Strobel, J, Görlitz, L and Staudacher, M (2005). Verfahren und Prüfkörper zur Bestimmung der Reinigungswirkung in einem Ultraschallbild
Gosch, C, Fundana, K, Heyden, A and Schnörr, C (2008). View Point Tracking of Rigid Object Based on Shape Sub-Manifolds. Computer Vision -- ECCV 2008. Springer. 5302 251--263PDF icon Technical Report (523.17 KB)
Gosch, C, Fundana, K, Heyden, A and Schnörr, C (2008). View Point Tracking of Rigid Object Based on Shape Sub-Manifolds. Computer Vision – ECCV 2008. Springer. 5302 251–263
Mansfield, A, Gehler, P, Van Gool, L and Rother, C (2012). Visibility maps for improving seam carving. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 6554 LNCS 131–144. http://www.adobe.com/products/photoshop/photoshopextended/features/
Falkenroth, A, Degreif, K, Jähne, B and Jähne, B (2007). Visualisation of oxygen concentration fields in the mass boundary layer by fluorescence quenching. Transport at the Air Sea Interface --- Measurements, Models and Parameterizations. Springer Verlag
Gianniotis, N and Tiño, P (2009). Visualization of Structured Data via Generative Probabilistic Modeling. Pattern Recognition. Springer. 5400 118-137
Garrido, Q, Damrich, S, Jäger, A, Cerletti, D, Claassen, M, Najman, L and Hamprecht, F A (2022). Visualizing hierarchies in scRNA-seq data using a density tree-biased autoencoder. Bioinformatics. arXiv preprint. 38 (Suppl 1) i316-i324

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