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W

Márquez-Valle, P, Gil, D, Hernàndez-Sabaté, A and Kondermann, D (2013). When is a confidence measure good enough?. submitted to CVPR 2013

V

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

Gianniotis, N and Tiño, P (2009). Visualization of Structured Data via Generative Probabilistic Modeling. Pattern Recognition. Springer. 5400 118-137

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

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/

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

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

Technical Report (523.17 KB)

Strobel, J, Görlitz, L and Staudacher, M (2005). Verfahren und Prüfkörper zur Bestimmung der Reinigungswirkung in einem Ultraschallbild

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)

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

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

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

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

Ruhnau, P, Gütter, C, Putze, T and Schnörr, C (2005). A variational approach for particle tracking velocimetry. Meas. Science and Techn. 16 1449-1458

U

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

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

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

T

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. 137-144

Technical Report (518.16 KB)

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/

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

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

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

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 - 2743

Technical Report (1.47 MB)

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

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

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

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

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 3558-3567

Technical Report (1.47 MB)

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

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

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

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

Goldlücke, B, Strekalovskiy, E and Cremers, D (2013). Tight convex relaxations for vector-valued labeling. SIAM Journal on Imaging Sciences

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