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Mackowiak, R, Lenz, P, Ghori, O, Diego, F, Lange, O and Rother, C (2019). CEREALS - Cost-Effective REgion-based Active Learning for Semantic Segmentation. British Machine Vision Conference 2018, BMVC 2018

Milbich, T, Roth, K, Sinha, S, Schmidt, L, Ghassemi, M and Ommer, B (2021). Characterizing Generalization under Out-Of-Distribution Shifts in Deep Metric Learning. https://arxiv.org/abs/2107.09562

Erz, M (2011). Charakterisierung von Laufzeitkamerasystemen für Lumineszenzlebensdauermessungen. IWR, Fakultät für Physik und Astronomie, Univ. Heidelberg. Dissertation. http://www.ub.uni-heidelberg.de/archiv/11598

Hamprecht, F A, Thiel, W and van Gunsteren, W F (2002). Chemical library subset selection algorithms: a unified derivation using spatial statistics. Journal of Chemical Information and Computer Sciences. 42 414-428

Schmund, D, Münsterer, T, Lauer, H, Jähne, B and Jähne, B (1995). The circular wind wave facilities at the University of Heidelberg. Air-Water Gas Transfer - Selected papers from the Third International Symposium on Air-Water Gas Transfer. AEON. 505--516

Kelm, B Michael, Menze, B H, Neff, T, Zechmann, C M and Hamprecht, F A (2006). CLARET: a tool for fully automated evaluation of MRSI with pattern recognition methods.. Bildverarbeitung für die Medizin 2006 - Algorithmen, Systeme, Anwendungen. Springer. 51-55. http://www.efmi-wg-mip.net/service/bvm2006

Technical Report (275.25 KB)

Heers, J, Schnörr, C and Stiehl, H S (1998). A class of parallel algorithms for nonlinear variational image segmentation. Proc. Noblesse Workshop on Non–Linear Model Based Image Analysis (NMBIA'98). Glasgow, Scotland

Lenzen, F, Becker, F, Lellmann, J, Petra, S and Schnörr, C (2013). A class of quasi-variational inequalities for adaptive image denoising and decomposition. Computational Optimization and Applications. Springer Netherlands. 54 371-398. http://dx.doi.org/10.1007/s10589-012-9456-0

Technical Report (748.66 KB)

Lenzen, F, Becker, F, Lellmann, J, Petra, S and Schnörr, C (2013). A Class of Quasi-Variational Inequalities for Adaptive Image Denoising and Decomposition. Computational Optimization and Applications (COAP). 54 (2) 371-398

Hamprecht, F A (2004). Classification. Practical Handbook on Image Processing for Scientific and Technical Applications. CRC Press. 509-519

Technical Report (320.84 KB)

Menze, B H, Wormit, M, Bachert, P, Lichy, M P, Schlemmer, H - P and Hamprecht, F A (2004). Classification of in vivo magnetic resonance spectra. Classification in ubiquitous challenge: Proceedings of the GfKl 2004. Springer. 362-369

Technical Report (240.1 KB)

Menze, B H and Ur, J A (2007). Classification of multispectral ASTER imagery in the archaeological survey for settlement sites of the Near East. Proc 10th International Symposium on Physical Measurements and Signature in Remote Sensing (ISPMRS 07), Davos, Switzerland. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences

Technical Report (920.71 KB)

Kaster, F O, Kelm, B Michael, Zechmann, C M, Weber, M - A, Hamprecht, F A and Nix, O (2009). Classification of Spectroscopic Images in the DIROlab Environment. World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany. Springer. 25/V 252--255

Technical Report (145.73 KB)

Bautista, M, Sanakoyeu, A, Sutter, E and Ommer, B (2016). CliqueCNN: Deep Unsupervised Exemplar Learning. Proceedings of the Conference on Advances in Neural Information Processing Systems (NIPS). MIT Press, Barcelona. https://arxiv.org/abs/1608.08792

Article (5.79 MB)

Long, S R and Klinke, J (2002). A closer look at short waves generated by wave interactions with adverse currents. Gas Transfer at Water Surfaces. American Geophysical Union. 127 121--128

Wenig, M, Leue, C, Platt, U, Jähne, B and Haußecker, H (2000). Cloud classification analyzing image sequences. Computer Vision and Applications. A Guide for Students and Practitioners. Academic Press. 652--653

Brosowsky, M (2017). Cluster Resolving For Animal Tracking: Multi Hypotheses Tracking With Part Based Model For Object Hypotheses Generation And Pose Estimation. University of Heidelberg

Kannan, A, Winn, J and Rother, C (2007). Clustering appearance and shape by learning jigsaws. Advances in Neural Information Processing Systems. 657–664

Kannan, A, Winn, J and Rother, C (2007). Clustering appearance and shape by learning jigsaws. Advances in Neural Information Processing Systems. 657–664

Geese, M, Ruhnau, P and Jähne, B (2012). CNN based dark signal non-uniformity estimation. Cellular Nanoscale Networks and Their Applications (CNNA), 2012 13th International Workshop on. 1--6

Geese, M, Jähne, B and Ruhnau, P (2012). CNN Based Dark Signal Non-Uniformity Estimation. CNNA. 1-6

Breitenreicher, D, Lellmann, J and Schnörr, C (2013). COAL: a generic modelling and prototyping framework for convex optimization problems of variational image analysis. Optimization Methods and Software. 28 1081-1094. http://www.tandfonline.com/doi/abs/10.1080/10556788.2012.672571

Technical Report (1.69 MB)

Carlsohn, M F, Menze, B H, Kelm, B Michael, Hamprecht, F A, Kercek, A, Leitner, R and Polder, G (2006). Color image processing. CRC Press. 7(17) 393-419

Waas, S and Jähne, B (1996). Combined height/slope/curvature measurements of short ocean wind waves. Proc.\ The Air-Sea Interface, Radio and Acoustic Sensing, Turbulence and Wave Dynamics, Marseille, 24--30. June 1993. RSMAS, University of Miami. 383--388

Waas, S and Jähne, B (1994). Combined height/slope/curvature measurements of short ocean wind waves

Jähne, B, Schmidt, M and Rocholz, R (2005). Combined optical slope/height measurements of short wind waves: principles and calibration. Meas. Sci. Technol. 16 1937--1944

Waas, S and Jähne, B (1992). Combined slope-height measurements of short wind waves: first results from field and laboratory measurements. Optics of the Air-Sea Interface: Theory and Measurements. 1749 295--306

Neumann, J, Schnörr, C and Steidl, G (2005). Combined SVM-based Feature Selection and Classification. Machine Learning. 61 129-150

Baust, M, Weinmann, A, Wieczorek, M, Lasser, T, Storath, M and Navab, N (2016). Combined Tensor Fitting and TV Regularization in Diffusion Tensor Imaging based on a Riemannian Manifold Approach. IEEE Transactions on Medical Imaging. 35 1972–1989

Technical Report (8.65 MB)

Rocholz, R, Wanner, S, Schimpf, U and Jähne, B (2011). Combined visualization of wind waves and water surface temperature. Gas Transfer at Water Surfaces 2010. 496--506. http://hdl.handle.net/2433/156156

Hering, F, Wierzimok, D, Melville, W K and Jähne, B (1996). Combined wave and flow field visualization for investigation of short-wave/long-wave interaction. Proc.\ The Air-Sea Interface, Radio and Acoustic Sensing, Turbulence and Wave Dynamics, Marseille, 24--30. June 1993. RSMAS, University of Miami. 133--138

Kelm, B Michael, Pal, C and McCallum, A (2006). Combining Generative and Discriminative Methods for Pixel Classification with Multi-Conditional Learning.. ICPR 2006. 2 828-832

Technical Report (114.99 KB)

Bruhn, A, Weickert, J and Schnörr, C (2002). Combining the Advantages of Local and Global Optic Flow Methods. Pattern Recognition, Proc. 24th DAGM Symposium. Springer, Zürich, Switzerland. 2449 454–462

Jähne, (1994). A comparative analytical study of low-level motion estimators in space-time images. Proc. 16. DAGM-Symposium Mustererkennung

Nagel, L, Krall, K Ellen and Jähne, B (2015). Comparative heat and gas exchange measurements in the Heidelberg Aeolotron, a large annular wind-wave tank. Ocean Sci. 11 111--120

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