Publications

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R. Mackowiak, Lenz, P., Ghori, O., Diego, F., Lange, O., and Rother, C., CEREALS - Cost-Effective REgion-based Active Learning for Semantic Segmentation, in British Machine Vision Conference 2018, BMVC 2018, 2019.
T. Milbich, Roth, K., Sinha, S., Schmidt, L., Ghassemi, M., and Ommer, B., Characterizing Generalization under Out-Of-Distribution Shifts in Deep Metric Learning. 2021.
M. Erz, Charakterisierung von Laufzeitkamerasystemen für Lumineszenzlebensdauermessungen, vol. Dissertation. IWR, Fakultät für Physik und Astronomie, Univ. Heidelberg, 2011.
F. A. Hamprecht, Thiel, W., and van Gunsteren, W. F., Chemical library subset selection algorithms: a unified derivation using spatial statistics, Journal of Chemical Information and Computer Sciences, vol. 42, pp. 414-428, 2002.
D. Schmund, Münsterer, T., Lauer, H., Jähne, B., and Jähne, B., The circular wind wave facilities at the University of Heidelberg, in Air-Water Gas Transfer - Selected papers from the Third International Symposium on Air-Water Gas Transfer, 1995, p. 505--516.
B. Michael Kelm, Menze, B. H., Neff, T., Zechmann, C. M., and Hamprecht, F. A., CLARET: a tool for fully automated evaluation of MRSI with pattern recognition methods., in Bildverarbeitung für die Medizin 2006 - Algorithmen, Systeme, Anwendungen, 2006, pp. 51-55.PDF icon Technical Report (275.25 KB)
J. Heers, Schnörr, C., and Stiehl, H. S., A class of parallel algorithms for nonlinear variational image segmentation, in Proc. Noblesse Workshop on Non–Linear Model Based Image Analysis (NMBIA'98), Glasgow, Scotland, 1998.
F. Lenzen, Becker, F., Lellmann, J., Petra, S., and Schnörr, C., A class of quasi-variational inequalities for adaptive image denoising and decomposition, Computational Optimization and Applications, vol. 54, pp. 371-398, 2013.PDF icon Technical Report (748.66 KB)
F. Lenzen, Becker, F., Lellmann, J., Petra, S., and Schnörr, C., A Class of Quasi-Variational Inequalities for Adaptive Image Denoising and Decomposition, Computational Optimization and Applications (COAP), vol. 54 (2), pp. 371-398, 2013.
F. A. Hamprecht, Classification, Practical Handbook on Image Processing for Scientific and Technical Applications. CRC Press, pp. 509-519, 2004.PDF icon Technical Report (320.84 KB)
B. H. Menze, Wormit, M., Bachert, P., Lichy, M. P., Schlemmer, H. - P., and Hamprecht, F. A., Classification of in vivo magnetic resonance spectra, in Classification in ubiquitous challenge: Proceedings of the GfKl 2004, 2004, pp. 362-369.PDF icon Technical Report (240.1 KB)
B. H. Menze and Ur, J. A., Classification of multispectral ASTER imagery in the archaeological survey for settlement sites of the Near East, in Proc 10th International Symposium on Physical Measurements and Signature in Remote Sensing (ISPMRS 07), Davos, Switzerland, 2007.PDF icon Technical Report (920.71 KB)
F. O. Kaster, Kelm, B. Michael, Zechmann, C. M., Weber, M. - A., Hamprecht, F. A., and Nix, O., Classification of Spectroscopic Images in the DIROlab Environment, in World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany, 2009, vol. 25/V, p. 252--255.PDF icon Technical Report (145.73 KB)
M. Bautista, Sanakoyeu, A., Sutter, E., and Ommer, B., CliqueCNN: Deep Unsupervised Exemplar Learning, in Proceedings of the Conference on Advances in Neural Information Processing Systems (NIPS), Barcelona, 2016.PDF icon Article (5.79 MB)
S. R. Long and Klinke, J., A closer look at short waves generated by wave interactions with adverse currents, in Gas Transfer at Water Surfaces, 2002, vol. 127, p. 121--128.
M. Wenig, Leue, C., Platt, U., Jähne, B., and Haußecker, H., Cloud classification analyzing image sequences, Computer Vision and Applications. A Guide for Students and Practitioners. Academic Press, p. 652--653, 2000.
M. Brosowsky, Cluster Resolving for Animal Tracking: Multi Hypotheses Tracking with Part Based Model for Object Hypotheses Generation and Pose Estimation, University of Heidelberg, 2017.
A. Kannan, Winn, J., and Rother, C., Clustering appearance and shape by learning jigsaws, in Advances in Neural Information Processing Systems, 2007, pp. 657–664.
A. Kannan, Winn, J., and Rother, C., Clustering appearance and shape by learning jigsaws, in Advances in Neural Information Processing Systems, 2007, pp. 657–664.
M. Geese, Ruhnau, P., and Jähne, B., CNN based dark signal non-uniformity estimation, in Cellular Nanoscale Networks and Their Applications (CNNA), 2012 13th International Workshop on, 2012, p. 1--6.
M. Geese, Jähne, B., and Ruhnau, P., CNN Based Dark Signal Non-Uniformity Estimation, CNNA, pp. 1-6, 2012.
D. Breitenreicher, Lellmann, J., and Schnörr, C., COAL: a generic modelling and prototyping framework for convex optimization problems of variational image analysis, Optimization Methods and Software, vol. 28, pp. 1081-1094, 2013.PDF icon Technical Report (1.69 MB)
M. F. Carlsohn, Menze, B. H., Kelm, B. Michael, Hamprecht, F. A., Kercek, A., Leitner, R., and Polder, G., Color image processing, vol. 7(17), R. Lukac and Plataniotis, K. N., Eds. CRC Press, 2006, pp. 393-419.
S. Waas and Jähne, B., Combined height/slope/curvature measurements of short ocean wind waves, in Proc.\ The Air-Sea Interface, Radio and Acoustic Sensing, Turbulence and Wave Dynamics, Marseille, 24--30. June 1993, 1996, p. 383--388.
S. Waas and Jähne, B., Combined height/slope/curvature measurements of short ocean wind waves. 1994.
B. Jähne, Schmidt, M., and Rocholz, R., Combined optical slope/height measurements of short wind waves: principles and calibration, Meas. Sci. Technol., vol. 16, p. 1937--1944, 2005.
S. Waas and Jähne, B., Combined slope-height measurements of short wind waves: first results from field and laboratory measurements, in Optics of the Air-Sea Interface: Theory and Measurements, 1992, vol. 1749, p. 295--306.
J. Neumann, Schnörr, C., and Steidl, G., Combined SVM-based Feature Selection and Classification, Machine Learning, vol. 61, pp. 129-150, 2005.
M. Baust, Weinmann, A., Wieczorek, M., Lasser, T., Storath, M., and Navab, N., Combined Tensor Fitting and TV Regularization in Diffusion Tensor Imaging based on a Riemannian Manifold Approach, IEEE Transactions on Medical Imaging, vol. 35, no. 8, pp. 1972–1989, 2016.PDF icon Technical Report (8.65 MB)
R. Rocholz, Wanner, S., Schimpf, U., and Jähne, B., Combined visualization of wind waves and water surface temperature, in Gas Transfer at Water Surfaces 2010, 2011, p. 496--506.
F. Hering, Wierzimok, D., Melville, W. K., and Jähne, B., Combined wave and flow field visualization for investigation of short-wave/long-wave interaction, in Proc.\ The Air-Sea Interface, Radio and Acoustic Sensing, Turbulence and Wave Dynamics, Marseille, 24--30. June 1993, 1996, p. 133--138.
B. Michael Kelm, Pal, C., and McCallum, A., Combining Generative and Discriminative Methods for Pixel Classification with Multi-Conditional Learning., in ICPR 2006, 2006, vol. 2, pp. 828-832.PDF icon Technical Report (114.99 KB)
A. Bruhn, Weickert, J., and Schnörr, C., Combining the Advantages of Local and Global Optic Flow Methods, in Pattern Recognition, Proc. 24th DAGM Symposium, Zürich, Switzerland, 2002, vol. 2449, pp. 454–462.
B. Jähne, A comparative analytical study of low-level motion estimators in space-time images, in Proc. 16. DAGM-Symposium Mustererkennung, 1994.
L. Nagel, Krall, K. Ellen, and Jähne, B., Comparative heat and gas exchange measurements in the Heidelberg Aeolotron, a large annular wind-wave tank, Ocean Sci., vol. 11, p. 111--120, 2015.

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