Publications

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Hamprecht, F A (2004). Classification. Practical Handbook on Image Processing for Scientific and Technical Applications. CRC Press. 509-519PDF icon 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. 362-369PDF icon 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 SciencesPDF icon 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--255PDF icon 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/1608.08792PDF icon 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, Jähne, B and Ruhnau, P (2012). CNN Based Dark Signal Non-Uniformity Estimation. CNNA. 1-6
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
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/10.1080/10556788.2012.672571PDF icon 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 1972–1989PDF icon 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. 2 828-832PDF icon 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
Nagel, L, Krall, K Ellen and Jähne, B (2014). Comparative heat and gas exchange measurements in the Heidelberg Aeolotron, a large annular wind-wave tank. Ocean Sci. Discuss. 11 1691--1718
Kräuter, C, Richter, K E, Jähne, B, Mesarchaki, E and Williams, J (2011). A comparative lab study of tansfer velocities of volatile tracers with widely varying solubilities. DPG Frühjahrstagung Dresden, Fachverband Umweltphysik. http://www.dpg-verhandlungen.de/year/2011/conference/dresden/part/up/session/1/contribution/29
Szeliski, R, Zabih, R, Scharstein, D, Veksler, O, Kolmogorov, V, Agarwala, A, Tappen, M and Rother, C (2008). A comparative study of energy minimization methods for Markov random fields with smoothness-based priors. IEEE Transactions on Pattern Analysis and Machine Intelligence. Springer-Verlag. 30 1068–1080. http://vision.middlebury.edu/MRF.
Szeliski, R, Zabih, R, Scharstein, D, Veksler, O, Kolmogorov, V, Agarwala, A, Tappen, M and Rother, C (2008). A comparative study of energy minimization methods for Markov random fields with smoothness-based priors. IEEE Transactions on Pattern Analysis and Machine Intelligence. 30 1068–1080
Kappes, J H, Andres, B, Hamprecht, F A, Schnörr, C, Nowozin, S, Batra, D, Kim, S, Kausler, B X, Lellmann, J, Komodakis, N and Rother, C (2013). A Comparative Study of Modern Inference Techniques for Discrete Energy Minimization Problem. CVPRPDF icon Technical Report (1.35 MB)
Kappes, J H, Andres, B, Hamprecht, F A, Schnörr, C, Nowozin, S, Batra, D, Sungwoong, K, Kausler, B X, Lellmann, J, Komodakis, N and Rother, C (2013). A Comparative Study of Modern Inference Techniques for Discrete Energy Minimization Problems. CVPR 2013. ProceedingsPDF icon Technical Report (1.35 MB)
Kappes, J H, Andres, B, Hamprecht, F A, Schnörr, C, Nowozin, S, Batra, D, Kim, S, Kausler, B X, Kröger, T, Lellmann, J, Komodakis, N, Savchynskyy, B and Rother, C (2014). A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems. CoRR. http://arxiv.org/abs/1404.0533
Kappes, J H, Andres, B, Hamprecht, F A, Schnörr, C, Nowozin, S, Batra, D, Kim, S, Kausler, B X, Kröger, T, Lellmann, J, Komodakis, N, Savchynskyy, B and Rother, C (2015). A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems. Int.~J.~Comp.~VisionPDF icon Technical Report (5.12 MB)
Kappes, J H, Andres, B, Hamprecht, F A, Schnörr, C, Nowozin, S, Batra, D, Kim, S, Kausler, B X, Kröger, T, Lellmann, J, Komodakis, N, Savchynskyy, B and Rother, C (2014). A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems. CoRR. abs/1404.0533. http://hci.iwr.uni-heidelberg.de/opengm2/PDF icon Technical Report (3.32 MB)

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