Publications

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Menze, B H, Kelm, B Michael, Splitthoff, N, Köthe, U and Hamprecht, F A (2011). On oblique random forests. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases 2011. Proceedings. Springer. 453-469PDF icon Technical Report (665.33 KB)
Schimpf, U, Frew, N M, Kalkenings, R, Garbe, C S and Jähne, B (2003). Observational studies of parameters influencing air--sea gas exchange. Geophysical Research Abstracts. 5 09328
Klinke, J, Jähne, B and Long, S R (1999). Observations of free and bound gravity-capillary Waves. The Wind-Driven Air-Sea Interface, Electromagnetic and Acoustic Sensing, Wave Dynamics and Turbulent Fluxes. 87--88
Huang, N E, Toba, Y, Shen, Z, Klinke, J, Jähne, B and Banner, M L (2001). Ocean wave spectra and integral properties. Wind Stress over the Ocean. Cambridge University Press. 82--123
Takami, M, Bell, P and Ommer, B (2014). Offline Learning of Prototypical Negatives for Efficient Online Exemplar SVM. Winter Conference on Applications of Computer Vision. IEEE. 377--384. http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6836075
Griessinger, M (2008). Oject Detection With Generic Features: An Application To Sted Microscopy. University of Heidelberg
Horvát, E - A, Hanselmann, M, Hamprecht, F A and Zweig, K A (2012). One plus one makes three (for social networks). PLoS ONE. 4,7
Geißler, P and Jähne, B (1995). One-image depth-from-focus for concentration measurements. Proc. ISPRS Intercommission Workshop `From Pixels to Sequences', Zurich, March 22 - 24, 1995, In Int'l Arch. of Photog. and Rem. Sens. RISC Books. XXX-5W1 122--127
Ruhnau, P, Stahl, A and Schnörr, C (2006). On-Line Variational Estimation of Dynamical Fluid Flows with Physics-Based Spatio-Temporal Regularization. Proc.~DAGM 2006. Springer. 375-388 375-388PDF icon Technical Report (902.47 KB)
Andres, B, Beier, T and Kappes, J H (2012). OpenGM: A C++ Library for Discrete Graphical Models. ArXiv e-prints
Wenig, M, Jähne, B and Platt, U (2005). Operator representation as a new differential optical absorption spectroscopy formalism. Appl. Optics. 44 3246-3253
Barron, J L, Liptay, A and Spies, H (2000). Optical and range flow to measure 3D plant growth and motion. Image Vision Computing New Zealand. 68--77
Becker, F, Petra, S and Schnörr, C (2014). Optical Flow. Handbook of Mathematical Methods in Imaging. Springer
Garbe, C S, Roetmann, K and Jähne, B (2006). An optical flow based technique for the non-invasive measurement of microfluidic flows. 12th Intern. Symp. on Flow Visualization, Göttingen, 10--14. September 2006
Garbe, C S, Roetmann, K, Beushausen, V and Jähne, B (2008). An optical flow MTV based technique for measuring microfluidic flow in the presence of diffusion and Taylor dispersion. Exp. Fluids. 44 439--450
Schmund, D, Schurr, U and Jähne, B (2000). Optical leaf growth analysis. Computer Vision and Applications - A Guide for Students and Practitioners. Academic Press. 640-641
Kiefhaber, D (2014). Optical Measurement of Short Wind Waves --- from the Laboratory to the Field. Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg. http://www.ub.uni-heidelberg.de/archiv/16304
Kiefhaber, D, Rocholz, R, Bauer, P Salomon and Jähne, B (2013). Optical measurement of surface ocean waves. 3rd EOS Topical Meeting on Blue Photonics --- Optics in the Sea
Mischler, W and Jähne, B (2012). Optical measurements of bubbles and spray in wind/water facilities at high wind speeds. 12th International Triennial Conference on Liquid Atomization and Spray Systems 2012, Heidelberg (ICLASS 2012)
Klinke, J (1996). Optical Measurements of Small-Scale Wind Generated Water Surface Waves in the Laboratory and the Field. Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg
Jähne, B and Jähne, B (1989). Optical measuring technique for small scale water surface waves. Advanced Optical Instrumentation for Remote Sensing of the Earth's Surface from Space, SPIE Proceeding 1129, International Congress on Optical Science and Engineering, Paris, 24-28 April 1989. 147--152
Mischler, W, Rocholz, R and Jähne, B (2009). Optical method for measuring size-distribution and lifetime of bubbles. Poster abstracts SOLAS Open Science Conference, Barcelona, 16--19 Sep. 2009
Friedl, F, Krah, N and Jähne, B (2015). Optical sensing of oxygen using a modified Stern-Volmer equation for high laser irradiance. Sensors and Actuators B: Chemical. 206 336--342
Ruhnau, P and Schnörr, C (2007). Optical Stokes Flow Estimation: An Imaging-Based Control Approach. Exp.~in Fluids. 42 61--78PDF icon Technical Report (1.54 MB)
Jähne, (1983). Optical water waves measuring techniques. Talk, 1st International Symposium on Gas Transfer at Water Surfaces, Cornell University, Ithaca, New York, June 13--15, 1983
Klappstein, J (2008). Optical-Flow based Detection of Moving Objects in Traffic Scenes. IWR, Fakultät für Mathematik und Informatik, Univ.\ Heidelberg. http://www.ub.uni-heidelberg.de/archiv/8591/
Jähne, (2012). Optik, Photonik und Bildverarbeitung --- eine spannende Reise. Optik & Photonik. 7 2--3
Menze, B H, Lichy, M P, Bachert, P, Kelm, B Michael, Schlemmer, H P and Hamprecht, F A (2006). Optimal Classification of Long Echo Time in vivo Magnetic Resonance Spectra in the Detection of Recurrent Brain Tumor. NMR in Biomedicine. 19 599-609PDF icon Technical Report (289.77 KB)
Atif, M (2013). Optimal Depth Estimation and Extended Depth of Field from Single Images by Computational Imaging using Chromatic Aberrations. IWR, Fakultät für Physik und Astronomie, Univ.\ Heidelberg. http://www.ub.uni-heidelberg.de/archiv/15594
Atif, M and Jähne, B (2012). Optimal Depth Estimation from a Single Image by Computational Imaging using Chromatic Aberrations. Forum Bildverarbeitung. KIT Scientific Publishing. 23--34. http://digbib.ubka.uni-karlsruhe.de/volltexte/1000030440
Atif, M and Jähne, B (2013). Optimal Depth Estimation from a Single Image by Computational Imaging Using Chromatic Aberrations. tm --- Technisches Messen. 80 343--348
Künsch, H R, Agrell, E and Hamprecht, F A (2005). Optimal lattices for sampling. IEEE Transactions on Information Theory. 51 634-647
Jehle, M and Jähne, B (2010). Optimal Lighting for Defect Detection: Illumination Systems, Machine Learning, and Practical Verification. Forum Bildverarbeitung, Regensburg, 02.-03.12.2010. KIT SCientific Publishing. 301-312
Jehle, M and Jähne, B (2010). Optimal lighting for defect detection: illumination systems, machine learning, and practical verification. Forum Bildverarbeitung. KIT Scientific Publishing. 241--252. http://digbib.ubka.uni-karlsruhe.de/volltexte/1000020266
Scharr, H (2000). Optimal Separable Interpolation Of Color Images With Bayer Array Format. DFG research unit Image Sequence Analysis to Investigate Dynamic Processes, Interdisciplinary Center for Scientific Computing, University of Heidelberg, Germany. http://www.ub.uni-heidelberg.de/archiv/12680/

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