Publications

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Schapowalow, A (2011). Implementierung Der Focus-Sweep Technik Mit Hilfe Von Scheimpflugoptik Und Tdi-Technik. Fakultät für Mathematik und Informatik, Ruprecht-Karls-Universität Heidelberg, Germany
Jähne, B and Jähne, B (1995). Impact of quantitative visualization and image processing on the study of small-scale air-sea interaction. Air-Water Gas Transfer, Selected Papers, 3rd Intern. Symp. on Air-Water Gas Transfer. AEON. 3--12
Meijering, E, Carpenter, A E, Peng, H, Hamprecht, F A and Olivo-Marin, J (2016). Imagining the future of bioimage analysis. Nature Biotechnology. 34 1250-1255PDF icon Technical Report (924.57 KB)
Rocholz, R and Jähne, B (2006). Imaging System for combined slope/height measurements of short wind waves : ISHG. Verhandlungen der Deutschen Physikalischen Gesellschaft, Spring Conference, Heidelberg, 15.-17.03.2006. Deutsche Physikalische Gesellschaft. http://www.dpg-verhandlungen.de/2006/heidelberg/up.html
Geißler, P, Jähne, B, Geißler, P and Haußecker, H (1999). Imaging optics. Handbook of Computer Vision and Applications. Academic Press. 1: Sensors and Imaging 63--101
Geißler, P and Jähne, B (2000). Imaging optics. Computer Vision and Applications - A Guide for Students and Practitioners. Academic Press. 53--84
Jähne, B and Geißler, P (1995). An imaging optical technique for bubble measurements. Proc. Sea Surface Sound '94. World Scientific. 290--296
Herzog, A G (2010). Imaging of Water-sided Gas-Concentration Fields at a Wind-Driven, Wavy Air-Water Interface. Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ. Heidelberg. Dissertation
Jähne, B, Klinke, J and Waas, S (1994). Imaging of short ocean wind waves: a critical theoretical review. J. Opt. Soc. Am. A. 11 2197--2209
Jähne, (1993). Imaging of gas transfer across gas/liquid interfaces. Imaging in Transport Processes. Begell House Publishers. 247--256. http://www.dl.begellhouse.com/references/1bb331655c289a0a,36adf33e6f249361.html
Falkenroth, A and Jähne, B (2006). Imaging concentration profiles of water boundary layer by Double-Dye LIF and inverse modelling. Verhandlungen der Deutschen Physikalischen Gesellschaft, Spring Conference, Heidelberg, 15.-17.03.2006. Deutsche Physikalische Gesellschaft. http://www.dpg-verhandlungen.de/2006/heidelberg/up.html
Frank, M and Hamprecht, F A (2011). Image-Based Supervision of a Periodically Working Machine. Pattern Analysis and Applications. 1-10PDF icon Technical Report (466.61 KB)
Töppe, E, Oswald, M R, Cremers, D and Rother, C (2011). Image-based 3D modeling via cheeger sets. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 6492 LNCS 53–64
Esser, P, Rombach, R, Blattmann, A and Ommer, B (2021). ImageBART: Bidirectional Context with Multinomial Diffusion for Autoregressive Image Synthesis. https://arxiv.org/abs/2108.08827
Jähne, B, Jähne, B and Haußecker, H (1999). Image warping. Handbook of Computer Vision and Applications. Volume II: Signal Processing and Pattern Recognition. Academic Press. 193--206
Wenig, M, Leue, C, Kraus, S, Wagner, T, Platt, U and Jähne, B (2001). Image sequence analysis of satellite NO$_2$ concnetration maps. Pattern Recognition, 23rd DAGM Symposium Munich. Springer. 2191 223--230
Jähne, B, Klinke, J, Geißler, P and Hering, F (1993). Image sequence analysis of ocean wind waves. Imaging in Transport Processes. Begell House Publishers. 257--268. http://www.dl.begellhouse.com/references/1bb331655c289a0a,36adf33e6f249361.html
Jähne, (1987). Image sequence analysis of complex physical objects: nonlinear small scale water surface waves. Proc. of 1st International Conference on Computer Vision. IEEE. 191--200
Jähne, (2003). Image sequence analysis in environmental and live sciences. Proceedings of the 25th DAGM Symposium on Pattern Recognition. Springer. 2781 608--617
Geese, M (2013). Image Sensor Nonuniformity Correction by a Scene-Based Maximum Likelihood Approach. IWR, Fakultät für Physik und Astronomie, Univ.\ Heidelberg. http://www.ub.uni-heidelberg.de/archiv/14391
Geese, M (2013). Image Sensor Nonuniformity Correction by a Scene-Based Maximum Likelihood Approach. IWR, Univ. Heidelberg. Dissertation
Lempitsky, V, Kohli, P, Rother, C and Sharp, T (2009). Image segmentation with a bounding box prior. Proceedings of the IEEE International Conference on Computer Vision. 277–284
Lempitsky, V, Blake, A and Rother, C (2008). Image segmentation by branch-and-mincut. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 5305 LNCS 15–29
Lempitsky, V, Blake, A and Rother, C (2008). Image segmentation by branch-and-mincut. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 5305 LNCS 15–29
Zisler, M, Aström, F, Petra, S and Schnörr, C (2017). Image Reconstruction by Multilabel Propagation. Proc. SSVM. Springer. 10302
Schleicher, J (2011). Image Processing For Super-Resolution Localization Microscopy Utilizing An Fpga Accelerator. University of Heidelberg
Aström, F, Petra, S, Schmitzer, B and Schnörr, C (2017). Image Labeling by Assignment. J. Math. Imag. Vision. 58 211–238. Papers/Astroem2017.pdf
Aström, F, Petra, S, Schmitzer, B and Schnörr, C (2016). Image Labeling by Assignment. http://arxiv.org/abs/1603.05285
Hühnerbein, R, Savarino, F, Aström, F and Schnörr, C (2018). Image Labeling Based on Graphical Models Using Wasserstein Messages and Geometric Assignment. SIAM Journal on Imaging Sciences. 11 1317-1362PDF icon Technical Report (2.62 MB)
Hühnerbein, R, Savarino, F, Aström, F and Schnörr, C (2018). Image Labeling Based on Graphical Models Using Wasserstein Messages and Geometric Assignment. SIAM J. Imaging Science. 11 1317–1362. https://epubs.siam.org/doi/abs/10.1137/17M1150669
Hühnerbein, R, Savarino, F, Aström, F and Schnörr, C (2017). Image Labeling Based on Graphical Models Using Wasserstein Messages and Geometric Assignment. http://arxiv.org/abs/1710.01493
Schellewald, C, Keuchel, J and Schnörr, C (2001). Image labeling and grouping by minimizing linear functionals over cones. Proc. Third Int. Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR'01). Springer, INRIA, Sophia Antipolis, France. 2134 267–282
Kaster, F O (2011). Image Analysis for the Life Sciences - Computer-assisted Tumor Diagnostics and Digital Embryomics. University of Heidelberg
Haußecker, H, Jähne, B, Geißler, P and Haußecker, H (1999). Illumination sources and techniques. Handbook of Computer Vision and Applications. Academic Press. 1: Sensors and Imaging 137--162
Berg, S, Kutra, D, Kroeger, T, Straehle, C N, Kausler, B X, Haubold, C, Schiegg, M, Ales, J, Beier, T, Rudy, M, Eren, K, Cervantes, J I, Xu, B, Beuttenmüller, F, Wolny, A, Zhang, C, Köthe, U, Hamprecht, F A and Kreshuk, A (2019). ilastik: interactive machine learning for (bio)image analysis. Nature Methods. 16 1226-1232

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