Publications

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Beyer, M (1997). Skalenraumanalyse nichtlinearer Wasseroberflächenwellen. IWR, Fakultät für Physik und Astronomie, Univ.\ Heidelberg
Schimpf, U, Haußecker, H, Jähne, B, Jähne, B and Haußecker, H (2000). Small-scale air-sea interaction with thermography. Computer Vision and Applications. A Guide for Students and Practitioners. Academic Press. 638--639
Storath, M, Kiefer, L and Weinmann, A (2019). Smoothing for signals with discontinuities using higher order Mumford-Shah models. Numerische Mathematik. 143(2) 423-460PDF icon Technical Report (1.09 MB)
Jähne, B and Herrmann, H (1997). Softwarekonzepte und Algorithmen für die 3D-Bildverarbeitung. 4. ABW Workshop, TA Esslingen 22.--23.01.1997
Lenzen, F and Berger, J (2015). Solution-Driven Adaptive Total Variation Regularization. LNCS. Springer International Publishing. http://dx.doi.org/10.1007/978-3-319-18461-6_17PDF icon Technical Report (857.29 KB)
Pape, C, Beier, T, Li, P, Jain, V, Brock, D D and Kreshuk, A (2017). Solving Large Multicut Problems for Connectomics via Domain Decomposition. Bioimage Computing Workshop. ICCV. 1-10
Lenzen, F, Lellmann, J, Becker, F and Schnörr, C (2014). Solving Quasi-Variational Inequalities for Image Restoration with Adaptive Constraint Sets. SIAM J.~Imag.~Sci. 7 2139--2174PDF icon Technical Report (802.13 KB)
Lenzen, F, Lellmann, J, Becker, F and Schnörr, C (2014). Solving QVIs for Image Restoration with Adaptive Constraint Sets. SIAM Journal on Imaging Sciences (SIIMS), in press
Atif, M, Zimmermann, K and Jähne, B (2010). A space-variant (3D) image simulation tool for computational cameras. International Conference on Computational Photography (ICCP) 2010
Peter, S, Kirschbaum, E, Both, M, Campbell, L A, Harvey, B K, Heins, C, Durstewitz, D, Diego, F and Hamprecht, F A (2017). Sparse convolutional coding for neuronal assembly detection. NIPS, poster
Diego, F and Hamprecht, F A (2014). Sparse Space-Time Deconvolution for Calcium Image Analysis. NIPS. Proceedings. 64-72. http://papers.nips.cc/paper/5342-sparse-space-time-deconvolution-for-calcium-image-analysisPDF icon Technical Report (5.27 MB)
Breitenreicher, D, Lellmann, J and Schnörr, C (2011). Sparse Template-Based Variational Image Segmentation. Advances in Adaptive Data Analysis. 3 149-166PDF icon Technical Report (866.28 KB)
Petra, S, Schröder, A, Wieneke, B and Schnörr, C (2008). On Sparsity Maximization in Tomographic Particle Image Reconstruction. Pattern Recognition -- 30th DAGM Symposium. Springer Verlag. 5096 294--303PDF icon Technical Report (1014.71 KB)
Wanner, S and Goldlücke, B (2012). Spatial and Angular Variational Super-Resolution of 4D Light Fields. European Conference on Computer Vision (ECCV)
Wanner, S and Goldlücke, B (2012). Spatial and Angular Variational Superresolution of 4D Light Fields. ECCV 2012. Proceedings, Part 5. Springer. 7576 608-621
Jähne, B, Jähne, B and Haußecker, H (1999). Spatial and fourier domain. Handbook of Computer Vision and Applications. Volume II: Signal Processing and Pattern Recognition. Academic Press. 35--66
Wagner, T, Leue, C, Wenig, M, Pfeilsticker, K and Platt, U (2001). Spatial and temporal distribution of enhanced boundary layer BrO concentrations measured by the GOME instrument aboard ERS-2. J. Geophys. Res. 106 24225--24235
Kirchner, M (2004). Spatial Extensions To Self-Modeling Curve Resolution. University of Heidelberg
Klinke, J and Jähne, B (1995). Spatial measurement of short ocean waves during the MBL-ARI West Coast Experiment. IAPSO Proceedings, XXI General Assembly, Honolulu, Hawai, August 1995, PS-10 Spatial Structure of Short Ocean Waves. 390
Jehle, M (2006). Spatio-temporal analysis of flows close to water surfaces. IWR, Fakultät für Physik und Astronomie, Univ.\ Heidelberg. http://www.ub.uni-heidelberg.de/archiv/7060/
Schmidt, M (2008). Spatiotemporal Analysis of Range Imagery. IWR, Fakultät für Physik und Astronomie, University of Heidelberg. http://www.ub.uni-heidelberg.de/archiv/8879/
Uttenweiler, D, Weber, C, Jähne, B, Fink, R and Schaar, H (2003). Spatiotemporal anisotropic diffusion filtering to improve signal-to-noise ratios and object restoration in fluorescence microscopic image sequences.. J Biomed Opt. Ruprecht-Karls-Universität Heidelberg, Institut für Physiologie und Pathophysiologie Medical Biophysics, Im Neuenheimer Feld 326, 69120 Heidelberg, Germany. dietmar.uttenweiler@urz.uni-heidelberg.de. 8 40--47. http://dx.doi.org/10.1117/1.1527627
Herzog, A G, Friedl, F and Jähne, B (2010). Spatio-temporal fluctuations of water-sided gas concentration fields under wind-induced turbulence. 6th Int. Symp. Gas Transfer at Water Surfaces, Kyoto, May 17--21, 2010
Garbe, C S, Kondermann, D, Jehle, M and Jähne, B (2009). Spatiotemporal image analysis for flow measurements. Imaging Measurement Methods for Flow Analysis, Results of the DFG Priority Programme 1147 Imaging Measurement Methods for Flow Analysis 2003-2009. Springer. 106 289--305
Jähne, (1993). Spatio-Temporal Image Processing: Theory And Scientific Applications. Springer-Verlag
Rocholz, R (2008). Spatiotemporal Measurement of Short Wind-Driven Water Waves. Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg. http://www.ub.uni-heidelberg.de/archiv/8897
Rocholz, R and Jähne, B (2010). Spatio-temporal measurements of short wind water waves. EGU General Assembly 2010, Symposium AS2.2. EGU2010-5509
Peter, S (2015). Spatio-Temporal Motif Deconvolution For Calcium Image Analysis. University of Heidelberg
Antic, B, Büchler, U, Wahl, A S, Schwab, M E and Ommer, B (2015). Spatiotemporal Parsing of Motor Kinematics for Assessing Stroke Recovery. Medical Image Computing and Computer-Assisted Intervention. SpringerPDF icon Article (2.24 MB)
Reith, S (2014). Spatio-Temporal Slope Measurement Of Short Wind Waves Under The Influence Of Surface Films At The Heidelberg Aeolotron. Institut für Umweltphysik, Universität Heidelberg, Germany. http://www.ub.uni-heidelberg.de/archiv/17697
Antic, B and Ommer, B (2015). Spatio-temporal Video Parsing for Abnormality Detection. arXiv. abs/1502.06235. http://arxiv.org/abs/1502.06235PDF icon Technical Report (4.61 MB)
Rahaman, N, Arpit, D, Baratin, A, Draxler, F, Lin, M, Hamprecht, F A, Bengio, Y and Courville, A (2018). On the spectral bias of deep neural networks. arXiv preprint arXiv:1806.08734
Lauer, F and Schnörr, C (2009). Spectral Clustering of Linear Subspaces for Motion Segmentation. Proceedings of the IEEE Conference on Computer Vision (ICCV 09) Kyoto, Japan, in press. 678-685
Lauer, F and Schnörr, C (2009). Spectral Clustering of Linear Subspaces for Motion Segmentation. Proc.~IEEE Int.~Conf.~Computer Vision (ICCV'09)PDF icon Technical Report (1.12 MB)
Vogel, F (2006). Spectroscopic Techniques For Gas-Exchange Measurements. Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg

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