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Cremers, D and Schnörr, C (2003). Statistical Shape Knowledge in Variational Motion Segmentation. Image and Vision Comp. 21 77-86
Brocke, M (2002). Statistical Image Sequence Processing for Temporal Change Detection. Proceedings of the 24th DAGM Symposium on Pattern Recognition. LNCS 2449 215--223
Kondermann, C, Mester, R and Garbe, C S (2008). A statistical confidence measure for optical flows. Proceedings of the ECCV. 5304 290--301
Schmähling, (2006). Statistical characterization of technical surface microstructure. University of Heidelberg
Hayn, M (2007). Statistical Analysis Of Spatio-Temporal Patterns In Global Nox Satellite Data. University of Heidelberg
Berger, K, Meister, S, Nair, R and Kondermann, D (2013). A State of the Art Report on Kinect Sensor Setups in Computer Vision. Time-of-Flight and Depth Imaging. Sensors, Algorithms, and Applications. Springer. 8200 257-272
Jäger, M, Humbert, S and Hamprecht, F A (2008). Sputter Tracking for the Automatic Monitoring of Industrial Laser Welding Processes. IEEE Transactions on Industrial Electronics. 55 2177-2184PDF icon Technical Report (1.83 MB)
Schmidt, S, Kappes, J H, Bergtholdt, M, Pekar, V, Dries, S, Bystrov, D and Schnörr, C (2007). Spine Detection and Labeling Using a Parts-Based Graphical Model. Proc. 20th International Conference on Information Processing in Medical Imaging (IPMI 2007). Springer. 4584 122-133PDF icon Technical Report (1.46 MB)
Krolla, B, Diebold, M, Goldlücke, B and Stricker, D (2014). Spherical Light Fields. Proceedings of the British Machine Vision Conference. BMVA Press
Vogel, F (2006). Spectroscopic Techniques For Gas-Exchange Measurements. Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg
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)
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
Antic, B and Ommer, B (2015). Spatio-temporal Video Parsing for Abnormality Detection. arXiv. abs/1502.06235. icon Technical Report (4.61 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.
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)
Peter, S (2015). Spatio-Temporal Motif Deconvolution For Calcium Image Analysis. University of Heidelberg
Rocholz, R and Jähne, B (2010). Spatio-temporal measurements of short wind water waves. EGU General Assembly 2010, Symposium AS2.2. EGU2010-5509
Rocholz, R (2008). Spatiotemporal Measurement of Short Wind-Driven Water Waves. Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg.
Jähne, (1993). Spatio-Temporal Image Processing: Theory And Scientific Applications. Springer-Verlag
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
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
Uttenweiler, D, Weber, C, Jähne, B, Fink, R H A 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. 8 40--47.
Schmidt, M (2008). Spatiotemporal Analysis of Range Imagery. IWR, Fakultät für Physik und Astronomie, University of Heidelberg.
Jehle, M (2006). Spatio-temporal analysis of flows close to water surfaces. IWR, Fakultät für Physik und Astronomie, Univ.\ 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
Kirchner, M (2004). Spatial Extensions To Self-Modeling Curve Resolution. University of Heidelberg
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
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
Wanner, S and Goldlücke, B (2012). Spatial and Angular Variational Superresolution of 4D Light Fields. ECCV 2012. Proceedings, Part 5. Springer. 7576 608-621
Wanner, S and Goldlücke, B (2012). Spatial and Angular Variational Super-Resolution of 4D Light Fields. European Conference on Computer Vision (ECCV)
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)
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)
Diego, F and Hamprecht, F A (2014). Sparse Space-Time Deconvolution for Calcium Image Analysis. NIPS. Proceedings. 64-72. icon Technical Report (5.27 MB)
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