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S. Wanner and Goldlücke, B., “Spatial and Angular Variational Superresolution of 4D Light Fields”, in ECCV 2012. Proceedings, Part 5, 2012, vol. 7576, pp. 608-621.
S. Wanner and Goldlücke, B., “Spatial and Angular Variational Super-Resolution of 4D Light Fields”, in European Conference on Computer Vision (ECCV), 2012.
B. Jähne, Jähne, B., and Haußecker, H., “Spatial and fourier domain”, Handbook of Computer Vision and Applications. Volume II: Signal Processing and Pattern Recognition. Academic Press, p. 35--66, 1999.
T. Wagner, Leue, C., Wenig, M., Pfeilsticker, K., and Platt, U., “Spatial and temporal distribution of enhanced boundary layer BrO concentrations measured by the GOME instrument aboard ERS-2”, J. Geophys. Res., vol. 106, p. 24225--24235, 2001.
M. Kirchner, “Spatial extensions to self-modeling curve resolution”, University of Heidelberg, 2004.
J. Klinke and Jähne, B., “Spatial measurement of short ocean waves during the MBL-ARI West Coast Experiment”, in IAPSO Proceedings, XXI General Assembly, Honolulu, Hawai, August 1995, PS-10 Spatial Structure of Short Ocean Waves, 1995, p. 390.
C. Rhemann, Rother, C., Kohli, P., and Gelautz, M., “A spatially varying PSF-based prior for alpha matting”, in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2010, pp. 2149–2156.
M. Jehle, “Spatio-temporal analysis of flows close to water surfaces”. IWR, Fakultät für Physik und Astronomie, Univ.\ Heidelberg, 2006.
M. Schmidt, “Spatiotemporal Analysis of Range Imagery”. IWR, Fakultät für Physik und Astronomie, University of Heidelberg, 2008.
D. Uttenweiler, Weber, C., Jähne, B., Fink, R. H. A., and Schaar, H., “Spatiotemporal anisotropic diffusion filtering to improve signal-to-noise ratios and object restoration in fluorescence microscopic image sequences.”, J Biomed Opt, vol. 8, p. 40--47, 2003.
A. G. Herzog, Friedl, F., and Jähne, B., “Spatio-temporal fluctuations of water-sided gas concentration fields under wind-induced turbulence”, in 6th Int. Symp. Gas Transfer at Water Surfaces, Kyoto, May 17--21, 2010, 2010.
C. S. Garbe, Kondermann, D., Jehle, M., and Jähne, B., “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, vol. 106. Springer, p. 289--305, 2009.
B. Jähne, Spatio-Temporal Image Processing: Theory and Scientific Applications, vol. 751. Springer-Verlag, 1993.
R. Rocholz, “Spatiotemporal Measurement of Short Wind-Driven Water Waves”. Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg, 2008.
R. Rocholz and Jähne, B., “Spatio-temporal measurements of short wind water waves”, in EGU General Assembly 2010, Symposium AS2.2, 2010, pp. EGU2010-5509.
K. Schwarz, “Spatio-Temporal Measurements of Water-Wave Height and Slope using Laser-Induced Fluorescence and Splines”, Institut für Umweltphysik, Universität Heidelberg, Germany, 2016.
S. Peter, “Spatio-Temporal Motif Deconvolution for Calcium Image Analysis”, University of Heidelberg, 2015.
B. Antic, Büchler, U., Wahl, A. - S., Schwab, M. E., and Ommer, B., “Spatiotemporal Parsing of Motor Kinematics for Assessing Stroke Recovery”, in Medical Image Computing and Computer-Assisted Intervention, 2015.PDF icon Article (2.24 MB)
A. Rennebaum, “Spatio-Temporal Properties of the initial Wave Formation Phase at the Aeolotron”, Institut für Umweltphysik, Universität Heidelberg, Germany, 2017.
S. Reith, “Spatio-temporal slope measurement of short wind waves under the influence of surface films at the Heidelberg Aeolotron”, Institut für Umweltphysik, Universität Heidelberg, Germany, 2014.
B. Antic and Ommer, B., “Spatio-temporal Video Parsing for Abnormality Detection”, arXiv, vol. abs/1502.06235, 2015.PDF icon Technical Report (4.61 MB)
N. Rahaman, Arpit, D., Baratin, A., Draxler, F., Lin, M., Hamprecht, F. A., Bengio, Y., and Courville, A., “On the spectral bias of deep neural networks”, arXiv preprint arXiv:1806.08734, 2018.
F. Lauer and Schnörr, C., “Spectral Clustering of Linear Subspaces for Motion Segmentation”, in Proc.~IEEE Int.~Conf.~Computer Vision (ICCV'09), 2009.PDF icon Technical Report (1.12 MB)
F. Lauer and Schnörr, C., “Spectral Clustering of Linear Subspaces for Motion Segmentation”, in Proc. IEEE Int. Conf. Computer Vision (ICCV'09), Kyoto, Japan, 2009.
F. Lauer and Schnörr, C., “Spectral Clustering of Linear Subspaces for Motion Segmentation”, in Proceedings of the IEEE Conference on Computer Vision (ICCV 09) Kyoto, Japan, in press, 2009, pp. 678-685.
F. Vogel, “Spectroscopic Techniques for Gas-Exchange Measurements”, Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg, 2006.
M. Hornáček, Fitzgibbon, A., and Rother, C., “SphereFlow: 6 DoF scene flow from RGB-D pairs”, in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2014, pp. 3526–3533.
B. Krolla, Diebold, M., Goldlücke, B., and Stricker, D., “Spherical Light Fields”, in Proceedings of the British Machine Vision Conference, 2014.
S. Schmidt, Kappes, J. H., Bergtholdt, M., Pekar, V., Dries, S., Bystrov, D., and Schnörr, C., “Spine Detection and Labeling Using a Parts-Based Graphical Model”, in Proc. 20th International Conference on Information Processing in Medical Imaging (IPMI 2007), 2007, vol. 4584, pp. 122-133.PDF icon Technical Report (1.46 MB)
S. Schmidt, Kappes, J. H., Bergtholdt, M., Pekar, V., Dries, S., Bystrov, D., and Schnörr, C., “Spine Detection and Labeling Using a Parts-Based Graphical Model”, in Proc. 20th International Conference on Information Processing in Medical Imaging (IPMI 2007), 2007, vol. 4584, pp. 122-133.
M. Jäger, Humbert, S., and Hamprecht, F. A., “Sputter Tracking for the Automatic Monitoring of Industrial Laser Welding Processes”, IEEE Transactions on Industrial Electronics, vol. 55, pp. 2177-2184, 2008.PDF icon Technical Report (1.83 MB)
K. Berger, Meister, S., Nair, R., and Kondermann, D., “A State of the Art Report on Kinect Sensor Setups in Computer Vision”, in Time-of-Flight and Depth Imaging. Sensors, Algorithms, and Applications, 2013, vol. 8200, pp. 257-272.
M. Hayn, “Statistical analysis of spatio-temporal patterns in global NOX satellite data”, University of Heidelberg, 2007.
J. Schmähling, “Statistical characterization of technical surface microstructure”. University of Heidelberg, 2006.
C. Kondermann, Mester, R., and Garbe, C. S., “A statistical confidence measure for optical flows”, in Proceedings of the ECCV, 2008, vol. 5304, p. 290--301.

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