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F. Huhn, “A Simple Instrument for the Measurement of the Slope and Height Distributions of Small Scale Wind-Driven Water Waves”, Institute for Environmental Physics, University of Heidelberg, 2008.
B. Jähne, “SIMD-Bildverarbeitungsalgorithmen mit dem Multimedia Extension-Instruktionssatz (MMX) von Intel”, Automatisierungstechnik, vol. 10, p. 453--460, 1997.
M. Hanselmann, Voss, B., Renard, B. Y., Lindner, M., Köthe, U., Kirchner, M., and Hamprecht, F. A., “SIMA: Simultaneous Multiple Alignment of LC/MS Peak Lists”, Bioinformatics, vol. 27 (7), pp. 987-993, 2011.PDF icon Technical Report (2.2 MB)
B. Jähne, “Signifikanter Umbruch zeichnet sich ab - Aktuelle Entwicklungen in der Bildverarbeitung”. 2008.
C. Schnörr, “Signal and Image Approximation with Level-Set Constraints”, Computing, vol. 81, pp. 137-160, 2007.
C. Schnörr, “Signal and Image Approximation with Level-Set Constraints”, Computing, vol. 81, pp. 137-160, 2007.PDF icon Technical Report (506.8 KB)
T. Kumpf, “Sichtfelddesign mittels Freiformspiegel für katadioptrische Systeme”, IWR, Fakultät für Physik und Astronomie, Univ.\ Heidelberg, 2009.
T. Milbich, Roth, K., Brattoli, B., and Ommer, B., “Sharing Matters for Generalization in Deep Metric Learning”, IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2020.
A. Monroy, Bell, P., and Ommer, B., “Shaping Art with Art: Morphological Analysis for Investigating Artistic Reproductions”, in Proceedings of the European Conference on Computer Vision, Workshop on VISART, 2012, vol. 7583, p. 571--580.PDF icon Technical Report (7 MB)
D. Cremers, Kohlberger, T., and Schnörr, C., “Shape Statistics in Kernel Space for Variational Image Segmentation”, Pattern Recognition, vol. 36, pp. 1929–1943, 2003.
D. Cremers, Kohlberger, T., and Schnörr, C., “Shape Statistics in Kernel Space for Variational Image Segmentation”, Pattern Recognition, vol. 36, p. 1929--1943, 2003.PDF icon Technical Report (1.67 MB)
M. Bergtholdt and Schnörr, C., “Shape Priors and Online Appearance Learning for Variational Segmentation and Object Recognition in Static Scenes”, Pattern Recognition, Proc. 27th DAGM Symposium, vol. 3663. Springer, pp. 342–350, 2005.
M. Amirul Islam, Kowal, M., Esser, P., Jia, S., Ommer, B., Derpanis, K. G., and Bruce, N., “Shape or Texture: Understanding Discriminative Features in CNNs”, International Conference on Learning Representations (ICLR). 2021.
E. - M. Didden, Thorarinsdottir, T. L., Lenkoski, A., and Schnörr, C., “Shape from Texture using Locally Scaled Point Processes”, Image Anal. Stereol., vol. 34, pp. 161-170, 2015.
B. Jähne, Waas, S., and Klinke, J., “Shape from shading techniques for short ocean wind waves”, in Imaging in Transport Processes, 1993, p. 269--281.
R. Weber, “Setup of a Laser Slope Gauge for the Measurement of Wave Slope Distributions at the Small Circular Wind Wave Facility”, Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg, 2010.
S. Gerloff, Hagemann, A., Schnörr, C., Tieck, S., Stiehl, H. S., Dombrowski, R., Dreyer, M., and Wiesendanger, R., “Semi–Automated Analysis of SXM Images”, in Proc. 9th Int. Conf. on Scanning Tunneling Microscopy/Spectroscopy and Related Techniques (STM'97), Hamburg, Germany, 1997.
L. Görlitz, Menze, B. H., Weber, M. - A., and Kelm, B. Michael, “Semi-Supervised Tumor Detection in MRSI With Discriminative Random Fields”, in Pattern Recognition, 2007, vol. 4713, pp. 224-233.PDF icon Technical Report (872.46 KB)
E. Fita, “Semi-supervised distance-based segmentation”, Heidelberg University, 2019.
A. Drory, Haubold, C., Avidan, S., and Hamprecht, F. A., “Semi-Global Matching: A Principled Derivation in Terms of Message Passing”, in GCPR. Proceedings, 2014, pp. 43-53.PDF icon Technical Report (2.6 MB)
M. Heiler, Keuchel, J., and Schnörr, C., “Semidefinite Clustering for Image Segmentation with A-priori Knowledge”, Pattern Recognition, Proc. 27th DAGM Symposium, vol. 3663. Springer, pp. 309–317, 2005.
J. Greis, “Semi-automatic analysis of high-information-content neurobiological image data”, University of Heidelberg, 2009.
B. Maco, Cantoni, M., Holtmaat, A., Kreshuk, A., Hamprecht, F. A., and Knott, G. W., “Semiautomated Correlative 3D Electron Microscopy of In Vivo Imaged Axons and Dendrites”, Nature Protocols, vol. 9, pp. 1354-1366, 2014.PDF icon Technical Report (2.01 MB)
M. Hullin, Klein, R., Schultz, T., Yao, A., Li, W., Hosseini Jafari, O., and Rother, C., “Semantic-Aware Image Smoothing”, Vision, Modeling, and Visualization, 2017.
D. Rathore, “Semantic Segmentation Using Deep Learning”, University of Heidelberg, 2016.
D. Kawetzki, “Semantic Segmentation of Urban Scenes Using Deep Learning”, Heidelberg University, 2018.
S. Wolf, Li, Y., Pape, C., Bailoni, A., Kreshuk, A., and Hamprecht, F. A., “The Semantic Mutex Watershed for Efficient Bottom-Up Semantic Instance Segmentation”, ECCV. Proceedings. pp. 208-224, 2020.
Y. Li, “Semantic Instance Segmentation with the Multiway Mutex Watershed”, Heidelberg University, 2019.
A. Zouhar, Rother, C., and Fuchs, S., “Semantic 3-D labeling of ear implants using a global parametric transition prior”, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2015, vol. 9350, pp. 177–184.
S. K. Mustikovela, Jampani, V., De Mello, S., Liu, S., Iqbal, U., Rother, C., and Kautz, J., “Self-Supervised Viewpoint Learning From Image Collections”, in CONSAC, 2020.PDF icon PDF (8.77 MB)
Ö. Sümer, Dencker, T., and Ommer, B., “Self-supervised Learning of Pose Embeddings from Spatiotemporal Relations in Videos”, in Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2017.PDF icon Paper (3.98 MB)PDF icon Supplementary Material (3.36 MB)
P. Neigel, “Self-Similarity Based Detection of Temporal Motifs in Multivariate Signals”, Heidelberg University, 2017.
M. Zisler, Zern, A., Petra, S., and Schnörr, C., “Self-Assignment Flows for Unsupervised Data Labeling on Graphs”, preprint: arXiv, 2019.
M. Staudacher, Hamprecht, F. A., and Görlitz, L., “Self Adjustment of Scanning Electron Microscopes / Selbstadaptivität von Rasterelektronenmikroskopen”, Patent, Patent Number WO2009062781A1, 2009.PDF icon Technical Report (46.64 KB)
B. Andres, Hamprecht, F. A., and Garbe, C. S., “Selection of Local Optical Flow Models by Means of Residual Analysis”, in Pattern Recognition, 2007, vol. 4713, pp. 72-81.PDF icon Technical Report (229.64 KB)

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