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Huhn, F (2008). 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
Jähne, (1997). SIMD-Bildverarbeitungsalgorithmen mit dem Multimedia Extension-Instruktionssatz (MMX) von Intel. Automatisierungstechnik. 10 453--460
Hanselmann, M, Voss, B, Renard, B Y, Lindner, M, Köthe, U, Kirchner, M and Hamprecht, F A (2011). SIMA: Simultaneous Multiple Alignment of LC/MS Peak Lists. Bioinformatics. 27 (7) 987-993PDF icon Technical Report (2.2 MB)
Jähne, (2008). Signifikanter Umbruch zeichnet sich ab - Aktuelle Entwicklungen in der Bildverarbeitung. http://www.vdma-verlag.com/home/p427.html
Schnörr, (2007). Signal and Image Approximation with Level-Set Constraints. Computing. 81 137-160
Schnörr, (2007). Signal and Image Approximation with Level-Set Constraints. Computing. 81 137-160PDF icon Technical Report (506.8 KB)
Kumpf, T (2009). Sichtfelddesign Mittels Freiformspiegel Für Katadioptrische Systeme. IWR, Fakultät für Physik und Astronomie, Univ.\ Heidelberg
Milbich, T, Roth, K, Brattoli, B and Ommer, B (2020). Sharing Matters for Generalization in Deep Metric Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI). https://arxiv.org/abs/2004.05582
Monroy, A, Bell, P and Ommer, B (2012). Shaping Art with Art: Morphological Analysis for Investigating Artistic Reproductions. Proceedings of the European Conference on Computer Vision, Workshop on VISART. Springer. 7583 571--580PDF icon Technical Report (7 MB)
Cremers, D, Kohlberger, T and Schnörr, C (2003). Shape Statistics in Kernel Space for Variational Image Segmentation. Pattern Recognition. 36 1929–1943
Cremers, D, Kohlberger, T and Schnörr, C (2003). Shape Statistics in Kernel Space for Variational Image Segmentation. Pattern Recognition. 36 1929--1943PDF icon Technical Report (1.67 MB)
Bergtholdt, M and Schnörr, C (2005). Shape Priors and Online Appearance Learning for Variational Segmentation and Object Recognition in Static Scenes. Pattern Recognition, Proc. 27th DAGM Symposium. Springer. 3663 342–350
Islam, M Amirul, Kowal, M, Esser, P, Jia, S, Ommer, B, Derpanis, K G and Bruce, N (2021). Shape or Texture: Understanding Discriminative Features in CNNs. International Conference on Learning Representations (ICLR)
Didden, E - M, Thorarinsdottir, T L, Lenkoski, A and Schnörr, C (2015). Shape from Texture using Locally Scaled Point Processes. Image Anal. Stereol. 34 161-170
Jähne, B, Waas, S and Klinke, J (1993). Shape from shading techniques for short ocean wind waves. Imaging in Transport Processes. Begell House Publishers. 269--281. http://www.dl.begellhouse.com/references/1bb331655c289a0a,36adf33e6f249361.html
Weber, R (2010). 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
Gerloff, S, Hagemann, A, Schnörr, C, Tieck, S, Stiehl, H S, Dombrowski, R, Dreyer, M and Wiesendanger, R (1997). Semi–Automated Analysis of SXM Images. Proc. 9th Int. Conf. on Scanning Tunneling Microscopy/Spectroscopy and Related Techniques (STM'97). Hamburg, Germany
Görlitz, L, Menze, B H, Weber, M - A and Kelm, B Michael (2007). Semi-Supervised Tumor Detection in MRSI With Discriminative Random Fields. Pattern Recognition. Springer. 4713 224-233PDF icon Technical Report (872.46 KB)
Fita, E (2019). Semi-Supervised Distance-Based Segmentation. Heidelberg University
Drory, A, Haubold, C, Avidan, S and Hamprecht, F A (2014). Semi-Global Matching: A Principled Derivation in Terms of Message Passing. GCPR. Proceedings. 43-53PDF icon Technical Report (2.6 MB)
Heiler, M, Keuchel, J and Schnörr, C (2005). Semidefinite Clustering for Image Segmentation with A-priori Knowledge. Pattern Recognition, Proc. 27th DAGM Symposium. Springer. 3663 309–317
Greis, J (2009). Semi-Automatic Analysis Of High-Information-Content Neurobiological Image Data. University of Heidelberg
Maco, B, Cantoni, M, Holtmaat, A, Kreshuk, A, Hamprecht, F A and Knott, G W (2014). Semiautomated Correlative 3D Electron Microscopy of In Vivo Imaged Axons and Dendrites. Nature Protocols. 9 1354-1366PDF icon Technical Report (2.01 MB)
Hullin, M, Klein, R, Schultz, T, Yao, A, Li, W, Hosseini Jafari, O and Rother, C (2017). Semantic-Aware Image Smoothing. Vision, Modeling, and Visualization. https://hci.iwr.uni-heidelberg.de/vislearn/wp-content/uploads/2014/08/paper1024_CRC.pdf
Rathore, D (2016). Semantic Segmentation Using Deep Learning. University of Heidelberg
Kawetzki, D (2018). Semantic Segmentation Of Urban Scenes Using Deep Learning. Heidelberg University
Wolf, S, Li, Y, Pape, C, Bailoni, A, Kreshuk, A and Hamprecht, F A (2020). The Semantic Mutex Watershed for Efficient Bottom-Up Semantic Instance Segmentation. ECCV. Proceedings. 208-224
Li, Y (2019). Semantic Instance Segmentation With The Multiway Mutex Watershed. Heidelberg University
Zouhar, A, Rother, C and Fuchs, S (2015). Semantic 3-D labeling of ear implants using a global parametric transition prior. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 9350 177–184
Mustikovela, S K, Jampani, V, De Mello, S, Liu, S, Iqbal, U, Rother, C and Kautz, J (2020). Self-Supervised Viewpoint Learning From Image Collections. CONSAC. https://github.com/NVlabs/SSVPDF icon PDF (8.77 MB)
Sümer, Ö, Dencker, T and Ommer, B (2017). Self-supervised Learning of Pose Embeddings from Spatiotemporal Relations in Videos. Proceedings of the IEEE International Conference on Computer Vision (ICCV)PDF icon Paper (3.98 MB)PDF icon Supplementary Material (3.36 MB)
Neigel, P (2017). Self-Similarity Based Detection Of Temporal Motifs In Multivariate Signals. Heidelberg University
Zisler, M, Zern, A, Petra, S and Schnörr, C (2019). Self-Assignment Flows for Unsupervised Data Labeling on Graphs. preprint: arXiv. https://arxiv.org/abs/1911.03472
Staudacher, M, Hamprecht, F A and Görlitz, L (2009). Self Adjustment of Scanning Electron Microscopes / Selbstadaptivität von Rasterelektronenmikroskopen. Patent, Patent Number WO2009062781A1PDF icon Technical Report (46.64 KB)
Andres, B, Hamprecht, F A and Garbe, C S (2007). Selection of Local Optical Flow Models by Means of Residual Analysis. Pattern Recognition. Springer. 4713 72-81PDF icon Technical Report (229.64 KB)

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