Publications

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Ommer, B (2008). Seeing The Objects Behind The Parts: Learning Compositional Models For Visual Recognition. VDM Verlag. http://www.amazon.com/Seeing-Objects-Behind-Parts-Compositional/dp/3639021444/ref=sr_1_1?ie=UTF8&s=books&qid=1232659136&sr=1-1
Kostrykin, L, Schnörr, C and Rohr, K (2018). Segmentation of Cell Nuclei Using Intensity-Based Model Fitting and Sequential Convex Programming. Proc. ISBI
Markowsky, P, Reith, S, Zuber, T E, König, R, Rohr, K and Schnörr, C (2017). Segmentation of cell structure using model-based set covering with iterative reweighting. Proc. ISBI
Andres, B, Köthe, U, Helmstaedter, M, Denk, W and Hamprecht, F A (2008). Segmentation of SBFSEM Volume Data of Neural Tissue by Hierarchical Classification. Pattern Recognition. 30th DAGM Symposium Munich, Germany, June 10-13, 2008. Proceedings. Springer. 5096 142-152PDF icon Technical Report (1.21 MB)
Schnörr, (1994). Segmentation of Visual Motion by Minimizing Convex Non-Quadratic Functionals. 12th Int. Conf. on Pattern Recognition. Jerusalem, Israel
Leue, C, Geißler, P, Jähne, B, Jähne, B, Geißler, P and Haußecker, H (1996). Segmentierung von Partikelbildern in der Strömungsvisualisierung. Proceedings of 18th DAGM-Symposium Mustererkennung. 118--129
Haubold, C, Schiegg, M, Kreshuk, A, Berg, S, Köthe, U and Hamprecht, F A (2016). Segmenting and Tracking Multiple Dividing Targets Using ilastik. Focus on Bio-Image Informatics. Springer. 219 199-229PDF icon Technical Report (4.46 MB)
Jähne, (1998). Sehen, was man sonst nicht sieht. Ruperto Carola. 32--36. http://www.uni-heidelberg.de/uni/presse/RuCa3_98/jaehne.htm
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)
Andres, B, Garbe, C S, Schnörr, C and Jähne, B (2007). Selection of local optical flow models by means of residual analysis. Proceedings of the 29th DAGM Symposium on Pattern Recognition. Springer. 72--81
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)
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
Neigel, P (2017). Self-Similarity Based Detection Of Temporal Motifs In Multivariate Signals. Heidelberg University
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)
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)
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
Li, Y (2019). Semantic Instance Segmentation With The Multiway Mutex Watershed. 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
Kawetzki, D (2018). Semantic Segmentation Of Urban Scenes Using Deep Learning. Heidelberg University
Rathore, D (2016). Semantic Segmentation Using Deep Learning. University of Heidelberg
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
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)
Greis, J (2009). Semi-Automatic Analysis Of High-Information-Content Neurobiological Image Data. University of Heidelberg
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
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)
Fita, E (2019). Semi-Supervised Distance-Based Segmentation. Heidelberg University
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)
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
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
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
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
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)
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
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)
Cremers, D, Kohlberger, T and Schnörr, C (2003). Shape Statistics in Kernel Space for Variational Image Segmentation. Pattern Recognition. 36 1929–1943

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