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Hanselmann, M (2010). Computational Methods for the Analysis of Mass Spectrometry Images. University of Heidelberg
Kirchner, M, Renard, B Y, Köthe, U, Pappin, D J, Hamprecht, F A, Steen, J A J and Steen, H (2010). Computational Protein Profile Similarity Screening for Quantitative Mass Spectrometry Experiments. Bioinformatics. 26 (1) 77-83PDF icon Technical Report (380.19 KB)
Wulf, M, Stiehl, H S and Schnörr, C (2000). On the computational rôle of the primate retina. Proc. 2nd ICSC Symposium on Neural Computation (NC 2000). Berlin, Germany
Jähne, B and Haußecker, H (2000). Computer Vision And Applications: A Guide For Students And Practitioners. Academic Press
Bell, P and Ommer, B (2018). Computer Vision und Kunstgeschichte — Dialog zweier Bildwissenschaften. Computing Art Reader: Einführung in die digitale Kunstgeschichte, P. Kuroczyński et al. (ed.)PDF icon 413-17-83318-2-10-20181210.pdf (2.98 MB)
Kandemir, M and Hamprecht, F A (2014). Computer-aided diagnosis from weak supervision: A benchmarking study. Computerized Medical Imaging and Graphics. 42 44-50PDF icon Technical Report (4.28 MB)
Haußecker, H and Fleet, D J (2001). Computing optical flow with physical models of brightness variation. IEEE Trans. Pattern Analysis Machine Intelligence. 23 661--673
Hanselmann, M, Kirchner, M, Renard, B Y, Amstalden, E R, Glunde, K, Heeren, R M A and Hamprecht, F A (2008). Concise Representation of MS Images by Probabilistic Latent Semantic Analysis. Analytical Chemistry. 80 9649-9658PDF icon Technical Report (3.91 MB)
Arnab, A, Zheng, S, Jayasumana, S, Romera-paredes, B, Kirillov, A, Savchynskyy, B, Rother, C, Kahl, F and Torr, P (2018). Conditional Random Fields Meet Deep Neural Networks for Semantic Segmentation. Cvpr. XX 1–15. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.308.8889&rep=rep1&type=pdf%0Ahttp://dx.doi.org/10.1109/CVPR.2012.6248050
Kluger, F, Brachmann, E, Ackermann, H, Rother, C, Yang, M Ying and Rosenhahn, B (2020). CONSAC: Robust Multi-Model Fitting by Conditional Sample Consensus. CVPR 2020. http://arxiv.org/abs/2001.02643PDF icon PDF (9.95 MB)
Schiegg, M, Hanslovsky, P, Kausler, B X, Hufnagel, L and Hamprecht, F A (2013). Conservation Tracking. ICCV 2013. Proceedings. 2928--2935PDF icon Technical Report (5.22 MB)
Nair, R (2010). Construction And Analysis Of Random Tree Ensembles. University of Heidelberg
Kotovenko, D, Sanakoyeu, A, Lang, S and Ommer, B (2019). Content and Style Disentanglement for Artistic Style Transfer. Proceedings of the Intl. Conf. on Computer Vision (ICCV)
Jähne, B, Jähne, B, Haußecker, H and Geißler, P (1999). Continuous and digital signals. Handbook of Computer Vision and Applications. Academic Press. 2 9--34
Fundana, K, Heyden, A, Gosch, C and Schnörr, C (2008). Continuous Graph Cuts for Prior-Based Object Segmentation. 19th Int.~Conf.~Patt.~Recog.~(ICPR). 1--4PDF icon Technical Report (414.89 KB)
Lellmann, J and Schnörr, C (2010). Continuous Multiclass Labeling Approaches And Algorithms. Univ. of Heidelberg. http://www.ub.uni-heidelberg.de/archiv/10460/
Lellmann, J and Schnörr, C (2011). Continuous Multiclass Labeling Approaches and Algorithms. CoRR. abs/1102.5448. http://arxiv.org/abs/1102.5448
Lellmann, J and Schnörr, C (2011). Continuous Multiclass Labeling Approaches and Algorithms. SIAM J.~Imag.~Sci. 4 1049-1096PDF icon Technical Report (4.31 MB)
Lauer, F, Bloch, G and Vidal, R (2009). A Continuous Optimization Framework for Hybrid System Identification. submitted to Automatica
Savarino, F and Schnörr, C (2019). Continuous-Domain Assignment Flows. preprint: arXiv. https://arxiv.org/abs/1910.07287
Schmitzer, B and Schnörr, C (2013). Contour Manifolds and Optimal Transport
Gosch, C (2009). Contour Methods for View Point Tracking. University of Heidelberg. http://www.ub.uni-heidelberg.de/archiv/9684/
Schlecht, J and Ommer, B (2011). Contour-based Object Detection. BMVC. 1--9PDF icon Technical Report (2.62 MB)
Heiler, M and Schnörr, C (2006). Controlling Sparseness in Non-negative Tensor Factorization. Computer Vision -- ECCV 2006. Springer. 3951 56-67PDF icon Technical Report (568.86 KB)
Yuan, J, Schnörr, C and Steidl, G (2009). Convex Hodge Decomposition and Regularization of Image Flows. J.~Math.~Imag.~Vision. 33 169-177PDF icon Technical Report (1003.75 KB)
Yuan, J, Steidl, G and Schnörr, C (2008). Convex Hodge Decomposition of Image Flows. Pattern Recognition -- 30th DAGM Symposium. Springer Verlag. 5096 416--425PDF icon Technical Report (290.72 KB)
Lellmann, J, Kappes, J H, Yuan, J, Becker, F and Schnörr, C (2008). Convex Multi-Class Image Labeling By Simplex-Constrained Total Variation. IWR, University of Heidelberg. http://www.ub.uni-heidelberg.de/archiv/8759/PDF icon Technical Report (2.6 MB)
Lellmann, J, Kappes, J H, Yuan, J, Becker, F and Schnörr, C (2009). Convex Multi-Class Image Labeling by Simplex-Constrained Total Variation. Scale Space and Variational Methods in Computer Vision (SSVM 2009). Springer. 5567 150-162PDF icon Technical Report (1.75 MB)
Lellmann, J, Kappes, J H, Yuan, J, Becker, F, Schnörr, C, Mórken, K and Lysaker, M (2009). Convex Multi-Class Image Labeling by Simplex-Constrained Total Variation. Scale Space and Variational Methods in Computer Vision (SSVM 2009). Springer. 5567 150-162
Lellmann, J, Becker, F and Schnörr, C (2009). Convex Optimization for Multi-Class Image Labeling with a Novel Family of Total Variation Based Regularizers. Proceedings of the IEEE Conference on Computer Vision (ICCV 09) Kyoto, Japan. 646-653
Lellmann, J, Becker, F and Schnörr, C (2009). Convex Optimization for Multi-Class Image Labeling with a Novel Family of Total Variation Based Regularizers. IEEE International Conference on Computer Vision (ICCV). 646 -- 653PDF icon Technical Report (930.18 KB)
Silvestri, F, Reinelt, G and Schnörr, C (2015). A Convex Relaxation Approach to the Affine Subspace Clustering Problem. Proc.~GCPRPDF icon Technical Report (878.63 KB)
Keuchel, J, Schellewald, C, Cremers, D and Schnörr, C (2001). Convex Relaxations for Binary Image Partitioning and Perceptual Grouping. Mustererkennung 2001. Springer, Munich, Germany. 2191 353–360
Yuan, J, Schnörr, C, Kohlberger, T and Ruhnau, P (2004). Convex Set-Based Estimation of Image Flows. ICPR 2004 – 17th Int. Conf. on Pattern Recognition. IEEE, Cambridge, UK. 1 124-127
Swoboda, P and Schnörr, C (2013). Convex Variational Image Restoration with Histogram Priors. SIAM J.~Imag.~Sci. 6 1719-1735PDF icon Technical Report (553.54 KB)

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