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M. Hanselmann, “Computational Methods for the Analysis of Mass Spectrometry Images”. University of Heidelberg, 2010.
M. Kirchner, Renard, B. Y., Köthe, U., Pappin, D. J., Hamprecht, F. A., Steen, J. A. J., and Steen, H., “Computational Protein Profile Similarity Screening for Quantitative Mass Spectrometry Experiments”, Bioinformatics, vol. 26 (1), pp. 77-83, 2010.PDF icon Technical Report (380.19 KB)
M. Wulf, Stiehl, H. S., and Schnörr, C., “On the computational rôle of the primate retina”, in Proc. 2nd ICSC Symposium on Neural Computation (NC 2000), Berlin, Germany, 2000.
B. Jähne and Haußecker, H., Computer Vision and Applications: A Guide for Students and Practitioners. Academic Press, 2000, p. 679.
P. Bell and Ommer, B., “Computer Vision und Kunstgeschichte — Dialog zweier Bildwissenschaften”, in Computing Art Reader: Einführung in die digitale Kunstgeschichte, P. Kuroczyński et al. (ed.), 2018.PDF icon 413-17-83318-2-10-20181210.pdf (2.98 MB)
M. Kandemir and Hamprecht, F. A., “Computer-aided diagnosis from weak supervision: A benchmarking study”, Computerized Medical Imaging and Graphics, vol. 42, pp. 44-50, 2014.PDF icon Technical Report (4.28 MB)
H. Haußecker and Fleet, D. J., “Computing optical flow with physical models of brightness variation”, IEEE Trans. Pattern Analysis Machine Intelligence, vol. 23, p. 661--673, 2001.
M. Hanselmann, Kirchner, M., Renard, B. Y., Amstalden, E. R., Glunde, K., Heeren, R. M. A., and Hamprecht, F. A., “Concise Representation of MS Images by Probabilistic Latent Semantic Analysis”, Analytical Chemistry, vol. 80, pp. 9649-9658, 2008.PDF icon Technical Report (3.91 MB)
A. Arnab, Zheng, S., Jayasumana, S., Romera-paredes, B., Kirillov, A., Savchynskyy, B., Rother, C., Kahl, F., and Torr, P., “Conditional Random Fields Meet Deep Neural Networks for Semantic Segmentation”, Cvpr, vol. XX, pp. 1–15, 2018.
F. Kluger, Brachmann, E., Ackermann, H., Rother, C., Yang, M. Ying, and Rosenhahn, B., “CONSAC: Robust Multi-Model Fitting by Conditional Sample Consensus”, in CVPR 2020, 2020.PDF icon PDF (9.95 MB)
M. Schiegg, Hanslovsky, P., Kausler, B. X., Hufnagel, L., and Hamprecht, F. A., “Conservation Tracking”, in ICCV 2013. Proceedings, 2013, p. 2928--2935.PDF icon Technical Report (5.22 MB)
R. Nair, “Construction and analysis of random tree ensembles”, University of Heidelberg, 2010.
D. Kotovenko, Sanakoyeu, A., Lang, S., and Ommer, B., “Content and Style Disentanglement for Artistic Style Transfer”, in Proceedings of the Intl. Conf. on Computer Vision (ICCV), 2019.
B. Jähne, Jähne, B., Haußecker, H., and Geißler, P., “Continuous and digital signals”, Handbook of Computer Vision and Applications, vol. 2. Academic Press, p. 9--34, 1999.
K. Fundana, Heyden, A., Gosch, C., and Schnörr, C., “Continuous Graph Cuts for Prior-Based Object Segmentation”, in 19th Int.~Conf.~Patt.~Recog.~(ICPR), 2008, p. 1--4.PDF icon Technical Report (414.89 KB)
J. Lellmann and Schnörr, C., “Continuous Multiclass Labeling Approaches and Algorithms”, Univ. of Heidelberg, 2010.
J. Lellmann and Schnörr, C., “Continuous Multiclass Labeling Approaches and Algorithms”, CoRR, vol. abs/1102.5448, 2011.
J. Lellmann and Schnörr, C., “Continuous Multiclass Labeling Approaches and Algorithms”, SIAM J.~Imag.~Sci., vol. 4, pp. 1049-1096, 2011.PDF icon Technical Report (4.31 MB)
F. Lauer, Bloch, G., and Vidal, R., “A Continuous Optimization Framework for Hybrid System Identification”, in submitted to Automatica, 2009.
F. Savarino and Schnörr, C., “Continuous-Domain Assignment Flows”, preprint: arXiv, 2019.
B. Schmitzer and Schnörr, C., “Contour Manifolds and Optimal Transport”. 2013.
C. Gosch, “Contour Methods for View Point Tracking”. University of Heidelberg, 2009.
J. Schlecht and Ommer, B., “Contour-based Object Detection”, in BMVC, 2011, p. 1--9.PDF icon Technical Report (2.62 MB)
M. Heiler and Schnörr, C., “Controlling Sparseness in Non-negative Tensor Factorization”, in Computer Vision -- ECCV 2006, 2006, vol. 3951, pp. 56-67.PDF icon Technical Report (568.86 KB)
J. Yuan, Schnörr, C., and Steidl, G., “Convex Hodge Decomposition and Regularization of Image Flows”, J.~Math.~Imag.~Vision, vol. 33, pp. 169-177, 2009.PDF icon Technical Report (1003.75 KB)
J. Yuan, Steidl, G., and Schnörr, C., “Convex Hodge Decomposition of Image Flows”, in Pattern Recognition -- 30th DAGM Symposium, 2008, vol. 5096, p. 416--425.PDF icon Technical Report (290.72 KB)
J. Lellmann, Kappes, J. H., Yuan, J., Becker, F., and Schnörr, C., “Convex Multi-Class Image Labeling by Simplex-Constrained Total Variation”, IWR, University of Heidelberg, 2008.PDF icon Technical Report (2.6 MB)
J. Lellmann, Kappes, J. H., Yuan, J., Becker, F., and Schnörr, C., “Convex Multi-Class Image Labeling by Simplex-Constrained Total Variation”, in Scale Space and Variational Methods in Computer Vision (SSVM 2009), 2009, vol. 5567, pp. 150-162.PDF icon Technical Report (1.75 MB)
J. Lellmann, Kappes, J. H., Yuan, J., Becker, F., Schnörr, C., Mórken, K., and Lysaker, M., “Convex Multi-Class Image Labeling by Simplex-Constrained Total Variation”, in Scale Space and Variational Methods in Computer Vision (SSVM 2009), 2009, vol. 5567, pp. 150-162.
J. Lellmann, Becker, F., and Schnörr, C., “Convex Optimization for Multi-Class Image Labeling with a Novel Family of Total Variation Based Regularizers”, in Proceedings of the IEEE Conference on Computer Vision (ICCV 09) Kyoto, Japan, 2009, pp. 646-653.
J. Lellmann, Becker, F., and Schnörr, C., “Convex Optimization for Multi-Class Image Labeling with a Novel Family of Total Variation Based Regularizers”, in IEEE International Conference on Computer Vision (ICCV), 2009, p. 646 -- 653.PDF icon Technical Report (930.18 KB)
F. Silvestri, Reinelt, G., and Schnörr, C., “A Convex Relaxation Approach to the Affine Subspace Clustering Problem”, in Proc.~GCPR, 2015.PDF icon Technical Report (878.63 KB)
J. Keuchel, Schellewald, C., Cremers, D., and Schnörr, C., “Convex Relaxations for Binary Image Partitioning and Perceptual Grouping”, in Mustererkennung 2001, Munich, Germany, 2001, vol. 2191, pp. 353–360.
J. Yuan, Schnörr, C., Kohlberger, T., and Ruhnau, P., “Convex Set-Based Estimation of Image Flows”, in ICPR 2004 – 17th Int. Conf. on Pattern Recognition, Cambridge, UK, 2004, vol. 1, pp. 124-127.
P. Swoboda and Schnörr, C., “Convex Variational Image Restoration with Histogram Priors”, SIAM J.~Imag.~Sci., vol. 6, pp. 1719-1735, 2013.PDF icon Technical Report (553.54 KB)

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