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B. Maco, Holtmaat, A., Cantoni, M., Kreshuk, A., Straehle, C. N., Hamprecht, F. A., and Knott, G. W., “Correlative in vivo 2 photon and focused ion beam scanning electron microscopy of cortical neurons”, PloS one, vol. 8 (2), 2013.

Technical Report (2.13 MB)

G. Krause, “Correlation of Performance and Entropy in Active Learning with Convolutional Neural Networks”, Heidelberg University, 2017.

M. Hering, Körner, K., and Jähne, B., “Correlated speckle noise in white-light interferometry: theoretical analysis of measurement uncertainty”, Appl. Optics, vol. 48, p. 525--538, 2009.

L. A. Royer, Richmond, D. L., Rother, C., Andres, B., and Kainmueller, D., “Convexity shape constraints for image segmentation”, in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016, vol. 2016-Decem, pp. 402–410.

C. Schnörr, “Convex Variational Segmentation of Multi-Channel Images”, in Proc. 12th Int. Conf. on Analysis and Optimization of Systems: Images, Wavelets and PDE's, Paris, 1996, vol. 219.

P. Swoboda and Schnörr, C., “Convex Variational Image Restoration with Histogram Priors”, SIAM J.~Imag.~Sci., vol. 6, pp. 1719-1735, 2013.

Technical Report (553.54 KB)

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.

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.

F. Silvestri, Reinelt, G., and Schnörr, C., “A Convex Relaxation Approach to the Affine Subspace Clustering Problem”, in Proc.~GCPR, 2015.

Technical Report (878.63 KB)

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.

Technical Report (930.18 KB)

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, 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, 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.

Technical Report (1.75 MB)

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.

Technical Report (2.6 MB)

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.

Technical Report (290.72 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.

Technical Report (1003.75 KB)

M. Heiler and Schnörr, C., “Controlling Sparseness in Non-negative Tensor Factorization”, in Computer Vision -- ECCV 2006, 2006, vol. 3951, pp. 56-67.

Technical Report (568.86 KB)

J. Schlecht and Ommer, B., “Contour-based Object Detection”, in BMVC, 2011, p. 1--9.

Technical Report (2.62 MB)

C. Gosch, “Contour Methods for View Point Tracking”. University of Heidelberg, 2009.

B. Schmitzer and Schnörr, C., “Contour Manifolds and Optimal Transport”. 2013.

F. Savarino and Schnörr, C., “Continuous-Domain Assignment Flows”, preprint: arXiv, 2019.

F. Lauer, Bloch, G., and Vidal, R., “A Continuous Optimization Framework for Hybrid System Identification”, in submitted to Automatica, 2009.

J. Lellmann and Schnörr, C., “Continuous Multiclass Labeling Approaches and Algorithms”, SIAM J.~Imag.~Sci., vol. 4, pp. 1049-1096, 2011.

Technical Report (4.31 MB)

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”, Univ. of Heidelberg, 2010.

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.

Technical Report (414.89 KB)

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.

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.

R. Nair, “Construction and analysis of random tree ensembles”, University of Heidelberg, 2010.

M. Schiegg, Hanslovsky, P., Kausler, B. X., Hufnagel, L., and Hamprecht, F. A., “Conservation Tracking”, in ICCV 2013. Proceedings, 2013, p. 2928--2935.

Technical Report (5.22 MB)

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 (9.95 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.

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.

Technical Report (3.91 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. 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.

Technical Report (4.28 MB)

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