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Journal Article
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)
F. Savarino and Schnörr, C., “Continuous-Domain Assignment Flows”, preprint: arXiv, 2019.
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)
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.PDF icon 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.PDF icon Technical Report (4.28 MB)
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)
F. Rathke and Schnörr, C., “A Computational Approach to Log-Concave Density Estimation”, An. St. Univ. Ovidius Constanta, vol. 23, pp. 151-166, 2015.PDF icon Technical Report (1.07 MB)
F. Rathke and Schnörr, C., “A Computational Approach to Log-Concave Density Estimation”, An. St. Univ. Ovidius Constanta, vol. 23, pp. 151-166, 2015.
C. Schnörr, “Computation of Discontinuous Optical Flow by Domain Decomposition and Shape Optimization”, ijcv, vol. 8, pp. 153–165, 1992.
B. H. Menze, Kelm, B. Michael, Masuch, R., Himmelreich, U., Bachert, P., Petrich, W., and Hamprecht, F. A., “A Comparison of Random Forest and its Gini Importance with Standard Chemometric Methods for the Feature Selection and Classification of Spectral Data”, BMC Bioinformatics, vol. 10:213, 2009.PDF icon Technical Report (675 KB)
M. Marxen, Sullivan, P. E., Loewen, M. R., and Jähne, B., “Comparison of Gaussian particle center estimators and the achievable measurement density for particle tracking velocimetry”, Exp. Fluids, vol. 29, pp. 145-153, 2000.
C. Weber, Zechmann, C. M., Kelm, B. Michael, Zamecnik, R., Hendricks, D., Waldherr, R., Hamprecht, F. A., Delorme, S., Bachert, P., and Ikinger, U., “Comparison of correctness of manuel and automatic evaluation of MR-spectrum with prostrate cancer”, Der Urologe, vol. 46, p. 1252, 2007.
P. A. Lange, Jähne, B., Tschiersch, J., and Ilmberger, J., “Comparison between an amplitude-measuring wire and a slope-measuring laser water wave gauge”, Rev. Sci. Instrum., vol. 53, p. 651--655, 1982.
J. H. Kappes, Andres, B., Hamprecht, F. A., Schnörr, C., Nowozin, S., Batra, D., Kim, S., Kausler, B. X., Kröger, T., Lellmann, J., Komodakis, N., Savchynskyy, B., and Rother, C., “A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems”, Int.~J.~Comp.~Vision, 2015.PDF icon Technical Report (5.12 MB)
J. H. Kappes, Andres, B., Hamprecht, F. A., Schnörr, C., Nowozin, S., Batra, D., Kim, S., Kausler, B. X., Kröger, T., Lellmann, J., Komodakis, N., Savchynskyy, B., and Rother, C., “A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems”, CoRR, vol. abs/1404.0533, 2014.PDF icon Technical Report (3.32 MB)
J. H. Kappes, Andres, B., Hamprecht, F. A., Schnörr, C., Nowozin, S., Batra, D., Kim, S., Kausler, B. X., Kröger, T., Lellmann, J., Komodakis, N., Savchynskyy, B., and Rother, C., “A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems”, International Journal of Computer Vision, vol. 115, pp. 155–184, 2015.
J. H. Kappes, Andres, B., Hamprecht, F. A., Schnörr, C., Nowozin, S., Batra, D., Kim, S., Kausler, B. X., Kröger, T., Lellmann, J., Komodakis, N., Savchynskyy, B., and Rother, C., “A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems”, International Journal of Computer Vision, vol. 115, pp. 155–184, 2015.
J. H. Kappes, Andres, B., Hamprecht, F. A., Schnörr, C., Nowozin, S., Batra, D., Kim, S., Kausler, B. X., Kröger, T., Lellmann, J., Komodakis, N., Savchynskyy, B., and Rother, C., “A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems”, International Journal of Computer Vision, vol. 115, pp. 155–184, 2015.
J. H. Kappes, Andres, B., Hamprecht, F. A., Schnörr, C., Nowozin, S., Batra, D., Kim, S., Kausler, B. X., Kröger, T., Lellmann, J., Komodakis, N., Savchynskyy, B., and Rother, C., “A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems”, International Journal of Computer Vision, pp. 1-30, 2015.PDF icon Technical Report (1.5 MB)
J. H. Kappes, Andres, B., Hamprecht, F. A., Schnörr, C., Nowozin, S., Batra, D., Kim, S., Kausler, B. X., Kröger, T., Lellmann, J., Komodakis, N., Savchynskyy, B., and Rother, C., “A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems”, CoRR, 2014.
R. Szeliski, Zabih, R., Scharstein, D., Veksler, O., Kolmogorov, V., Agarwala, A., Tappen, M., and Rother, C., “A comparative study of energy minimization methods for Markov random fields with smoothness-based priors”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 30, pp. 1068–1080, 2008.
R. Szeliski, Zabih, R., Scharstein, D., Veksler, O., Kolmogorov, V., Agarwala, A., Tappen, M., and Rother, C., “A comparative study of energy minimization methods for Markov random fields with smoothness-based priors”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 30, pp. 1068–1080, 2008.
L. Nagel, Krall, K. Ellen, and Jähne, B., “Comparative heat and gas exchange measurements in the Heidelberg Aeolotron, a large annular wind-wave tank”, Ocean Sci., vol. 11, p. 111--120, 2015.
L. Nagel, Krall, K. Ellen, and Jähne, B., “Comparative heat and gas exchange measurements in the Heidelberg Aeolotron, a large annular wind-wave tank”, Ocean Sci. Discuss., vol. 11, p. 1691--1718, 2014.
M. Baust, Weinmann, A., Wieczorek, M., Lasser, T., Storath, M., and Navab, N., “Combined Tensor Fitting and TV Regularization in Diffusion Tensor Imaging based on a Riemannian Manifold Approach”, IEEE Transactions on Medical Imaging, vol. 35, no. 8, pp. 1972–1989, 2016.PDF icon Technical Report (8.65 MB)
J. Neumann, Schnörr, C., and Steidl, G., “Combined SVM-based Feature Selection and Classification”, Machine Learning, vol. 61, pp. 129-150, 2005.
B. Jähne, Schmidt, M., and Rocholz, R., “Combined optical slope/height measurements of short wind waves: principles and calibration”, Meas. Sci. Technol., vol. 16, p. 1937--1944, 2005.
D. Breitenreicher, Lellmann, J., and Schnörr, C., “COAL: a generic modelling and prototyping framework for convex optimization problems of variational image analysis”, Optimization Methods and Software, vol. 28, pp. 1081-1094, 2013.PDF icon Technical Report (1.69 MB)
M. Geese, Jähne, B., and Ruhnau, P., “CNN Based Dark Signal Non-Uniformity Estimation”, CNNA, pp. 1-6, 2012.
F. Lenzen, Becker, F., Lellmann, J., Petra, S., and Schnörr, C., “A Class of Quasi-Variational Inequalities for Adaptive Image Denoising and Decomposition”, Computational Optimization and Applications (COAP), vol. 54 (2), pp. 371-398, 2013.
F. Lenzen, Becker, F., Lellmann, J., Petra, S., and Schnörr, C., “A class of quasi-variational inequalities for adaptive image denoising and decomposition”, Computational Optimization and Applications, vol. 54, pp. 371-398, 2013.PDF icon Technical Report (748.66 KB)
F. A. Hamprecht, Thiel, W., and van Gunsteren, W. F., “Chemical library subset selection algorithms: a unified derivation using spatial statistics”, Journal of Chemical Information and Computer Sciences, vol. 42, pp. 414-428, 2002.

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