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Journal Article
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.
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.
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)
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)
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)
S. Petra, Schnörr, C., and Schröder, A., “Critical Parameter Values and Reconstruction Propertiesof Discrete Tomography: Application to Experimental FluidDynamics”, Fundamenta Informaticae, vol. 125, p. 285--312, 2013.PDF icon Technical Report (1.42 MB)
D. Cremers, Tischhäuser, F., Weickert, J., and Schnörr, C., “Diffusion Snakes: Introducing Statistical Shape Knowledge into the Mumford–Shah functional”, Int. J. Computer Vision, vol. 50, pp. 295–313, 2002.
J. Lellmann, Lellmann, B., Widmann, F., and Schnörr, C., “Discrete and Continuous Models for Partitioning Problems”, Int.~J.~Comp.~Visionz, vol. 104, pp. 241-269, 2013.PDF icon Technical Report (4.74 MB)
J. Yuan, Schnörr, C., and Mémin, E., “Discrete Orthogonal Decomposition and Variational Fluid Flow Estimation”, J.~Math.~Imag.~Vision, vol. 28, pp. 67-80, 2007.PDF icon Technical Report (752.44 KB)
K. Rohr and Schnörr, C., “An Efficient Approach to the Identification of Characteristic Intensity Variations”, vol. 11, pp. 273–277, 1993.
B. Savchynskyy, Schmidt, S., Kappes, J. H., and Schnörr, C., “Efficient MRF Energy Minimization via Adaptive Diminishing Smoothing”, UAI. Proceedings, pp. 746-755, 2012.
C. Schellewald, Roth, S., and Schnörr, C., “Evaluation of a convex relaxation to a quadratic assignment matching approach for relational object views”, Image Vision Comp., vol. 25, p. 1301--1314, 2007.PDF icon Technical Report (439.9 KB)
A. Nicola, Petra, S., Popa, C., and Schnörr, C., “A general extending and constraining procedure for linear iterative methods”, Int.~J.~Comp.~Math., 2011.PDF icon Technical Report (633.79 KB)
B. Schmitzer and Schnörr, C., “Globally Optimal Joint Image Segmentation and Shape Matching based on Wasserstein Modes”, J.~Math.~Imag.~Vision, vol. 52, p. 436--458, 2015.PDF icon Technical Report (1.97 MB)
A. Bruhn, Jakob, T., Fischer, M., Weickert, J., Brüning, U., and Schnörr, C., “High performance cluster computing with 3-D nonlinear diffusion filters”, Real-Time Imaging, vol. 10, pp. 41–51, 2004.
J. Gall, Potthoff, J., Schnörr, C., Rosenhahn, B., and Seidel, H. - P., “Interacting and Annealing Particle Filters: Mathematics and a Recipe for Applications”, J.~Math.~Imag.~Vision, vol. 28, p. 1--18, 2007.PDF icon Technical Report (1.11 MB)
J. Gall, Potthoff, J., Schnörr, C., Rosenhahn, B., and Seidel, H. - P., “Interacting and Annealing Particle Filters: Mathematics and a Recipe for Applications”, J. Math. Imag. Vision, vol. 28, pp. 1–18, 2007.
A. Bruhn, Weickert, J., and Schnörr, C., “Lucas/Kanade Meets Horn/Schunck: Combining Local and Global Optic Flow Methods”, vol. 61, pp. 211-231, 2005.
M. Welk, Weickert, J., Becker, F., Schnörr, C., Feddern, C., and Burgeth, B., “Median and related local filters for tensor-valued images”, Signal Processing, vol. 87, pp. 291-308, 2007.PDF icon Technical Report (1007.29 KB)
D. Breitenreicher and Schnörr, C., “Model-Based Multiple Rigid Object Detection and Registration in Unstructured Range Data”, Int.~J.~Comp.~Vision, vol. 92, p. 32--52, 2011.PDF icon Technical Report (4.3 MB)
D. Breitenreicher and Schnörr, C., “Model-Based Multiple Rigid Object Detection and Registration in Unstructured Range Data”, Int. J. Comp. Vision, vol. 92, pp. 32–52, 2011.
B. Schmitzer and Schnörr, C., “Modelling convex shape priors and matching based on the Gromov-Wasserstein distance”, Journal of Mathematical Imaging and Vision, vol. 46, pp. 143-159, 2013.PDF icon Technical Report (957.78 KB)
B. Schmitzer and Schnörr, C., “Modelling convex shape priors and matching based on the Gromov-Wasserstein distance”, Journal of Mathematical Imaging and Vision, vol. 46, pp. 143-159, 2013.
J. Hendrik Kappes, Swoboda, P., Savchynskyy, B., Hazan, T., and Schnörr, C., “Multicuts and Perturb & MAP for Probabilistic Graph Clustering”, Journal of Mathematical Imaging and Vision, vol. 56, pp. 221–237, 2016.
A. Bruhn, Weickert, J., Kohlberger, T., and Schnörr, C., “A Multigrid Platform for Real-Time Motion Computation with Discontinuity-Preserving Variational Methods”, Int.~J.~Computer Vision, vol. 70, pp. 257-277, 2006.PDF icon Technical Report (447.65 KB)
A. Bruhn, Weickert, J., Kohlberger, T., and Schnörr, C., “A Multigrid Platform for Real-Time Motion Computation with Discontinuity-Preserving Variational Methods”, Int. J. Computer Vision, vol. 70, pp. 257-277, 2006.
D. Cremers, Sochen, N., and Schnörr, C., “Multiphase Dynamic Labeling for Variational Recognition-Driven Image Segmentation”, ijcv, vol. 66, pp. 67-81, 2006.
M. Zisler, Kappes, J. H., Schnörr, C., Petra, S., and Schnörr, C., “Non-Binary Discrete Tomography by Continuous Non-Convex Optimization”, IEEE Comp. Imaging, vol. 2, pp. 335-347, 2016.
P. Ruhnau and Schnörr, C., “Optical Stokes Flow Estimation: An Imaging-Based Control Approach”, Exp.~in Fluids, vol. 42, p. 61--78, 2007.PDF icon Technical Report (1.54 MB)
J. Lellmann, Lenzen, F., and Schnörr, C., “Optimality Bounds for a Variational Relaxation of the Image Partitioning Problem”, Journal of Mathematical Imaging and Vision, vol. 47 (3), pp. 239-257, 2013.
J. Lellmann, Lenzen, F., and Schnörr, C., “Optimality Bounds for a Variational Relaxation of the Image Partitioning Problem”, Journal of Mathematical Imaging and Vision, vol. 47, pp. 239-257, 2012.PDF icon Technical Report (616.16 KB)

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