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D

P. Fornland and Schnörr, C., “Determining the Dominant Plane from Uncalibrated Stereo Vision by a Robust and Convergent Iterative Approach without Correspondence”, in Proc. Int. Conf. Comp. Vision and Patt. Recog. (CVPR'97), San Juan, Puerto Rico, 1997.

A. Bruhn, Jakob, T., Fischer, M., Kohlberger, T., Weickert, J., Brüning, U., and Schnörr, C., “Designing 3–D Nonlinear Diffusion Filters for High Performance Cluster Computing”, in Pattern Recognition, Proc. 24th DAGM Symposium, Zürich, Switzerland, 2002, vol. 2449, pp. 290–297.

F. Becker and Schnörr, C., “Decomposition of Quadratric Variational Problems”, in Pattern Recognition -- 30th DAGM Symposium, 2008, vol. 5096, p. 325--334.

F. Becker and Schnörr, C., “Decomposition of Quadratric Variational Problems”, in Pattern Recognition -- 30th DAGM Symposium, 2008, vol. 5096, p. 325--334.

Technical Report (1.29 MB)

C

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.

Technical Report (1.42 MB)

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)

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 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.

Technical Report (930.18 KB)

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)

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

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)

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)

F. Rathke and Schnörr, C., “A Computational Approach to Log-Concave Density Estimation”, An. St. Univ. Ovidius Constanta, vol. 23, pp. 151-166, 2015.

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. 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.

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.

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.

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.

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., Sungwoong, K., Kausler, B. X., Lellmann, J., Komodakis, N., and Rother, C., “A Comparative Study of Modern Inference Techniques for Discrete Energy Minimization Problems”, in CVPR 2013. Proceedings, 2013.

Technical Report (1.35 MB)

J. H. Kappes, Andres, B., Hamprecht, F. A., Schnörr, C., Nowozin, S., Batra, D., Kim, S., Kausler, B. X., Lellmann, J., Komodakis, N., and Rother, C., “A Comparative Study of Modern Inference Techniques for Discrete Energy Minimization Problem”, in CVPR, 2013.

Technical Report (1.35 MB)

A. Bruhn, Weickert, J., and Schnörr, C., “Combining the Advantages of Local and Global Optic Flow Methods”, in Pattern Recognition, Proc. 24th DAGM Symposium, Zürich, Switzerland, 2002, vol. 2449, pp. 454–462.

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.

Technical Report (1.69 MB)

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.

Technical Report (748.66 KB)

B

J. H. Kappes, Savchynskyy, B., and Schnörr, C., “A Bundle Approach To Efficient MAP-Inference by Lagrangian Relaxation”, in CVPR. Proceedings, 2012, pp. 1688-1695.

J. H. Kappes, Savchynskyy, B., and Schnörr, C., “A Bundle Approach To Efficient MAP-Inference by Lagrangian Relaxation”, in CVPR, 2012.

Technical Report (430.63 KB)

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