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2013
F. Becker, Lenzen, F., Kappes, J. H., and Schnörr, C., “Variational Recursive Joint Estimation of Dense Scene Structure and Camera Motion from Monocular High Speed Traffic Sequences”, International Journal of Computer Vision, vol. 105, pp. 269–297, 2013.
2014
S. Petra and Schnörr, C., “Average Case Recovery Analysis of Tomographic Compressive Sensing”, Linear Algebra and its Applications, vol. 441, pp. 168-198, 2014.PDF icon Technical Report (1.85 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”, CoRR, vol. abs/1404.0533, 2014.PDF icon Technical Report (3.32 MB)
A. Denitiu, Petra, S., Schnörr, C., and Schnörr, C., “An Entropic Perturbation Approach to TV-Minimization for Limited-Data Tomography”, in Discrete Geometry for Computer Imagery (DGCI) 2014, 2014, p. 262--274.PDF icon Technical Report (894.83 KB)
A. Denitiu, Petra, S., Schnörr, C., and Schnörr, C., “An Entropic Perturbation Approach to TV-Minimization for Limited-Data Tomography”, in Discrete Geometry for Computer Imagery (DGCI) 2014, 2014, pp. 262–274.
B. Schmitzer and Schnörr, C., “Globally Optimal Joint Image Segmentation and Shape Matching based on Wasserstein Modes”. 2014.PDF icon Technical Report (2.9 MB)
B. Schmitzer and Schnörr, C., “Globally Optimal Joint Image Segmentation and Shape Matching based on Wasserstein Modes”. 2014.
J. H. Kappes, Beier, T., and Schnörr, C., “MAP-Inference on Large Scale Higher-Order Discrete Graphical Models by Fusion Moves”, in Computer Vision - {ECCV} 2014 Workshops - Zurich, Switzerland, September 6-7 and 12, 2014, Proceedings, Part {II}, 2014.PDF icon Technical Report (557.49 KB)
J. Hendrik Kappes, Beier, T., and Schnörr, C., “MAP-Inference on Large Scale Higher-Order Discrete Graphical Models by Fusion Moves”, in International Workshop on Graphical Models in Computer Vision, 2014.
F. Becker, Petra, S., and Schnörr, C., “Optical Flow”, Handbook of Mathematical Methods in Imaging. Springer, 2014.
P. Swoboda, Savchynskyy, B., Kappes, J. H., and Schnörr, C., “Partial Optimality by Pruning for MAP-inference with General GraphicalModels”, CVPR. Proceedings. pp. 1170-1177, 2014.
P. Swoboda, Savchynskyy, B., Kappes, J. H., and Schnörr, C., “Partial Optimality by Pruning for MAP-inference with General Graphical Models”, in IEEE Conference on Computer Vision and Pattern Recognition 2014, 2014.PDF icon Technical Report (703.34 KB)
P. Swoboda, Savchynskyy, B., Kappes, J. H., and Schnörr, C., “Partial Optimality by Pruning for MAP-inference with General Graphical Models”, in IEEE Conference on Computer Vision and Pattern Recognition 2014, 2014.
A. Denitiu, Petra, S., Schnörr, C., and Schnörr, C., “Phase Transitions and Cosparse Tomographic Recovery of Compound Solid Bodies from Few Projections”, Fundamenta Informaticae, vol. 135, p. 73--102, 2014.PDF icon Technical Report (2.24 MB)
A. Denitiu, Petra, S., Schnörr, C., and Schnörr, C., “Phase Transitions and Cosparse Tomographic Recovery of Compound Solid Bodies from Few Projections”, Fundamenta Informaticae, vol. 135, pp. 73–102, 2014.
F. Rathke, Schmidt, S., and Schnörr, C., “Probabilistic Intra-Retinal Layer Segmentation in 3-D OCT Images Using Global Shape Regularization”, Med. Image Anal., vol. 18, pp. 781–794, 2014.
F. Rathke, Schmidt, S., and Schnörr, C., “Probabilistic Intra-Retinal Layer Segmentation in 3-D OCT Images Using Global Shape Regularization”, Medical Image Analysis, vol. 18, pp. 781-794, 2014.PDF icon Technical Report (4.07 MB)
F. Lenzen, Lellmann, J., Becker, F., and Schnörr, C., “Solving Quasi-Variational Inequalities for Image Restoration with Adaptive Constraint Sets”, SIAM J.~Imag.~Sci., vol. 7, p. 2139--2174, 2014.PDF icon Technical Report (802.13 KB)
F. Lenzen, Lellmann, J., Becker, F., and Schnörr, C., “Solving QVIs for Image Restoration with Adaptive Constraint Sets”, SIAM Journal on Imaging Sciences (SIIMS), in press, 2014.
2015
A. Biesdorf, Wörz, S., von Tengg-Kobligk, H., Rohr, K., and Schnörr, C., “3D Segmentation of Vessels by Incremental Implicit Polynomial Fitting and Convex Optimization”, in Proc.~ISBI, 2015.PDF icon Technical Report (611.33 KB)
E. Bodnariuc, Gurung, A., Petra, S., and Schnörr, C., “Adaptive Dictionary-Based Spatio-temporal Flow Estimation for Echo PIV”, in Proc.~EMMCVPR, 2015, vol. 8932, p. 378--391.PDF icon Technical Report (951.37 KB)
E. Bodnariuc, Gurung, A., Petra, S., and Schnörr, C., “Adaptive Dictionary-Based Spatio-temporal Flow Estimation for Echo PIV”, in EMMCVPR, 2015.
N. Gianniotis, Schnörr, C., Molkenthin, C., and Bora, S. S., “Approximate variational inference based on a finite sample of Gaussian latent variables”, Patt.~Anal.~Appl., 2015.PDF icon Technical Report (1.4 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, 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”, 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”, 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.
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)
A. Neufeld, Berger, J., Becker, F., Lenzen, F., and Schnörr, C., “Estimating Vehicle Ego-Motion and Piecewise Planar Scene Structure from Optical Flow in a Continuous Framework”, in 37th German Conference on Pattern Recognition, 2015.
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)
J. H. Kappes, Swoboda, P., Savchynskyy, B., Hazan, T., and Schnörr, C., “Probabilistic Correlation Clustering and Image Partitioning Using Perturbed Multicuts”, in Proc.~SSVM, 2015.PDF icon Technical Report (1.1 MB)

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