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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.
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 Quasi-Variational Inequalities for Image Restoration with Adaptive Constraint Sets”, SIAM J. Imag. Sci., vol. 7, pp. 2139–2174, 2014.
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.
B. Krolla, Diebold, M., Goldlücke, B., and Stricker, D., “Spherical Light Fields”, in Proceedings of the British Machine Vision Conference, 2014.
L. Fiaschi, Diego, F., Grosser, K. - H., Schiegg, M., Köthe, U., Zlatic, M., and Hamprecht, F. A., “Tracking indistinguishable translucent objects over time using weakly supervised structured learning”, in CVPR. Proceedings, 2014, pp. 2736 - 2743.PDF icon Technical Report (1.47 MB)
S. Lenor, Martini, J., Jähne, B., Stopper, U., Weber, S., and Ohr, F., “Tracking-based visibility estimation”, in Pattern Recognition, 36th German Conference, GCPR 2014, Münster, Germany, September 2-5, 2014, 2014, vol. 8753, p. 365--376.
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”, 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”, 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.
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)
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)
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)
B. Schmitzer and Schnörr, C., “Globally Optimal Joint Image Segmentation and Shape Matching based on Wasserstein Modes”, J. Math. Imag. Vision, vol. 52, pp. 436–458, 2015.
B. Schmitzer and Schnörr, C., “Globally Optimal Joint Image Segmentation and Shape Matching based on Wasserstein Modes”, J. Math. Imag. Vision, vol. 52, pp. 436–458, 2015.
H. Abu Alhaija, Sellent, A., Kondermann, D., and Rother, C., “Graphflow—6D large displacement scene flow via graph matching”, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2015, vol. 9358, pp. 285–296.
M. Schiegg, Hanslovsky, P., Haubold, C., Köthe, U., Hufnagel, L., and Hamprecht, F. A., “Graphical Model for Joint Segmentation and Tracking of Multiple Dividing Cell”, Bioinformatics, vol. 31, no. 6, pp. 948-956, 2015.PDF icon Technical Report (534.29 KB)
A. Kirillov, Savchynskyy, B., Schlesinger, D., Vetrov, D., and Rother, C., “Inferring M-best diverse labelings in a single one”, in Proceedings of the IEEE International Conference on Computer Vision, 2015, vol. 2015 Inter, pp. 1814–1822.
A. Kirillov, Savchynskyy, B., Schlesinger, D., Vetrov, D., and Rother, C., “Inferring M-best diverse labelings in a single one”, in Proceedings of the IEEE International Conference on Computer Vision, 2015, vol. 2015 Inter, pp. 1814–1822.
K. Schelten, Nowozin, S., Jancsary, J., Rother, C., and Roth, S., “Interleaved regression tree field cascades for blind image deconvolution”, in Proceedings - 2015 IEEE Winter Conference on Applications of Computer Vision, WACV 2015, 2015, pp. 494–501.

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