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Book

F. A. Hamprecht, Jähne, B., and Schnörr, C., Eds., Pattern Recognition, 29th DAGM Symposium, Heidelberg, Germany, September 12-14, 2007, Proceedings, vol. 4713. Springer, 2007.

Conference Paper

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.

Technical Report (611.33 KB)

C. Kondermann, Kondermann, D., Jähne, B., Garbe, C. S., Schnörr, C., and Jähne, B., “An adaptive confidence measure for optical flows based on linear subspace projections”, in Proceedings of the 29th DAGM Symposium on Pattern Recognition, 2007, vol. 4713, p. 132--141.

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.

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.

S. Weber, Nagy, A., Schüle, T., Schnörr, C., and Kuba, A., “A Benchmark Evaluation of Large-Scale Optimization Approaches to Binary Tomography”, in Discrete Geometry for Computer Imagery (DGCI 2006), 2006, vol. 4245, pp. 146-156.

Technical Report (301.1 KB)

S. Weber, Schüle, T., Schnörr, C., and Kuba, A., “Binary Tomography with Deblurring”, in Combinatorial Image Analysis, 2006, vol. 4040, pp. 375-388.

Technical Report (803.63 KB)

S. Petra, Schnörr, C., Becker, F., and Lenzen, F., “B-SMART: Bregman-Based First-Order Algorithms for Non-Negative Compressed Sensing Problems”, in Proceedings of the 4th International Conference on Scale Space and Variational Methods in Computer Vision SSVM, 2013, pp. 110-124.

S. Petra, Schnörr, C., Becker, F., and Lenzen, F., “B-SMART: Bregman-Based First-Order Algorithms for Non-Negative Compressed Sensing Problems”, in Proceedings of the 4th International Conference on Scale Space and Variational Methods in Computer Vision (SSVM) 2013, 2013, vol. 7893, pp. 110-124.

Technical Report (1.15 MB)

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)

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.

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.

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)

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)

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)

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)

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. 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., 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, 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, 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.

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)

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)

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

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.

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.

D. Cremers, Schnörr, C., Weickert, J., and Schellewald, C., “Diffusion Snakes Using Statistical Shape Knowledge”, in Proc. Algebraic Frames for the Perception-Action Cycle, Kiel, 2000, vol. 1888, pp. 164–174.

D. Cremers, Schnörr, C., and Weickert, J., “Diffusion–Snakes: Combining Statistical Shape Knowledge and Image Information in a Variational Framework”, in IEEE First Workshop on Variational and Level Set Methods in Computer Vision, Vancouver, Canada, 2001, pp. 237–244.

A. Bruhn, Weickert, J., Kohlberger, T., and Schnörr, C., “Discontinuity-Preserving Computation of Variational Optic Flow in Real-Time”, in Scale-Space 2005, 2005, vol. 3459, pp. 279–290.

M. Zisler, Petra, S., Schnörr, C., and Schnörr, C., “Discrete Tomography by Continuous Multilabeling Subject to Projection Constraints”, in Proc. GCPR, 2016.

A. Stahl, Ruhnau, P., and Schnörr, C., “A Distributed Parameter Approach to Dynamic Image Motion”, in ECCV 2006, International Workshop on The Representation and Use of Prior Knowledge in Vision, 2006.

Technical Report (1.24 MB)

B. Savchynskyy, Schmidt, S., Kappes, J. H., and Schnörr, C., “Efficient MRF Energy Minimization via Adaptive Diminishing Smoothing”, in UAI 2012, 2012.

Technical Report (529 KB)

B. Andres, Kappes, J. H., Köthe, U., Schnörr, C., and Hamprecht, F. A., “An Empirical Comparison of Inference Algorithms for Graphical Models with Higher Order Factors Using OpenGM”, in Pattern Recognition, Proc.~32th DAGM Symposium, 2010, pp. 353-362.

B. Andres, Kappes, J. H., Köthe, U., Schnörr, C., and Hamprecht, F. A., “An Empirical Comparison of Inference Algorithms for Graphical Models with Higher Order Factors Using OpenGM”, in Pattern Recognition, Proc.~32th DAGM Symposium, 2010.

Technical Report (218.43 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, p. 262--274.

Technical Report (894.83 KB)

Pages