Publications

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Conference Paper
Biesdorf, A, Wörz, S, von Tengg-Kobligk, H, Rohr, K and Schnörr, C (2015). 3D Segmentation of Vessels by Incremental Implicit Polynomial Fitting and Convex Optimization. Proc.~ISBIPDF icon Technical Report (611.33 KB)
Kondermann, C, Kondermann, D, Jähne, B, Garbe, C S, Schnörr, C and Jähne, B (2007). An adaptive confidence measure for optical flows based on linear subspace projections. Proceedings of the 29th DAGM Symposium on Pattern Recognition. Springer. 4713 132--141
Bodnariuc, E, Gurung, A, Petra, S and Schnörr, C (2015). Adaptive Dictionary-Based Spatio-temporal Flow Estimation for Echo PIV. EMMCVPR
Bodnariuc, E, Gurung, A, Petra, S and Schnörr, C (2015). Adaptive Dictionary-Based Spatio-temporal Flow Estimation for Echo PIV. Proc.~EMMCVPR. Springer. 8932 378--391PDF icon Technical Report (951.37 KB)
Weber, S, Nagy, A, Schüle, T, Schnörr, C and Kuba, A (2006). A Benchmark Evaluation of Large-Scale Optimization Approaches to Binary Tomography. Discrete Geometry for Computer Imagery (DGCI 2006). Springer. 4245 146-156PDF icon Technical Report (301.1 KB)
Weber, S, Schüle, T, Schnörr, C and Kuba, A (2006). Binary Tomography with Deblurring. Combinatorial Image Analysis. Springer. 4040 375-388PDF icon Technical Report (803.63 KB)
Petra, S, Schnörr, C, Becker, F and Lenzen, F (2013). B-SMART: Bregman-Based First-Order Algorithms for Non-Negative Compressed Sensing Problems. Proceedings of the 4th International Conference on Scale Space and Variational Methods in Computer Vision (SSVM) 2013. Springer. 7893 110-124PDF icon Technical Report (1.15 MB)
Petra, S, Schnörr, C, Becker, F and Lenzen, F (2013). B-SMART: Bregman-Based First-Order Algorithms for Non-Negative Compressed Sensing Problems. Proceedings of the 4th International Conference on Scale Space and Variational Methods in Computer Vision SSVM. 110-124
Kappes, J H, Savchynskyy, B and Schnörr, C (2012). A Bundle Approach To Efficient MAP-Inference by Lagrangian Relaxation. CVPRPDF icon Technical Report (430.63 KB)
Kappes, J H, Savchynskyy, B and Schnörr, C (2012). A Bundle Approach To Efficient MAP-Inference by Lagrangian Relaxation. CVPR. Proceedings. 1688-1695
Bruhn, A, Weickert, J and Schnörr, C (2002). Combining the Advantages of Local and Global Optic Flow Methods. Pattern Recognition, Proc. 24th DAGM Symposium. Springer, Zürich, Switzerland. 2449 454–462
Kappes, J H, Andres, B, Hamprecht, F A, Schnörr, C, Nowozin, S, Batra, D, Kim, S, Kausler, B X, Lellmann, J, Komodakis, N and Rother, C (2013). A Comparative Study of Modern Inference Techniques for Discrete Energy Minimization Problem. CVPRPDF icon Technical Report (1.35 MB)
Kappes, J H, Andres, B, Hamprecht, F A, Schnörr, C, Nowozin, S, Batra, D, Sungwoong, K, Kausler, B X, Lellmann, J, Komodakis, N and Rother, C (2013). A Comparative Study of Modern Inference Techniques for Discrete Energy Minimization Problems. CVPR 2013. ProceedingsPDF icon Technical Report (1.35 MB)
Fundana, K, Heyden, A, Gosch, C and Schnörr, C (2008). Continuous Graph Cuts for Prior-Based Object Segmentation. 19th Int.~Conf.~Patt.~Recog.~(ICPR). 1--4PDF icon Technical Report (414.89 KB)
Heiler, M and Schnörr, C (2006). Controlling Sparseness in Non-negative Tensor Factorization. Computer Vision -- ECCV 2006. Springer. 3951 56-67PDF icon Technical Report (568.86 KB)
Yuan, J, Steidl, G and Schnörr, C (2008). Convex Hodge Decomposition of Image Flows. Pattern Recognition -- 30th DAGM Symposium. Springer Verlag. 5096 416--425PDF icon Technical Report (290.72 KB)
Lellmann, J, Kappes, J H, Yuan, J, Becker, F, Schnörr, C, Mórken, K and Lysaker, M (2009). Convex Multi-Class Image Labeling by Simplex-Constrained Total Variation. Scale Space and Variational Methods in Computer Vision (SSVM 2009). Springer. 5567 150-162
Lellmann, J, Kappes, J H, Yuan, J, Becker, F and Schnörr, C (2009). Convex Multi-Class Image Labeling by Simplex-Constrained Total Variation. Scale Space and Variational Methods in Computer Vision (SSVM 2009). Springer. 5567 150-162PDF icon Technical Report (1.75 MB)
Lellmann, J, Becker, F and Schnörr, C (2009). Convex Optimization for Multi-Class Image Labeling with a Novel Family of Total Variation Based Regularizers. IEEE International Conference on Computer Vision (ICCV). 646 -- 653PDF icon Technical Report (930.18 KB)
Lellmann, J, Becker, F and Schnörr, C (2009). Convex Optimization for Multi-Class Image Labeling with a Novel Family of Total Variation Based Regularizers. Proceedings of the IEEE Conference on Computer Vision (ICCV 09) Kyoto, Japan. 646-653
Silvestri, F, Reinelt, G and Schnörr, C (2015). A Convex Relaxation Approach to the Affine Subspace Clustering Problem. Proc.~GCPRPDF icon Technical Report (878.63 KB)
Becker, F and Schnörr, C (2008). Decomposition of Quadratric Variational Problems. Pattern Recognition -- 30th DAGM Symposium. 5096 325--334
Becker, F and Schnörr, C (2008). Decomposition of Quadratric Variational Problems. Pattern Recognition -- 30th DAGM Symposium. Springer Verlag. 5096 325--334PDF icon Technical Report (1.29 MB)
Bruhn, A, Jakob, T, Fischer, M, Kohlberger, T, Weickert, J, Brüning, U and Schnörr, C (2002). Designing 3–D Nonlinear Diffusion Filters for High Performance Cluster Computing. Pattern Recognition, Proc. 24th DAGM Symposium. Springer, Zürich, Switzerland. 2449 290–297
Fornland, P and Schnörr, C (1997). Determining the Dominant Plane from Uncalibrated Stereo Vision by a Robust and Convergent Iterative Approach without Correspondence. Proc. Int. Conf. Comp. Vision and Patt. Recog. (CVPR'97). San Juan, Puerto Rico
Cremers, D, Schnörr, C, Weickert, J and Schellewald, C (2000). Diffusion Snakes Using Statistical Shape Knowledge. Proc. Algebraic Frames for the Perception-Action Cycle. Springer, Kiel. 1888 164–174
Cremers, D, Schnörr, C and Weickert, J (2001). Diffusion–Snakes: Combining Statistical Shape Knowledge and Image Information in a Variational Framework. IEEE First Workshop on Variational and Level Set Methods in Computer Vision. IEEE Comp. Soc., Vancouver, Canada. 237–244
Bruhn, A, Weickert, J, Kohlberger, T and Schnörr, C (2005). Discontinuity-Preserving Computation of Variational Optic Flow in Real-Time. Scale-Space 2005. Springer. 3459 279–290
Zisler, M, Petra, S, Schnörr, C and Schnörr, C (2016). Discrete Tomography by Continuous Multilabeling Subject to Projection Constraints. Proc. GCPR
Stahl, A, Ruhnau, P and Schnörr, C (2006). A Distributed Parameter Approach to Dynamic Image Motion. ECCV 2006, International Workshop on The Representation and Use of Prior Knowledge in Vision. LNCS, SpringerPDF icon Technical Report (1.24 MB)
Savchynskyy, B, Schmidt, S, Kappes, J H and Schnörr, C (2012). Efficient MRF Energy Minimization via Adaptive Diminishing Smoothing. UAI 2012PDF icon Technical Report (529 KB)
Andres, B, Kappes, J H, Köthe, U, Schnörr, C and Hamprecht, F A (2010). An Empirical Comparison of Inference Algorithms for Graphical Models with Higher Order Factors Using OpenGM. Pattern Recognition, Proc.~32th DAGM Symposium. 353-362
Andres, B, Kappes, J H, Köthe, U, Schnörr, C and Hamprecht, F A (2010). An Empirical Comparison of Inference Algorithms for Graphical Models with Higher Order Factors Using OpenGM. Pattern Recognition, Proc.~32th DAGM SymposiumPDF icon Technical Report (218.43 KB)
Denitiu, A, Petra, S, Schnörr, C and Schnörr, C (2014). An Entropic Perturbation Approach to TV-Minimization for Limited-Data Tomography. Discrete Geometry for Computer Imagery (DGCI) 2014. Springer. 262–274

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