Publications

Export 277 results:
Author [ Title(Asc)] Type Year
Filters: Author is Christoph Schnörr  [Clear All Filters]
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z 
F
Lellmann, J, Breitenreicher, D and Schnörr, C (2010). Fast and Exact Primal-Dual Iterations for Variational Problems in Computer Vision. European Conference on Computer Vision (ECCV). Springer Berlin / Heidelberg. 6312 494--505PDF icon Technical Report (1.94 MB)
E
Petra, S, Popa, C and Schnörr, C (2008). Extended And Constrained Cimmino-Type Algorithms With Applications In Tomographic Image Reconstruction. IWR, University of Heidelberg. http://www.ub.uni-heidelberg.de/archiv/8798/PDF icon Technical Report (2.13 MB)
Schellewald, C, Roth, S and Schnörr, C (2001). Evaluation of Convex Optimization Techniques for the Weighted Graph--Matching Problem in Computer Vision. Mustererkennung 2001. Springer. 2191 361--368
Schmidt, S, Savchynskyy, B, Kappes, J H and Schnörr, C (2011). Evaluation of a First-Order Primal-Dual Algorithm for MRF Energy Minimization. EMMCVPR. Springer. 6819 89-103PDF icon Technical Report (684.13 KB)
Schmidt, S, Savchynskyy, B, Kappes, J H and Schnörr, C (2011). Evaluation of a First-Order Primal-Dual Algorithm for MRF Energy Minimization. EMMCVPR 2011. Springer. 6819 89-103
Schellewald, C, Roth, S and Schnörr, C (2007). Evaluation of a convex relaxation to a quadratic assignment matching approach for relational object views. Image Vision Comp. 25 1301--1314PDF icon Technical Report (439.9 KB)
Neufeld, A, Berger, J, Becker, F, Lenzen, F and Schnörr, C (2015). Estimating Vehicle Ego-Motion and Piecewise Planar Scene Structure from Optical Flow in a Continuous Framework. 37th German Conference on Pattern Recognition. Springer, Aachen
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--274PDF icon Technical Report (894.83 KB)
Petra, S, Popa, C and Schnörr, C (2008). Enhancing Sparsity by Constraining Strategies: Constrained SIRT versus Spectral Projected Gradient Methods. Proc. 7th Workshop on Modelling of Environmental and Life Sciences Problems (WMM 08)
Petra, S, Popa, C and Schnörr, C (2008). Enhancing Sparsity by Constraining Strategies: Constrained SIRT versus Spectral Projected Gradient Methods. Proc.~7th Workshop on Modelling of Environmental and Life Sciences Problems (WMM~08). Ed Acad Romane, Bucuresti
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)
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
Schnörr, C and Neumann, B (1992). Ein Ansatz zur effizienten und eindeutigen Rekonstruktion stückweise glatter Funktionen. Mustererkennung 1992, 14. DAGM-Symposium. Springer-Verlag. 411--416
Neumann, J, Schnörr, C and Steidl, G (2005). Efficient Wavelet Adaption for Hybrid Wavelet-Large Margin Classifiers. Pattern Recognition. 38 1815-1830
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)
Savchynskyy, B, Schmidt, S, Kappes, J H and Schnörr, C (2012). Efficient MRF Energy Minimization via Adaptive Diminishing Smoothing. UAI. Proceedings. 746-755
Heiler, M, Cremers, D and Schnörr, C (2001). Efficient Feature Subset Selection For Support Vector Machines. Dept.~Math.~and Comp.~Science
Rohr, K and Schnörr, C (1993). An Efficient Approach to the Identification of Characteristic Intensity Variations. IVC. 11 273--277
Neumann, J, Schnörr, C and Steidl, G (2003). Effectively Finding The Optimal Wavelet For Hybrid Wavelet - Large Margin Signal Classification. Dept.~Math.~and Comp.~Science
D
Wiehler, K, Grigat, R -- R, Heers, J, Schnörr, C and Stiehl, H S (1998). Dynamic Circular Cellular Networks for Adaptive Smoothing of Multi--Dimensional Signals. Proc.~5th IEEE Int.~Workshop on Cellular Neural Networks and their Applications
Kohlberger, T, Schnörr, C, Bruhn, A and Weickert, J (2003). Domain Decomposition For Variational Optical Flow Computation. Dept.~Math.~and Comp.~Science
Kohlberger, T, Schnörr, C, Bruhn, A and Weickert, J (2005). Domain decomposition for variational optical flow computation. IEEE Trans.~Image Proc. 14 1125-1137
Kohlberger, T, Schnörr, C, Bruhn, A and Weickert, J (2003). Domain Decomposition for Parallel Variational Optical Flow Computation. Pattern Recognition, Proc.~25th DAGM Symposium. Springer. 2781 196--203
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)
Schüle, T, Schnörr, C, Weber, S and Hornegger, J (2003). Discrete Tomography By Convex-Concave Regularization And D.c.~Programming. Dept.~Math.~and Comp.~Science
Schüle, T, Schnörr, C, Weber, S and Hornegger, J (2005). Discrete Tomography By Convex-Concave Regularization and D.C.~Programming. Discr.~Appl.~Math. 151 229-243
Yuan, J, Schnörr, C and Mémin, E (2007). Discrete Orthogonal Decomposition and Variational Fluid Flow Estimation. J.~Math.~Imag.~Vision. 28 67-80PDF icon Technical Report (752.44 KB)
Yuan, J, Ruhnau, P, Mémin, E and Schnörr, C (2005). Discrete Orthogonal Decomposition and Variational Fluid Flow Estimation. Scale-Space 2005. Springer. 3459 267--278
Lellmann, J, Lellmann, B, Widmann, F and Schnörr, C (2013). Discrete and Continuous Models for Partitioning Problems. Int.~J.~Comp.~Visionz. 104 241-269PDF icon Technical Report (4.74 MB)
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
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. 237--244
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. 1888 164--174
Cremers, D, Tischhäuser, F, Weickert, J and Schnörr, C (2002). Diffusion Snakes: Introducing Statistical Shape Knowledge into the Mumford--Shah functional. Int.~J.~Computer Vision. 50 295--313
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)
Schnörr, (1991). Determining Optical Flow for Irregular Domains by Minimizing Quadratic Functionals of a Certain Class. IJCV. 6 25--38

Pages