Publications

2024

  1. Cassel, J.; Boll, B.; Petra, S.; Albers, P. and Schnörr, C. Sigma Flows for Image and Data Labeling and Learning Structured Prediction. In preprint arXiv:2408.15946, 2024. pdf  BibTeX
  2. Sorrenson, P.; Behrend-Uriarte, D.; Schnörr, C. and Köthe, U. Learning Distances from Data with Normalizing Flows and Score Matching. In preprint arXiv:2407.09297, 2024. pdf  BibTeX
  3. Sanmartín, E. F.; Schnörr, C. and Hamprecht, F.A. The Central Spanning Tree Problem. In preprint arXiv:2404.06447, 2024. pdf  BibTeX
  4. Savarino, F.; Albers, P. and Schnörr, C. On the Geometric Mechanics of Assignment Flows for Metric Data Labeling. In Information Geometry, 7: 1-31, 2024. pdf  BibTeX
  5. Boll, B.; Gonzalez-Alvarado, D.; Petra, S. and Schnörr, C. Generative Assignment Flows for Representing and Learning Joint Distributions of Discrete Data. In preprint arXiv:2406.04527, 2024. pdf  BibTeX
  6. Boll, B.; Gonzalez-Alvarado, D. and Schnörr, C. Generative Modeling of Discrete Joint Distributions by E-Geodesic Flow Matching on Assignent Manifolds. In preprint arXiv:2402.07846, 2024. pdf  BibTeX
  7. Draxler, F.; Wahl, S.; Schnörr, C. and Köthe, U. On the Universality of Coupling-based Normalizing Flows. In ICML (preprint arXiv:2402.06578), 2024. pdf  BibTeX
  8. Hans, M.; Kath, E.; Sparn, M.; Liebster, N.; Strobel, H.; Oberthaler, M. K.; Draxler, F. and Schnörr, C. Bose Einstein Condensate as Nonlinear Block of a Machine Learning Pipeline. In Physical Review Research, 6 (013122), 2024. pdf  BibTeX
  9. Boll, B.; Cassel, J.; Albers, P.; Petra, S. and Schnörr, C. A Geometric Embedding Approach to Multiple Games and Multiple Populations. In preprint arXiv:2401.05918, 2024. pdf  BibTeX

2023

  1. Boll, B. and Schnörr, C. On Certified Generalization in Structured Prediction. In NeurIPS, 2023. pdf  BibTeX
  2. Homeyer, C. and Schnörr, C. On Moving Object Segmentation from Monocular Video with Transformers. In Proc. IEEE/CVF International Conference on Computer Vision, pages 880-891, 2023. pdf  BibTeX
  3. Schwarz, J.; Cassel, J.; Boll, B.; Gärttner, M.; Albers, P. and Schnörr, C. Quantum State Assignment Flows. In Entropy, 25 (9): 1253, 2023. pdf  BibTeX
  4. Draxler, F.; Kühmichel, L.; Rousselot, A.; Müller, J.; Schnörr, C. and Köthe, U. On the Convergence Rate of Gaussianization with Random Rotations. In ICML, 2023. pdf  BibTeX
  5. Boll, B.; Zeilmann, A.; Petra, S. and Schnörr, C. Self-Certifying Classification by Linearized Deep Assignment. In PAMM: Proc. Appl. Math. Mech., 23 (1): e202200169, 2023. pdf  BibTeX
  6. Hans, M.; Kath, E.; Sparn, M.; Liebster, N.; Draxler, F.; Schnörr, C.; Strobel, H. and Oberthaler, M. K. Bose Einstein Condensate as Nonlinear Block of a Machine Learning Pipeline. In Physical Review Letters, in press (preprint arXiv:2304.14905), 2023. pdf  BibTeX
  7. Boll, B.; Schwarz, J.; Gonzalez-Alvarado, D.; Sitenko, D.; Petra, S. and Schnörr, C. Modeling Large-scale Joint Distributions and Inference by Randomized Assignment. In 9th Int. Conf. on Scale Space and Variational Methods in Computer Vision (SSVM), to appear, pages 730-742, LNCS 14009, 2023. pdf  BibTeX
  8. Schwarz, J.; Boll, B.; Sitenko, D.; Gonzalez-Alvarado, D.; Gärttner, M.; Albers, P. and Schnörr, C. Quantum State Assignment Flows. In 9th Int. Conf. on Scale Space and Variational Methods in Computer Vision (SSVM), to appear, pages 743-756, LNCS 14009, 2023. pdf  BibTeX
  9. Kahl, M. M.; Petra, S.; Schnörr, C.; Steidl, G. and Zisler, M. On the Remarkable Efficiency of the SMART Iteration. In 9th Int. Conf. on Scale Space and Variational Methods in Computer Vision (SSVM), to appear, pages 418-430, LNCS 14009, 2023. pdf  BibTeX
  10. Schnörr, D. and Schnörr, C. Learning System Parameters from Turing Patterns. In Machine Learning, 112: 3151–3190, 2023. pdf  BibTeX
  11. Sitenko, D.; Boll, B. and Schnörr, C. A Nonlocal Graph-PDE and Higher-Order Geometric Integration for Image Labeling. In SIAM J. Imaging Sciences, 16 (1): 501-567, 2023. pdf  BibTeX
  12. Zeilmann, A.; Petra, S. and Schnörr, C. Learning Linearized Assignment Flows for Image Labeling. In J. Math. Imag. Vision, 65: 164-184, 2023. pdf  BibTeX

2022

  1. Zern, A.; Zeilmann, A. and Schnörr, C. Assignment Flows for Data Labeling on Graphs: Convergence and Stability. In Information Geometry, 5: 355-404, 2022. pdf  BibTeX
  2. Draxler, F.; Schnörr, C. and Köthe, U. Whitening Convergence Rate of Coupling-based Normalizing Flows. In NeuIPS, 2022. pdf  BibTeX
  3. Sitenko, D.; Boll, B. and Schnörr, C. A Nonlocal Graph-PDE and Higher-Order Geometric Integration for Image Labeling. In arXiv, SIAM J. Imaging Sciences, in press, 2022. pdf  BibTeX
  4. Boll, B.; Zeilmann, A.; Petra, S. and Schnörr, C. Self-Certifying Classification by Linearized Deep Assignment. In preprint arXiv:2201.11162, 2022. pdf  BibTeX
  5. Homeyer, C.; Lange, O. and Schnörr, C. Multi-view Monocular Depth and Uncertainty Prediction with Deep SfM in Dynamic Environments. In ICPRAI, 2022. pdf  BibTeX

2021

  1. Savarino, F.; Albers, P. and Schnörr, C. On the Geometric Mechanics of Assignment Flows for Metric Data Labeling. In preprint: arXiv, 2021. pdf  BibTeX
  2. Zern, A.; Zeilmann, A. and Schnörr, C. Assignment Flows for Data Labeling on Graphs: Convergence and Stability. In Information Geometry, in press, 2021. pdf  BibTeX
  3. Sitenko, D.; Boll, B. and Schnörr, C. Assignment Flows and Nonlocal PDEs on Graphs. In DAGM GCPR, Springer, LNCS , 2021. pdf  BibTeX
  4. Gonzalez-Alvarado, D.; Zeilmann, A. and Schnörr, C. Quantifying Uncertainty of Image Labelings Using Assignment Flows. In DAGM GCPR, Springer, LNCS , 2021. pdf  BibTeX
  5. Sitenko, D.; Boll, B. and Schnörr, C. Assignment Flow For Order-Constrained OCT Segmentation. In Int. J. Computer Vision, 129: 3088-3118, 2021. pdf  BibTeX
  6. Schnörr, D. and Schnörr, C. Learning System Parameters from Turing Patterns. In preprint: arXiv, 2021. pdf  BibTeX
  7. Zeilmann, A.; Petra, S. and Schnörr, C. Learning Linearized Assignment Flows for Image Labeling. In preprint: arXiv, 2021. pdf  BibTeX
  8. Savarino, F. and Schnörr, C. Continuous-Domain Assignment Flows (Special issue: Connections between Deep Learning and Partial Differential Equations). In Europ. J. Appl. Math., 32 (3): 570-597, 2021. pdf  BibTeX
  9. Boll, B.; Schwarz, J. and Schnörr, C. On the Correspondence between Replicator Dynamics and Assignment Flows. In SSVM 2021: Scale Space and Variational Methods in Computer Vision, pages 373-384, Springer, LNCS 12679, 2021. pdf  BibTeX
  10. Zeilmann, A.; Petra, S. and Schnörr, C. Learning Linear Assignment Flows for Image Labeling via Exponential Integration. In SSVM 2021: Scale Space and Variational Methods in Computer Vision, pages 385-397, Springer, LNCS 12679, 2021. pdf  BibTeX
  11. Savarino, F.; Albers, P. and Schnörr, C. On the Geometric Mechanics of Assignment Flows for Metric Data Labeling. In SSVM 2021: Scale Space and Variational Methods in Computer Vision, pages 398-410, Springer, LNCS 12679, 2021. pdf  BibTeX
  12. Hühnerbein, R.; Savarino, F.; Petra, S. and Schnörr, C. Learning Adaptive Regularization for Image Labeling Using Geometric Assignment. In J. Math. Imaging Vision, 63: 186-215, 2021. pdf  BibTeX

2020

  1. Sitenko, D.; Boll, B. and Schnörr, C. Assignment Flow for Order-Constrained OCT Segmentation. In preprint: arXiv, 2020. pdf  BibTeX
  2. Draxler, F.; Schwarz, J.; Schnörr, C. and Köthe, U. Characterizing The Role of A Single Coupling Layer in Affine Normalizing Flows. In GCPR, 2020. pdf  BibTeX
  3. Schwarz, J.; Draxler, F.; Köhte, U. and Schnörr, C. Riemannian SOS-Polynomial Normalizing Flows. In GCPR, 2020. pdf  BibTeX
  4. Sitenko, D.; Boll, B. and Schnörr, C. Assignment Flow for Order-Constrained OCT Segmentation. In GCPR, 2020. pdf  BibTeX
  5. Savarino, F. and Schnörr, C. Continuous-Domain Assignment Flows. In Europ. J. Appl. Math., 2020. BibTeX
  6. Hühnerbein, R.; Savarino, F.; Petra, S. and Schnörr, C. Learning Adaptive Regularization for Image Labeling Using Geometric Assignment. In J. Math. Imaging Vision, 2020. pdf  BibTeX
  7. Zisler, M.; Zern, A.; Petra, S. and Schnörr, C. Self-Assignment Flows for Unsupervised Data Labeling on Graphs. In SIAM Journal on Imaging Sciences, 13 (3): 1113-1156, 2020. pdf  BibTeX
  8. Schnörr, C. Assignment Flows. In Handbook of Variational Methods for Nonlinear Geometric Data, pages 235—260, Springer, 2020. pdf  BibTeX
  9. Censor, Y.; Petra, S. and Schnörr, C. Superiorization vs. Accelerated Convex Optimization: The Superiorized/Regularized Least Squares Case. In J. Appl. Numer. Optimization (in press; arXiv:1911.05498), 2 (1): 15-62, 2020. pdf  BibTeX
  10. Zern, A.; Zisler, M.; Petra, S. and Schnörr, C. Unsupervised Assignment Flow: Label Learning on Feature Manifolds by Spatially Regularized Geometric Assignment. In Journal of Mathematical Imaging and Vision, 62 (6-7): 982-1006, 2020. pdf  BibTeX
  11. Zern, A.; Zeilmann, A. and Schnörr, C. Assignment Flows for Data Labeling on Graphs: Convergence and Stability. In preprint: arXiv, 2020. pdf  BibTeX
  12. Zeilmann, A.; Savarino, F.; Petra, S. and Schnörr, C. Geometric Numerical Integration of the Assignment Flow. In Inverse Problems, 36 (3): 034004 (33pp), 2020. pdf  BibTeX
  13. Desana, M. and Schnörr, C. Sum-Product Graphical Models. In Machine Learning, 109 (1): 135-173, 2020. pdf  BibTeX

2019

  1. Censor, Y.; Petra, S. and Schnörr, C. Superiorization vs. Accelerated Convex Optimization: The Superiorized/Regularized Least Squares Case. In preprint: arXiv, 2019. pdf  BibTeX
  2. Zisler, M.; Zern, A.; Petra, S. and Schnörr, C. Self-Assignment Flows for Unsupervised Data Labeling on Graphs. In preprint: arXiv, 2019. pdf  BibTeX
  3. Hühnerbein, R.; Savarino, F.; Petra, S. and Schnörr, C. Learning Adaptive Regularization for Image Labeling Using Geometric Assignment. In preprint: arXiv, 2019. pdf  BibTeX
  4. Savarino, F. and Schnörr, C. Continuous-Domain Assignment Flows. In preprint: arXiv, 2019. pdf  BibTeX
  5. Schnörr, C. Assignment Flows. In Variational Methods for Nonlinear Geometric Data and Applications, Springer, 2019. BibTeX
  6. Kostrykin, L.; Schnörr, C. and Rohr, K. Globally Optimal Segmentation of Cell Nuclei in Fluoroscence Microscopy Images using Shape and Intensity Information. In Medical Image Analysis, 2019. BibTeX
  7. Desana, M. and Schnörr, C. Sum-Product Graphical Models. In Machine Learning, 2019. BibTeX
  8. Zeilmann, A.; Savarino, F.; Petra, S. and Schnörr, C. Geometric Numerical Integration of the Assignment Flow. In Inverse Problems, 2019. BibTeX
  9. Rathke, F. and Schnörr, C. Fast Multivariate Log-Concave Density Estimation. In Comp. Statistics & Data Analysis, 140: 41–58, 2019. BibTeX
  10. Zern, A.; Zisler, M.; Petra, S. and Schnörr, C. Unsupervised Assignment Flow: Label Learning on Feature Manifolds by Spatially Regularized Geometric Assignment. In preprint: arXiv, 2019. pdf  BibTeX
  11. Savarino, F. and Schnörr, C. A Variational Perspective on the Assignment Flow. In Proc. SSVM, Springer, 2019. pdf  BibTeX
  12. Zisler, M.; Zern, A.; Petra, S. and Schnörr, C. Unsupervised Labeling by Geometric and Spatially Regularized Self-Assignment. In Proc. SSVM, Springer, 2019. pdf  BibTeX
  13. Hühnerbein, R.; Savarino, F.; Petra, S. and Schnörr, C. Learning Adaptive Regularization for Image Labeling Using Geometric Assignment. In Proc. SSVM, Springer, 2019. pdf  BibTeX

2018

  1. Zeilmann, A.; Savarino, F.; Petra, S. and Schnörr, C. Geometric Numerical Integration of the Assignment Flow. In preprint: arXiv, 2018. pdf  BibTeX
  2. Zern, A.; Zisler, M.; Aström, F.; Petra, S. and Schnörr, C. Unsupervised Label Learning on Manifolds by Spatially Regularized Geometric Assignment. In GCPR, 2018. pdf  BibTeX
  3. Hühnerbein, R.; Savarino, F.; Aström, F. and Schnörr, C. Image Labeling Based on Graphical Models Using Wasserstein Messages and Geometric Assignment. In SIAM J. Imaging Science, 11 (2): 1317-1362, 2018. pdf  BibTeX
  4. Rathke, F. and Schnörr, C. Fast Multivariate Log-Concave Density Estimation. In preprint: arXiv, 2018. BibTeX
  5. Zern, A.; Rohr, K. and Schnörr, C. Geometric Image Labeling with Global Convex Labeling Constraints. In EMMCVPR, pages 533-547, LNCS 10746, 2018. pdf  BibTeX
  6. Kostrykin, L.; Schnörr, C. and Rohr, K. Segmentation of Cell Nuclei Using Intensity-Based Model Fitting and Sequential Convex Programming. In Proc. ISBI, 2018. pdf  BibTeX

2017

  1. Aström, F. and Schnörr, C. A Geometric Approach for Color Image Regularization. In Comp. Vision Image Understanding, 165: 43-59, 2017. pdf  BibTeX
  2. Hühnerbein, R.; Savarino, F.; Aström, F. and Schnörr, C. Image Labeling Based on Graphical Models Using Wasserstein Messages and Geometric Assignment. , arXiv, preprint. pdf  www  BibTeX
  3. Zern, A.; Rohr, K. and Schnörr, C. Geometric Image Labeling with Global Convex Labeling Constraints. In Proc. EMMCVPR, 2017. BibTeX
  4. Zisler, M.; Savarino, F.; Petra, S. and Schnörr, C. Gradient Flows on a Riemannian Submanifold for Discrete Tomography. In Proc. GCPR, 2017. pdf  BibTeX
  5. Bodnariuc, E.; Petra, S.; Schnörr, C. and Voorneveld, J. A Local Spatio-Temporal Approach to Plane Wave Ultrasound Particle Image Velocimetry. In Proc. GCPR, 2017. pdf  BibTeX
  6. Rathke, F.; Desana, M. and Schnörr, C. Locally Adaptive Probabilistic Models for Global Segmentation of Pathological OCT Scans. In Proc. MICCAI, 2017. pdf  BibTeX
  7. Dalitz, R.; Petra, S. and Schnörr, C. Compressed Motion Sensing. In Proc. SSVM, Springer, LNCS 10302, 2017. pdf  BibTeX
  8. Savarino, F.; Hühnerbein, R.; Aström, F.; Recknagel, J. and Schnörr, C. Numerical Integration of Riemannian Gradient Flows for Image Labeling. In Proc. SSVM, Springer, LNCS 10302, 2017. pdf  BibTeX
  9. Aström, F.; Hühnerbein, R.; Savarino, F.; Recknagel, J. and Schnörr, C. MAP Image Labeling Using Wasserstein Messages and Geometric Assignment. In Proc. SSVM, Springer, LCNS 10302, 2017. pdf  BibTeX
  10. Zisler, M.; Aström, F.; Petra, S. and Schnörr, C. Image Reconstruction by Multilabel Propagation. In Proc. SSVM, Springer, LNCS 10302, 2017. pdf  BibTeX
  11. Markowsky, P.; Reith, S.; Zuber, T.E.; König, R.; Rohr, K. and Schnörr, C. Segmentation of cell structure using model-based set covering with iterative reweighting. In Proc. ISBI, 2017. pdf  BibTeX
  12. Aström, F.; Petra, S.; Schmitzer, B. and Schnörr, C. Image Labeling by Assignment. In J. Math. Imag. Vision, 58 (2): 211-238, 2017. BibTeX
  13. Berger, J.; Lenzen, F.; Becker, F.; Neufeld, A. and Schnörr, C. Second-Order Recursive Filtering on the Rigid-Motion Lie Group SE(3) Based on Nonlinear Observations. In J. Math. Imag. Vision, 58 (1): 102-129, 2017. pdf  BibTeX

2016

  1. Silvestri, F.; Reinelt, G. and Schnörr, C. Symmetry-free SDP Relaxations for Affine Subspace Clustering. , ArXiv, preprint. www  BibTeX
  2. Aström, F.; Petra, S.; Schmitzer, B. and Schnörr, C. A Geometric Approach to Image Labeling. In Proc. ECCV, 2016. pdf  BibTeX
  3. Aström, F. and Schnörr, C. Double-Opponent Vectorial Total Variation. In Proc. ECCV, 2016. pdf  BibTeX
  4. Bodnariuc, E.; Petra, S.; Poelma, C. and Schnörr, C. Parametric Dictionary-Based Velocimetry for Echo PIV. In Proc. CGPR, 2016. pdf  BibTeX
  5. Zisler, M.; Petra, S.; Schnörr, Cl. and Schnörr, Ch. Discrete Tomography by Continuous Multilabeling Subject to Projection Constraints. In Proc. GCPR, 2016. pdf  BibTeX
  6. Berger, J. and Schnörr, C. Joint Recursive Monocular Filtering of Camera Motion and Disparity Map. In 38th German Conference on Pattern Recognition, 2016. pdf  BibTeX
  7. Censor, Y.; Gibali, A.; Lenzen, F. and Schnörr, C. The Implicit Convex Feasibility Problem and Its Application to Adaptive Image Denoising. In J. Comp. Math., 34 (6): 608-623, 2016. pdf  BibTeX
  8. Aström, F. and Schnörr, C. A Geometric Approach to Color Image Regularization. , ArXiv, preprint. www  BibTeX
  9. Aström, F.; Petra, S.; Schmitzer, B. and Schnörr, C. The Assignment Manifold: A Smooth Model for Image Labeling. In Proc. 2nd Int. Workshop on Differential Geometry in Computer Vision and Machine Learning (DIFF-CVML'16; oral presentation; Grenander best paper award), 2016. BibTeX
  10. Desana, M. and Schnörr, C. Expectation Maximization for Sum-Product Networks as Exponential Family Mixture Models. pdf  www  BibTeX
  11. Kappes, J.H.; Swoboda, P.; Savchynskyy, B.; Hazan, T. and Schnörr, C. Multicuts and Perturb & MAP for Probabilistic Graph Clustering. In J. Math. Imag. Vision, 56 (2): 221-237, 2016. pdf  BibTeX
  12. Zisler, M.; Kappes, J.H.; Schnörr, Cl.; Petra, S. and Schnörr, Ch. Non-Binary Discrete Tomography by Continuous Non-Convex Optimization. In IEEE Comp. Imaging, 2 (3): 335-347, 2016. BibTeX
  13. Aström, F.; Petra, S.; Schmitzer, B. and Schnörr, C. Image Labeling by Assignment. , ArXiv, preprint. pdf  www  BibTeX
  14. Kappes, J.; Speth, M.; Reinelt, G. and Schnörr, C. Higher-order Segmentation via Multicuts. In Comp. Vision Image Understanding, 143: 104-119, 2016. pdf  BibTeX
  15. Swoboda, P.; Shekhovtsov, A.; Kappes, J.H.; Schnörr, C. and Savchynskyy, B. Partial Optimality by Pruning for MAP-Inference with General Graphical Models. In IEEE Trans. Patt. Anal. Mach. Intell., 38 (7): 1370-1382, 2016. pdf  BibTeX

2015

  1. Kappes, J.H.; Petra, S.; Schnörr, C. and Zisler, M. TomoGC: Binary Tomography by Constrained Graph Cuts. In Proc. GCPR, 2015. pdf  BibTeX
  2. Silvestri, F.; Reinelt, G. and Schnörr, C. A Convex Relaxation Approach to the Affine Subspace Clustering Problem. In Proc. GCPR, 2015. pdf  BibTeX
  3. Didden, E.-M.; Thorarinsdottir, T.L.; Lenkoski, A. and Schnörr, C. Shape from Texture using Locally Scaled Point Processes. In Image Anal. Stereol., 34 (3): 161-170, 2015. pdf  BibTeX
  4. Gianniotis, N.; Schnörr, C.; Molkenthin, C. and Bora, S.S. Approximate variational inference based on a finite sample of Gaussian latent variables. In Patt. Anal. Appl., 2015. pdf  BibTeX
  5. Berger, J.; Neufeld, A.; Becker, F.; Lenzen, F. and Schnörr, C. Second Order Minimum Energy Filtering on SE(3) with Nonlinear Measurement Equations. In Scale Space and Variational Methods in Computer Vision (SSVM 2015), 2015. pdf  BibTeX
  6. Berger, J.; Lenzen, F.; Becker, F.; Neufeld, A. and Schnörr, C. Second-Order Recursive Filtering on the Rigid-Motion Lie Group SE(3) Based on Nonlinear Observations. , ArXiv, preprint. pdf  www  BibTeX
  7. Biesdorf, A.; 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  BibTeX
  8. Bodnariuc, E.; Gurung, A.; Petra, S. and Schnörr, C. Adaptive Dictionary-Based Spatio-temporal Flow Estimation for Echo PIV. In Proc. EMMCVPR, pages 378-391, Springer, LNCS 8932, 2015. pdf  BibTeX
  9. Kappes, J.H.; 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. In Int. J. Comp. Vision, 115 (2): 155-184, 2015. pdf  BibTeX
  10. Kappes, J.; Swoboda, P.; Savchynskyy, B.; Hazan, T. and Schnörr, C. Probabilistic Correlation Clustering and Image Partitioning Using Perturbed Multicuts. In Proc. SSVM, Springer, LNCS , 2015. pdf  BibTeX
  11. Lenzen, F. and Berger, J. Solution-Driven Adaptive Total Variation Regularization. pdf  BibTeX
  12. Neufeld, A.; 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. pdf  BibTeX
  13. Rathke, F. and Schnörr, C. A Computational Approach to Log-Concave Density Estimation. In An. St. Univ. Ovidius Constanta, 23 (3): 151-166, 2015. pdf  BibTeX
  14. Schmitzer, B. and Schnörr, C. Globally Optimal Joint Image Segmentation and Shape Matching based on Wasserstein Modes. In J. Math. Imag. Vision, 52 (3): 436-458, 2015. pdf  BibTeX

2014

  1. Becker, F.; Petra, S. and Schnörr, C. Optical Flow. In Handbook of Mathematical Methods in Imaging, Springer, 2014. BibTeX
  2. Denitiu, A.; Petra, S.; Schnörr, Cl. and Schnörr, Ch. Phase Transitions and Cosparse Tomographic Recovery of Compound Solid Bodies from Few Projections. In Fundamenta Informaticae, 135: 73-102, 2014. pdf  BibTeX
  3. Denitiu, A.; 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, pages 262-274, Springer, LNCS , 2014. pdf  BibTeX
  4. Kappes, J. H.; 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. pdf  BibTeX
  5. Kappes, J. H.; 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. In CoRR, abs/1404.0533, 2014. pdf  BibTeX
  6. Kröger, T.; Kappes, J. H.; Beier, T.; Köthe, U. and Hamprecht, F. A. Asymmetric Cuts: Joint Image Labeling and Partitioning. In 36th German Conference on Pattern Recognition, 2014. BibTeX
  7. Lenzen, F.; Lellmann, J.; Becker, F. and Schnörr, C. Solving Quasi-Variational Inequalities for Image Restoration with Adaptive Constraint Sets. In SIAM J. Imag. Sci., 7 (4): 2139-2174, 2014. pdf  BibTeX
  8. Petra, S. and Schnörr, C. Average Case Recovery Analysis of Tomographic Compressive Sensing. In Linear Algebra and its Applications, 441: 168-198, 2014. pdf  BibTeX
  9. Rathke, F.; Schmidt, S. and Schnörr, C. Probabilistic Intra-Retinal Layer Segmentation in 3-D OCT Images Using Global Shape Regularization. In Medical Image Analysis, 18 (5): 781-794, 2014. pdf  BibTeX
  10. Schäfer, H.; Lenzen, F. and Garbe, C. S. Model based scattering correction in time-of-flight cameras. In Optics Express, 22: 29835-29846, 2014. BibTeX
  11. Schmitzer, B. and Schnörr, C. Globally Optimal Joint Image Segmentation and Shape Matching based on Wasserstein Modes. , preprint. pdf  BibTeX
  12. Swoboda, P.; 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  BibTeX

2013

  1. Becker, F.; 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. In International Journal of Computer Vision, 105: 269-297, 2013. pdf  doi  BibTeX
  2. Breitenreicher, D.; Lellmann, J. and Schnörr, C. COAL: a generic modelling and prototyping framework for convex optimization problems of variational image analysis. In Optimization Methods and Software, 28 (5): 1081-1094, 2013. pdf  doi  BibTeX
  3. Kappes, J. H.; Andres, B.; Hamprecht, Fred 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. pdf  BibTeX
  4. Kappes, J. H.; Speth, M.; Reinelt, G. and Schnörr, C. Towards Efficient and Exact MAP-Inference for Large Scale Discrete Computer Vision Problems via Combinatorial Optimization. In CVPR, 2013. pdf  BibTeX
  5. Kappes, J. H.; Speth, M.; Reinelt, G. and Schnörr, C. Higher-order Segmentation via Multicuts. pdf  BibTeX
  6. Lefloch, D.; Nair, R.; Lenzen, F.; Schäfer, H.; Streeter, L.; Cree, M. J.; Koch, R. and Kolb, A. Technical Foundation and Calibration Methods for Time-of-Flight Cameras. In Time-of-Flight and Depth Imaging: Sensors, Algorithms, and Applications, pages 3-24, Springer, Lecture Notes in Computer Science 8200, 2013. BibTeX
  7. Lellmann, J.; Lellmann, B.; Widmann, F. and Schnörr, C. Discrete and Continuous Models for Partitioning Problems. In Int. J. Comp. Visionz, 104 (3): 241-269, 2013. pdf  BibTeX
  8. Lenzen, F.; Becker, F. and Lellmann, J. Adaptive Second-Order Total Variation: An Approach Aware of Slope Discontinuities. In Proceedings of the 4th International Conference on Scale Space and Variational Methods in Computer Vision (SSVM) 2013, pages 371-398, Springer, LNCS 54, 2013. pdf  BibTeX
  9. Lenzen, F.; Becker, F.; Lellmann, J.; Petra, S. and Schnörr, C. A class of quasi-variational inequalities for adaptive image denoising and decomposition. In Computational Optimization and Applications, 54 (2): 371-398, 2013. pdf  BibTeX
  10. Lenzen, F.; Kim, K. In; Schäfer, H.; Nair, R.; Meister, S.; Becker, F.; Garbe, Christoph S. and Theobalt, C. Denoising Strategies for Time-of-Flight Data. In Time-of-Flight and Depth Imaging: Sensors, Algorithms, and Applications, pages 25-45, Springer, Lecture Notes in Computer Science 8200, 2013. pdf  BibTeX
  11. Nair, R.; Ruhl, K.; Lenzen, F.; Meister, S.; Schäfer, H.; Garbe, Christoph S.; Eisemann, M.; Magnor, M. and Kondermann, D. A Survey on Time-of-Flight Stereo Fusion. In Time-of-Flight and Depth Imaging: Sensors, Algorithms, and Applications, pages 105-127, Springer, Lecture Notes in Computer Science 8200, 2013. pdf  BibTeX
  12. Petra, S.; 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, pages 110-124, Springer, LNCS 7893, 2013. pdf  BibTeX
  13. Petra, S.; Schnörr, C. and Schröder, A. Critical Parameter Values and Reconstruction Propertiesof Discrete Tomography: Application to Experimental FluidDynamics. In Fundamenta Informaticae, 125: 285-312, 2013. pdf  BibTeX
  14. Savchynskyy, B.; Kappes, J. H.; Swoboda, P. and Schnörr, C. Global MAP-Optimality by Shrinking the Combinatorial Search Area with Convex Relaxation. In NIPS, 2013. pdf  BibTeX
  15. Schäfer, H.; Lenzen, F. and Garbe, Christoph. S. Depth and Intensity Based Edge Detection in Time-of-Flight Images. In 3DV-Conference, 2013 International Conference on, pages 111-118, 2013. pdf  BibTeX
  16. Schmitzer, B. and Schnörr, C. Object Segmentation by Shape Matching with Wasserstein Modes. In Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR 2013), pages 123-136, 2013. pdf  BibTeX
  17. Schmitzer, B. and Schnörr, C. Modelling convex shape priors and matching based on the Gromov-Wasserstein distance. In Journal of Mathematical Imaging and Vision, 46 (1): 143-159, 2013. pdf  BibTeX
  18. Schmitzer, B. and Schnörr, C. Contour Manifolds and Optimal Transport. , preprint. pdf  BibTeX
  19. Schmitzer, B. and Schnörr, C. A Hierarchical Approach to Optimal Transport. In Scale Space and Variational Methods (SSVM 2013), pages 452-464, 2013. pdf  BibTeX
  20. Swoboda, P.; Savchynskyy, B.; Kappes, J. H. and Schnörr, C. Partial Optimality via Iterative Pruning for the Potts Model. In Scale Space and Variational Methods (SSVM 2013), 2013. pdf  BibTeX
  21. Swoboda, P. and Schnörr, C. Variational Image Segmentation and Cosegmentation with the Wasserstein Distance. In Energy Minimization Methods in Computer Vision and Pattern Recognition, pages 321-334, Springer, Lecture Notes in Computer Science 8081, 2013. pdf  BibTeX
  22. Swoboda, P. and Schnörr, C. Convex Variational Image Restoration with Histogram Priors. In SIAM J. Imag. Sci., 6 (3): 1719-1735, 2013. pdf  BibTeX

2012

  1. Andres, B.; Beier, T. and Kappes, J. H. OpenGM: A C++ Library for Discrete Graphical Models. In ArXiv e-prints, 2012. pdf  BibTeX
  2. Andres, B.; Kappes, J. H.; Beier, T.; Köthe, U. and Hamprecht, Fred A. The Lazy Flipper: Efficient Depth-limited Exhaustive Search in Discrete Graphical Models. In ECCV 2012, 2012. pdf  BibTeX
  3. Becker, F.; Wieneke, B.; Petra, S.; Schröder, A. and Schnörr, C. Variational Adaptive Correlation Method for Flow Estimation. In IEEE Transactions on Image Processing, 21 (6): 3053-3065, 2012. pdf  BibTeX
  4. Kappes, J. H.; Savchynskyy, B. and Schnörr, C. A Bundle Approach To Efficient MAP-Inference by Lagrangian Relaxation. In cvpr, 2012. pdf  BibTeX
  5. Lellmann, J.; Lenzen, F. and Schnörr, C. Optimality Bounds for a Variational Relaxation of the Image Partitioning Problem. In Journal of Mathematical Imaging and Vision, 47 (3): 239-257, 2012. pdf  BibTeX
  6. Lenzen, F.; Becker, F.; Lellmann, J.; Petra, S. and Schnörr, C. Variational Image Denoising with Adaptive Constraint Sets. In Proceedings of the 3rd International Conference on Scale Space and Variational Methods in Computer Vision 2011, pages 206-217, Springer, 2012. pdf  BibTeX
  7. Nair, R.; Lenzen, F.; Meister, S.; Schäfer, H.; Garbe, Christoph S. and Kondermann, D. High accuracy TOF and stereo sensor fusion at interactive rates. In Computer Vision--ECCV 2012. Workshops and Demonstrations, pages 1-11, Springer Berlin Heidelberg, 2012. pdf  BibTeX
  8. Petra, S.; Schnörr, C. and Schröder, A. Critical Parameter Values and Reconstruction Properties of Discrete Tomography: Application to Experimental Fluid Dynamics. www  BibTeX
  9. Savchynskyy, B.; Schmidt, S.; Kappes, J. H. and Schnörr, C. Efficient MRF Energy Minimization via Adaptive Diminishing Smoothing. In UAI 2012, 2012. pdf  BibTeX
  10. Schmitzer, B. and Schnörr, C. Weakly Convex Coupling Continuous Cuts and Shape Priors. In Scale Space and Variational Methods (SSVM 2011), pages 423-434, 2012. pdf  BibTeX

2011

  1. Andres, B.; Kappes, J. H.; Beier, T.; Köthe, U. and Hamprecht, Fred A. Probabilistic Image Segmentation with Closedness Constraints. In Proceedings of ICCV, 2011. pdf  BibTeX
  2. Becker, F.; 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. In 2011 IEEE International Conference on Computer Vision (ICCV), pages 1692-1699, 2011. pdf  BibTeX
  3. Breitenreicher, D.; Lellmann, J. and Schnörr, C. Sparse Template-Based Variational Image Segmentation. In Advances in Adaptive Data Analysis, 3: 149-166, 2011. pdf  BibTeX
  4. Breitenreicher, D. and Schnörr, C. Model-Based Multiple Rigid Object Detection and Registration in Unstructured Range Data. In Int. J. Comp. Vision, 92: 32-52, 2011. pdf  doi  BibTeX
  5. Kappes, J. H.; Speth, M.; Andres, B.; Reinelt, G. and Schnörr, C. Globally Optimal Image Partitioning by Multicuts. In EMMCVPR, Springer, 2011. pdf  BibTeX
  6. Kappes, J. H. Inference on Highly-Connected Discrete Graphical Models with Applications to Visual Object Recognition. Ph.D. Thesis, Ruprecht-Karls-Universität Heidelberg, Faculty of Mathematics and Computer Sciences, Heidelberg, Germany, 2011. BibTeX
  7. Lellmann, J.; Lenzen, F. and Schnörr, C. Optimality Bounds for a Variational Relaxation of the Image Partitioning Problem. In Energy Min. Meth. Comp. Vis. Patt. Recogn., pages 132-146, Springer, LNCS 6819, 2011. pdf  BibTeX
  8. Lellmann, J.; Lenzen, F. and Schnörr, C. Optimality Bounds for a Variational Relaxation of the Image Partitioning Problem. Technical Report, IPA group, Heidelberg University, 2011. BibTeX
  9. Lellmann, J. and Schnörr, C. Continuous Multiclass Labeling Approaches and Algorithms. In CoRR, abs/1102.5448, 2011. BibTeX
  10. Lellmann, J. and Schnörr, C. Regularizers for Vector-Valued Data and Labeling Problems in Image Processing. In Control Systems and Computers, 2: 43-54, 2011. BibTeX
  11. Lellmann, J. and Schnörr, C. Continuous Multiclass Labeling Approaches and Algorithms. In SIAM J. Imag. Sci., 4 (4): 1049-1096, 2011. pdf  BibTeX
  12. Nicola, A.; Petra, S.; Popa, C. and Schnörr, C. A general extending and constraining procedure for linear iterative methods. In Int. J. Comp. Math., 2011. pdf  BibTeX
  13. Rathke, F.; Schmidt, S. and Schnörr, C. Order Preserving and Shape Prior Constrained Intra-Retinal Layer Segmentation in Optical Coherence Tomography. In MICCAI, pages 370-377, Springer, Lecture Notes in Computer Science 6893, 2011. pdf  BibTeX
  14. Savchynskyy, B.; Kappes, J. H.; Schmidt, S. and Schnörr, C. A Study of Nesterov's Scheme for Lagrangian Decomposition and MAP Labeling. In IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), 2011. pdf  BibTeX
  15. Schmidt, S.; Savchynskyy, B.; Kappes, J. H. and Schnörr, C. Evaluation of a First-Order Primal-Dual Algorithm for MRF Energy Minimization. In EMMCVPR, pages 89-103, Springer, LNCS 6819, 2011. pdf  BibTeX

2010

  1. Andres, B.; Kappes, J. H.; Köthe, U.; Schnörr, C. and Hamprecht, Fred A. An Empirical Comparison of Inference Algorithms for Graphical Models with Higher Order Factors Using OpenGM. In Pattern Recognition, Proc. 32th DAGM Symposium, 2010. pdf  BibTeX
  2. Bergtholdt, M.; Kappes, J. H.; Schmidt, S. and Schnörr, C. A Study of Parts-Based Object Class Detection Using Complete Graphs. In Int. J. Comp. Vision, 87 (1-2): 93-117, 2010. pdf  doi  BibTeX
  3. Breitenreicher, D. and Schnörr, C. Robust 3D object registration without explicit correspondence using geometric integration. In Machine Vision and Applications, 21 (5): 601-611, 2010. pdf  doi  BibTeX
  4. Heitz, D.; Mémin, E. and Schnörr, C. Variational fluid flow measurements from image sequences: synopsis and perspectives. In Exp. Fluids, 48 (3): 369-393, 2010. pdf  BibTeX
  5. Kappes, J. H.; Schmidt, S. and Schnörr, C. MRF Inference by k-Fan Decomposition and Tight Lagrangian Relaxation. In European Conference on Computer Vision (ECCV), pages 735-747, Springer Berlin / Heidelberg, 2010. pdf  BibTeX
  6. Lellmann, J.; Breitenreicher, D. and Schnörr, C. Fast and Exact Primal-Dual Iterations for Variational Problems in Computer Vision. In European Conference on Computer Vision (ECCV), pages 494-505, Springer Berlin / Heidelberg, LNCS 6312, 2010. pdf  BibTeX
  7. Lellmann, J. and Schnörr, C. Continuous Multiclass Labeling Approaches and Algorithms. Technical Report, Univ. of Heidelberg, 2010. BibTeX
  8. Vlasenko, A. and Schnörr, C. Physically Consistent and Efficient Variational Denoising of Image Fluid Flow Estimates. In IEEE Trans. Image Proc., 19 (3): 586-595, 2010. pdf  BibTeX

2009

  1. Becker, F. Variational Correlation and Decomposition Methods for Particle Image Velocimetry. Ph.D. Thesis, Heidelberg University, Faculty of Mathematics and Computer Sciences, Heidelberg, Germany, 2009. BibTeX
  2. Breitenreicher, D. and Schnörr, C. Intrinsic Second-Order Geometric Optimization for Robust Point Set Registration Without Correspondence. In Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR 2009), pages 274-287, Springer, LNCS 5681, 2009. pdf  BibTeX
  3. Gosch, C. Contour Methods for View Point Tracking. Ph.D. Thesis, University of Heidelberg, 2009. BibTeX
  4. Lauer, F. and Schnörr, C. Spectral Clustering of Linear Subspaces for Motion Segmentation. In Proc. IEEE Int. Conf. Computer Vision (ICCV'09), Kyoto, Japan, 2009. pdf  BibTeX
  5. Lellmann, J.; 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), pages 646-653, 2009. pdf  BibTeX
  6. Lellmann, J.; 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), pages 150-162, Springer, LNCS 5567, 2009. pdf  BibTeX
  7. Nicola, A.; Petra, S.; Popa, C. and Schnörr, C. On a general extending and constraining procedure for linear iterative methods. Technical Report, IWR, University of Heidelberg, 2009. pdf  BibTeX
  8. Petra, S.; Popa, C. and Schnörr, C. Accelerating Constrained SIRT with Applications in Tomographic Particle Image Reconstruction. Technical Report, IWR, University of Heidelberg, 2009. pdf  BibTeX
  9. Petra, S. and Schnörr, C. TomoPIV meets Compressed Sensing. Technical Report, IWR, University of Heidelberg, 2009. pdf  BibTeX
  10. Petra, S. and Schnörr, C. TomoPIV meets Compressed Sensing. In Pure Math. Appl., 20 (1-2): 49-76, 2009. pdf  BibTeX
  11. Petra, S.; Schröder, A. and Schnörr, C. 3D Tomography from Few Projections in Experimental Fluid Mechanics. In Imaging Measurement Methods for Flow Analysis, pages 63-72, Springer, Notes on Numerical Fluid Mechanics and Multidisciplinary Design 106, 2009. pdf  BibTeX
  12. Vlasenko, A. and Schnörr, C. Variational Approaches for Model-Based PIV and Visual Fluid Analysis. In Imaging Measurement Methods for Flow Analysis, pages 247-256, Springer, Notes on Numerical Fluid Mechanics and Multidisciplinary Design 106, 2009. pdf  BibTeX
  13. Yuan, J.; Schnörr, C. and Steidl, G. Convex Hodge Decomposition and Regularization of Image Flows. In J. Math. Imag. Vision, 33 (2): 169-177, 2009. pdf  BibTeX
  14. Yuan, Jing.; Schnörr, C. and Steidl, G. Total-Variation Based Piecewise Affine Regularization. In Scale Space and Variational Methods in Computer Vision (SSVM 2009), pages 552-564, Springer, LNCS 5567, 2009. pdf  BibTeX

2008

  1. Becker, F. and Schnörr, C. Decomposition of Quadratric Variational Problems. In Pattern Recognition -- 30th DAGM Symposium, pages 325-334, Springer Verlag, lncs 5096, 2008. pdf  BibTeX
  2. Becker, F.; Wieneke, B.; Yuan, J. and Schnörr, C. A Variational Approach to Adaptive Correlation for Motion Estimation in Particle Image Velocimetry. In Pattern Recognition -- 30th DAGM Symposium, pages 335-344, Springer Verlag, lncs 5096, 2008. pdf  BibTeX
  3. Becker, F.; Wieneke, B.; Yuan, J. and Schnörr, C. Variational Correlation Approach to Flow Measurement with Window Adaption. In 14th International Symposium on Applications of Laser Techniques to Fluid Mechanics, pages 1.1.3, 2008. pdf  BibTeX
  4. Enzweiler, M. and Gavrila, D. M. Monocular Pedestrian Detection: Survey and Experiments. In IEEE Transactions on Pattern Analysis and Machine Intelligence, available online: IEEE Computer Society Digital Library, http://doi.ieeecomputersociety.org/10.1109/TPAMI.2008.260, 2008. BibTeX
  5. Enzweiler, M. and Gavrila, D. M. A Mixed Generative-Discriminative Framework for Pedestrian Classification. In Proc. Int. Conf. Comp. Vision and Patt. Recog. (CVPR), 2008. BibTeX
  6. Enzweiler, M.; Kanter, P. and Gavrila, D. M. Monocular Pedestrian Recognition Using Motion Parallax. In Proc. IEEE Symposium on Intelligent Vehicles, pages 792-797, 2008. BibTeX
  7. Fundana, K.; Heyden, A.; Gosch, C. and Schnörr, C. Continuous Graph Cuts for Prior-Based Object Segmentation. In 19th Int. Conf. Patt. Recog. (ICPR), pages 1-4, 2008. pdf  BibTeX
  8. Gosch, C.; Fundana, K.; Heyden, A. and Schnörr, C. View Point Tracking of Rigid Object Based on Shape Sub-Manifolds. In Computer Vision -- ECCV 2008, pages 251-263, Springer, lncs 5302, 2008. pdf  BibTeX
  9. Kappes, J. H. and Schnörr, C. MAP-Inference for Highly-Connected Graphs with DC-Programming. In Pattern Recognition -- 30th DAGM Symposium, pages 1-10, Springer Verlag, lncs 5096, 2008. pdf  BibTeX
  10. Lellmann, J.; Kappes, J. H.; Yuan, J.; Becker, F. and Schnörr, C. Convex Multi-Class Image Labeling by Simplex-Constrained Total Variation. Technical Report, IWR, University of Heidelberg, 2008. pdf  BibTeX
  11. Munder, S.; Schnörr, C. and Gavrila, D. M. Pedestrian Detection and Tracking Using a Mixture of View-Based Shape-Texture Models. In IEEE Trans. Intell. Transp. Systems, 9: 333-343, 2008. BibTeX
  12. Petra, S.; Popa, C. and Schnörr, C. Extended and Constrained Cimmino-type Algorithms with Applications in Tomographic Image Reconstruction. Technical Report, IWR, University of Heidelberg, 2008. pdf  BibTeX
  13. Petra, S.; Popa, C. and Schnörr, C. Enhancing Sparsity by Constraining Strategies: Constrained SIRT versus Spectral Projected Gradient Methods. In Proc. 7th Workshop on Modelling of Environmental and Life Sciences Problems (WMM 08), Ed Acad Romane, Bucuresti, Constanta, Romania, 2008. pdf  BibTeX
  14. Petra, S.; Popa, C. and Schnörr, C. Enhancing Sparsity by Constraining Strategies: Constrained SIRT versus Spectral Projected Gradient Methods. In Proc. 7th Workshop on Modelling of Environmental and Life Sciences Problems (WMM 08), Bucharest, Romania, Ed Acad Romane , 2008. BibTeX
  15. Petra, S.; Schröder, A.; Wieneke, B. and Schnörr, C. On Sparsity Maximization in Tomographic Particle Image Reconstruction. In Pattern Recognition -- 30th DAGM Symposium, pages 294-303, Springer Verlag, lncs 5096, 2008. pdf  BibTeX
  16. Vlasenko, A. and Schnörr, C. Physically Consistent Variational Denoising of Image Fluid Flow Estimates. In Pattern Recognition -- 30th DAGM Symposium, pages 406-415, Springer Verlag, lncs 5096, 2008. pdf  BibTeX
  17. Yuan, J.; Steidl, G. and Schnörr, C. Convex Hodge Decomposition of Image Flows. In Pattern Recognition -- 30th DAGM Symposium, pages 416-425, Springer Verlag, lncs 5096, 2008. pdf  BibTeX

2007

  1. Gall, J.; Potthoff, J.; Schnörr, C.; Rosenhahn, B. and Seidel, H-P. Interacting and Annealing Particle Filters: Mathematics and a Recipe for Applications. In J. Math. Imag. Vision, 28: 1-18, 2007. pdf  BibTeX
  2. Karim, R.; Bergtholdt, M.; Kappes, J. H. and Schnörr, C. Greedy-Based Design of Sparse Two-Stage SVMs for Fast Classification. In Pattern Recognition -- 29th DAGM Symposium, pages 395-404, Springer, LCNS 4713, 2007. pdf  BibTeX
  3. Petra, S.; Schnörr, C.; Schröder, A. and . Wieneke, Tomographic Image Reconstruction in Experimental Fluid Dynamics: Synopsis and Problems. In Proc. 6th Workshop on Modelling of Environmental and Life Sciences Problems (WMM 07), Ed Acad Romane, Bucuresti, Constanta, Romania, 2007. pdf  BibTeX
  4. Ruhnau, P. and Schnörr, C. Optical Stokes Flow Estimation: An Imaging-Based Control Approach. In Exp. in Fluids, 42: 61-78, 2007. pdf  BibTeX
  5. Ruhnau, P.; Stahl, A. and Schnörr, C. Variational Estimation of Experimental Fluid Flows with Physics-Based Spatio-Temporal Regularization. In Measurement Science and Technology, 18: 755-763, 2007. pdf  BibTeX
  6. Schellewald, C.; Roth, S. and Schnörr, C. Evaluation of a convex relaxation to a quadratic assignment matching approach for relational object views. In Image Vision Comp., 25: 1301-1314, 2007. pdf  BibTeX
  7. Schmidt, S.; Kappes, J. H.; Bergtholdt, M.; Pekar, V.; Dries, S.; Bystrov, D. and Schnörr, C. Spine Detection and Labeling Using a Parts-Based Graphical Model. In Proc. 20th International Conference on Information Processing in Medical Imaging (IPMI 2007), pages 122-133, Springer, LCNS 4584, 2007. pdf  BibTeX
  8. Schnörr, C. Signal and Image Approximation with Level-Set Constraints. In Computing, 81: 137-160, 2007. pdf  BibTeX
  9. Schnörr, C.; Schüle, T. and Weber, S. Variational Reconstruction with DC-Programming. In Advances in Discrete Tomography and Its Applications, Birkhäuser, Boston, 2007. BibTeX
  10. Welk, M.; Weickert, J.; Becker, F.; Schnörr, C.; Feddern, C. and Burgeth, B. Median and related local filters for tensor-valued images. In Signal Processing, 87: 291-308, 2007. pdf  BibTeX
  11. Yuan, J.; Schnörr, C. and M'emin, E. Discrete Orthogonal Decomposition and Variational Fluid Flow Estimation. In J. Math. Imag. Vision, 28: 67-80, 2007. pdf  BibTeX
  12. Yuan, J.; Schnörr, C. and Steidl, G. Simultaneous Optical Flow Estimation and Decomposition. In SIAM J. Scientific Computing, 29 (6): 2283-2304, 2007. pdf  BibTeX
  13. Hamprecht, Fred A.; Schnörr, C. and Jähne, B., ed. Pattern Recognition -- 29th DAGM Symposium. Springer, LCNS 4713, 2007. BibTeX

2006

  1. Bergtholdt, M.; Kappes, J. H. and Schnörr, C. Learning of Graphical Models and Efficient Inference for Object Class Recognition. In Proc. DAGM 2006, pages 375-388, Springer, LCNS 375-388, 2006. pdf  BibTeX
  2. Bruhn, A.; Weickert, J.; Kohlberger, T. and Schnörr, C. A Multigrid Platform for Real-Time Motion Computation with Discontinuity-Preserving Variational Methods. In Int. J. Computer Vision, 70 (3): 257-277, 2006. pdf  BibTeX
  3. Cremers, D.; Sochen, N. and Schnörr, C. Multiphase Dynamic Labeling for Variational Recognition-Driven Image Segmentation. In ijcv, 66 (1): 67-81, 2006. BibTeX
  4. Heiler, M. and Schnörr, C. Controlling Sparseness in Non-negative Tensor Factorization. In Computer Vision -- ECCV 2006, pages 56-67, Springer, lncs 3951, 2006. pdf  BibTeX
  5. Heiler, M. and Schnörr, C. Learning Sparse Representations by Non-Negative Matrix Factorization and Sequential Cone Programming. In J. Mach. Learning Res., 7: 1385-1407, 2006. BibTeX
  6. Ruhnau, P.; Stahl, A. and Schnörr, C. On-Line Variational Estimation of Dynamical Fluid Flows with Physics-Based Spatio-Temporal Regularization. In Proc. DAGM 2006, pages 375-388, Springer, LNCS 375-388, 2006. pdf  BibTeX
  7. Stahl, A.; 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, LNCS, Springer, 2006. pdf  BibTeX
  8. Weber, S.; 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), pages 146-156, Springer, LNCS 4245, 2006. pdf  BibTeX
  9. Weber, S.; Schüle, T.; Schnörr, C. and Kuba, A. Binary Tomography with Deblurring. In Combinatorial Image Analysis, pages 375-388, Springer, Lect. Not. Comp. Science 4040, 2006. pdf  BibTeX

2005

  1. Bergtholdt, M.; Cremers, D. and Schnörr, C. Variational Segmentation with Shape Priors. In Handbook of Mathematical Models in Computer Vision, pages 147-160, Springer, 2005. BibTeX
  2. Bergtholdt, M. and Schnörr, C. Shape Priors and Online Appearance Learning for Variational Segmentation and Object Recognition in Static Scenes. In Pattern Recognition, Proc. 27th DAGM Symposium, pages 342-350, Springer, lncs 3663, 2005. BibTeX
  3. Bruhn, A.; Weickert, J.; Feddern, C.; Kohlberger, T. and Schnörr, C. Variational optic flow computation in real-time. In IEEE Trans. Image Proc., 14 (5): 608-615, 2005. BibTeX
  4. Bruhn, A.; Weickert, J.; Kohlberger, T. and Schnörr, C. Discontinuity-Preserving Computation of Variational Optic Flow in Real-Time. In Scale-Space 2005, pages 279-290, Springer, lncs 3459, 2005. BibTeX
  5. Bruhn, A.; Weickert, J. and Schnörr, C. Lucas/Kanade Meets Horn/Schunck: Combining Local and Global Optic Flow Methods. In ijcv, 61 (3): 211-231, 2005. BibTeX
  6. Heiler, M.; Keuchel, J. and Schnörr, C. Semidefinite Clustering for Image Segmentation with A-priori Knowledge. In Pattern Recognition, Proc. 27th DAGM Symposium, pages 309-317, Springer, lncs 3663, 2005. BibTeX
  7. Heiler, M. and Schnörr, C. Natural Image Statistics for Natural Image Segmentation. In Int. J. Comp. Vision, 63 (1): 5-19, 2005. BibTeX
  8. Heiler, M. and Schnörr, C. Learning Sparse Image Codes by Convex Programming. In Proc. Tenth IEEE Int. Conf. Computer Vision (ICCV'05), pages 1667-1674, Beijing, China, 2005. BibTeX
  9. Heiler, M. and Schnörr, C. Reverse-Convex Programming for Sparse Image Codes. In Proc. Int. Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR'05), pages 600-616, Springer, LNCS 3757, 2005. BibTeX
  10. Kohlberger, T.; Schnörr, C.; Bruhn, A. and Weickert, J. Domain decomposition for variational optical flow computation. In IEEE Trans. Image Proc., 14 (8): 1125-1137, 2005. BibTeX
  11. Neumann, J.; Schnörr, C. and Steidl, G. Combined SVM-based Feature Selection and Classification. In Machine Learning, 61: 129-150, 2005. BibTeX
  12. Neumann, J.; Schnörr, C. and Steidl, G. Efficient Wavelet Adaption for Hybrid Wavelet-Large Margin Classifiers. In Pattern Recognition, 38 (11): 1815-1830, 2005. BibTeX
  13. Ruhnau, P.; Gütter, C.; Putze, T. and Schnörr, C. A variational approach for particle tracking velocimetry. In Meas. Science and Techn., 16: 1449-1458, 2005. BibTeX
  14. Ruhnau, P.; Kohlberger, T.; Nobach, H. and Schnörr, C. Variational Optical Flow Estimation for Particle Image Velocimetry. In Experiments in Fluids, 38: 21-32, 2005. pdf  BibTeX
  15. Schüle, T.; Schnörr, C.; Weber, S. and Hornegger, J. Discrete Tomography By Convex-Concave Regularization and D.C. Programming. In Discr. Appl. Math., 151: 229-243, 2005. BibTeX
  16. Schüle, T.; Weber, S. and Schnörr, C. Adaptive Reconstruction of Discrete-Valued Objects from few Projections. In Electr. Notes in Discr. Math., 20: 365-384, 2005. BibTeX
  17. Schellewald, C. and Schnörr, C. Probabilistic Subgraph Matching Based on Convex Relaxation. In Proc. Int. Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR'05), pages 171-186, Springer, lncs 3757, 2005. BibTeX
  18. Weber, S.; Schüle, T. and Schnörr, C. Prior Learning and Convex-Concave Regularization of Binary Tomography. In Electr. Notes in Discr. Math., 20: 313-327, 2005. BibTeX
  19. Weber, S.; Schnörr, C.; Schüle, T. and Hornegger, J. Binary Tomography by Iterating Linear Programs. In Geometric Properties from Incomplete Data, Springer, 2005. BibTeX
  20. Welk, M.; Becker, F.; Schnörr, C. and Weickert, J. Matrix-Valued Filters as Convex Programs. In Scale-Space 2005, pages 204-216, Springer, lncs 3459, 2005. BibTeX
  21. Yuan, J.; Ruhnau, P.; M'emin, E. and Schnörr, C. Discrete Orthogonal Decomposition and Variational Fluid Flow Estimation. In Scale-Space 2005, pages 267-278, Springer, lncs 3459, 2005. BibTeX
  22. Yuan, J.; Schnörr, C.; Steidl, G. and Becker, F. A Study of Non-Smooth Convex Flow Decomposition. In Proc. Variational, Geometric and Level Set Methods in Computer Vision, pages 1-12, Springer, lncs 3752, 2005. BibTeX
  23. Paragios, N.; Faugeras, O.; Chan, T. and Schnörr, C., ed. Variational, Geometric and Level Sets in Computer Vision (VLSM'05). Springer, Beijing, China, lncs 3752, 2005. BibTeX

2004

  1. Bruhn, A.; Jakob, T.; Fischer, M.; Weickert, J.; Brüning, U. and Schnörr, C. High performance cluster computing with 3-D nonlinear diffusion filters. In Real-Time Imaging, 10 (1): 41-51, 2004. BibTeX
  2. Cremers, D.; Sochen, N. and Schnörr, C. Multiphase Dynamic Labeling for Variational Recognition-Driven Image Segmentation. In Computer Vision -- ECCV 2004, pages 74-86, Springer, lncs 3024, 2004. BibTeX
  3. Giebel, J.; Gavrila, D. M. and Schnörr, C. A Bayesian Framework for Multi-cue 3D Object Tracking. In Computer Vision -- ECCV 2004, pages 241-252, Springer, lncs 3024, 2004. BibTeX
  4. Keuchel, J.; Heiler, M. and Schnörr, C. Hierarchical Image Segmentation based on Semidefinite Programming. In Pattern Recognition, Proc. 26th DAGM Symposium, pages 120-128, Springer, lncs 3175, 2004. BibTeX
  5. Kohlberger, T.; Schnörr, C.; Bruhn, A. and Weickert, J. Parallel Variational Motion Estimation by Domain Decomposition and Cluster Computing. In Computer Vision -- ECCV 2004, pages 205-216, Springer, lncs 3024, 2004. BibTeX
  6. Neumann, J.; Schnörr, C. and Steidl, G. SVM-based Feature Selection by Direct Objective Minimisation. In Pattern Recognition, Proc. 26th DAGM Symposium, pages 212-219, Springer, lncs 3175, 2004. BibTeX
  7. Ruhnau, P.; Kohlberger, T.; Nobach, H. and Schnörr, C. Variational Optical Flow Estimation for Particle Image Velocimetry. In Proc. Lasermethoden in der Strömungsmesstechnik, Deutsche Gesellschaft für Laser-Anemometrie GALA e.V., Karlsruhe, 2004. BibTeX
  8. Weber, S.; Schüle, T.; Hornegger, J. and Schnörr, C. Binary Tomography by Iterating Linear Programs from Noisy Projections. In Combinatorial Image Analysis, Proc. Int. Workshop on Combinatorial Image Analysis (IWCIA'04), pages 38-51, Springer Verlag, LNCS 3322, 2004. BibTeX
  9. Weber, S.; Schüle, T.; Schnörr, C. and Hornegger, J. A Linear Programming Approach to Limited Angle 3D Reconstruction from DSA Projections. In Methods of Information in Medicine, 43 (4): 320-326, 2004. BibTeX
  10. Yuan, J.; Schnörr, C.; Kohlberger, T. and Ruhnau, P. Convex Set-Based Estimation of Image Flows. In ICPR 2004 -- 17th Int. Conf. on Pattern Recognition, pages 124-127, IEEE, Cambridge, UK, 2004. BibTeX

2003

  1. Bruhn, A.; Weickert, J.; Feddern, C.; Kohlberger, T. and Schnörr, C. Real-Time Optic Flow Computation with Variational Methods. In Proc. Computer Analysis of Images and Patterns (CAIP'03), pages 222-229, Springer, LNCS 2756, 2003. BibTeX
  2. Bruhn, A.; Weickert, J.; Feddern, C.; Kohlberger, T. and Schnörr, C. Variational Optic Flow Computation in Real-Time. Technical Report 89, Dept. Math. and Comp. Science, Saarland University, Germany, 2003. BibTeX
  3. Cremers, D.; Kohlberger, T. and Schnörr, C. Shape Statistics in Kernel Space for Variational Image Segmentation. In Pattern Recognition, 36 (9): 1929-1943, 2003. pdf  BibTeX
  4. Cremers, D. and Schnörr, C. Statistical Shape Knowledge in Variational Motion Segmentation. In Image and Vision Comp., 21 (1): 77-86, 2003. BibTeX
  5. Cremers, D.; Sochen, N. and Schnörr, C. Towards Recognition-Based Variational Segmentation Using Shape Priors and Dynamic Labeling. In Scale Space Methods in Computer Vision, pages 388-400, Springer, LNCS 2695, 2003. pdf  BibTeX
  6. Heiler, M. and Schnörr, C. Natural Statistics for Natural Image Segmentation. In Proc. IEEE Int. Conf. Computer Vision (ICCV 2003), pages 1259-1266, Nice, France, 2003. BibTeX
  7. Keuchel, J.; Schnörr, C.; Schellewald, C. and Cremers, D. Binary Partitioning, Perceptual Grouping, and Restoration with Semidefinite Programming. In pami, 25 (11): 1364-1379, 2003. BibTeX
  8. Kohlberger, T.; M'emin, E. and Schnörr, C. Variational Dense Motion Estimation Using the Helmholtz Decomposition. In Scale Space Methods in Computer Vision, pages 432-448, Springer, LNCS 2695, 2003. BibTeX
  9. Kohlberger, T.; Schnörr, C.; Bruhn, A. and Weickert, J. Domain Decomposition for Parallel Variational Optical Flow Computation. In Pattern Recognition, Proc. 25th DAGM Symposium, pages 196-203, Springer, LNCS 2781, 2003. BibTeX
  10. Kohlberger, T.; Schnörr, C.; Bruhn, A. and Weickert, J. Domain Decomposition for Variational Optical Flow Computation. Technical Report 07/2003, Dept. Math. and Comp. Science, University of Mannheim, Germany, 2003. BibTeX
  11. Neumann, J.; Schnörr, C. and Steidl, G. Feasible Adaption Criteria for Hybrid Wavelet -- Large Margin Classifiers. In Proc. Computer Analysis of Images and Patterns (CAIP'03), pages 588-595, Springer, LNCS 2756, 2003. BibTeX
  12. Neumann, J.; Schnörr, C. and Steidl, G. Effectively Finding the Optimal Wavelet for Hybrid Wavelet - Large Margin Signal Classification. Technical Report 5, Dept. Math. and Comp. Science, University of Mannheim, Germany, 2003. BibTeX
  13. Schüle, T.; Schnörr, C.; Weber, S. and Hornegger, J. Discrete Tomography By Convex-Concave Regularization and D.C. Programming. Technical Report 15, Dept. Math. and Comp. Science, University of Mannheim, Germany, 2003. BibTeX
  14. Schellewald, C. and Schnörr, C. Subgraph Matching with Semidefinite Programming. In Proc. Int. Workshop on Combinatorial Image Analysis (IWCIA'03), Palermo, Italy, 2003. BibTeX
  15. Weber, S.; Schüle, T.; Schnörr, C. and Hornegger, J. A Linear Programming Approach to Limited Angle 3D Reconstruction from DSA Projections. In Bildverarbeitung für die Medizin 2003, pages 41-45, Springer Verlag, 2003. BibTeX
  16. Weber, S.; Schnörr, C. and Hornegger, J. A Linear Programming Relaxation for Binary Tomography with Smoothness Priors. In Proc. Int. Workshop on Combinatorial Image Analysis (IWCIA'03), Palermo, Italy, 2003. BibTeX

2002

  1. Bruhn, A.; 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, pages 290-297, Springer, Zürich, Switzerland, lncs 2449, 2002. BibTeX
  2. Bruhn, A.; Weickert, J. and Schnörr, C. Combining the Advantages of Local and Global Optic Flow Methods. In Pattern Recognition, Proc. 24th DAGM Symposium, pages 454-462, Springer, Zürich, Switzerland, lncs 2449, 2002. BibTeX
  3. Cremers, D.; Kohlberger, T. and Schnörr, C. Nonlinear Shape Statistics in Mumford-Shah Based Segmentation. In Computer Vision -- ECCV 2002), pages 93-108, Springer Verlag, lncs 2351, 2002. pdf  BibTeX
  4. Cremers, D. and Schnörr, C. Motion Competition: Variational Integration of Motion Segmentation and Shape Regularization. In Pattern Recognition, Proc. 24th DAGM Symposium, pages 472-480, Springer, Zürich, Switzerland, lncs 2449, 2002. BibTeX
  5. Cremers, D.; Tischhäuser, F.; Weickert, J. and Schnörr, C. Diffusion Snakes: Introducing Statistical Shape Knowledge into the Mumford--Shah functional. In Int. J. Computer Vision, 50 (3): 295-313, 2002. BibTeX
  6. Hinterberger, W.; Scherzer, O.; Schnörr, C. and Weickert, J. Analysis of Optical Flow Models in the Framework of Calculus of Variations. In Numer. Funct. Anal. Optimiz., 23 (1/2): 69-89, 2002. BibTeX
  7. Keuchel, J.; Naumann, S.; Heiler, M. and Siegmund, A. Automatic Land Cover Analysis for Tenerife by Supervised Classification using Remotely Sensed Data. In Remote Sensing of Environment, 2002. BibTeX
  8. Keuchel, J.; Schnörr, C.; Schellewald, C. and Cremers, D. Unsupervised Image Partitioning with Semidefinite Programming. In Pattern Recognition, Proc. 24th DAGM Symposium, pages 141-149, Springer, Zürich, Switzerland, lncs 2449, 2002. BibTeX
  9. Schellewald, C.; Roth, S. and Schnörr, C. Performance Evaluation of a Convex Relaxation Approach to the Quadratic Assignment of Relational Object Views. Technical Report 02/2002, Dept. Math. and Comp. Science, University of Mannheim, Germany, 2002. BibTeX

2001

  1. Cremers, D.; Kohlberger, T. and Schnörr, C. Nonlinear Shape Statistics via Kernel Spaces. In Mustererkennung 2001, pages 269-276, Springer, Munich, Germany, Lect. Notes Comp. Science 2191, 2001. pdf  BibTeX
  2. Cremers, D.; 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, pages 237-244, IEEE Comp. Soc., Vancouver, Canada, 2001. BibTeX
  3. Heers, J.; Schnörr, C. and Stiehl, H.S. Globally--Convergent Iterative Numerical Schemes for Non--Linear Variational Image Smoothing and Segmentation on a Multi--Processor Machine. In IEEE Trans. Image Proc., 10 (6): 852-864, 2001. BibTeX
  4. Heiler, M.; Cremers, D. and Schnörr, C. Efficient Feature Subset Selection for Support Vector Machines. Technical Report 21/2001, Dept. Math. and Comp. Science, University of Mannheim, Germany, 2001. BibTeX
  5. Keuchel, J.; Schellewald, C.; Cremers, D. and Schnörr, C. Convex Relaxations for Binary Image Partitioning and Perceptual Grouping. In Mustererkennung 2001, pages 353-360, Springer, Munich, Germany, Lect. Notes Comp. Science 2191, 2001. BibTeX
  6. Schellewald, C.; Keuchel, J. and Schnörr, C. Image labeling and grouping by minimizing linear functionals over cones. In Proc. Third Int. Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR'01), pages 267-282, Springer, INRIA, Sophia Antipolis, France, Lect. Notes Comp. Science 2134, 2001. BibTeX
  7. Schellewald, C.; Roth, S. and Schnörr, C. Evaluation of Convex Optimization Techniques for the Weighted Graph--Matching Problem in Computer Vision. In Mustererkennung 2001, pages 361-368, Springer, Munich, Germany, Lect. Notes Comp. Science 2191, 2001. BibTeX
  8. Schellewald, C.; Roth, S. and Schnörr, C. Application of convex optimization techniques to the relational matching of object views. Technical Report, Dept. Math. and Comp. Science, University of Mannheim, Germany, 2001. BibTeX
  9. . Schnörr, Statistische Mustererkennung. , Unterlagen zur Vorlesung. BibTeX
  10. Weickert, J.; Heers, J.; Schnörr, C.; Zuiderveld, K.--J.; Scherzer, O. and Stiehl, H.--S. Fast parallel algorithms for a broad class of nonlinear variational diffusion approaches. In Real--Time Imaging, 7 (1): 31-45, 2001. BibTeX
  11. Weickert, J. and Schnörr, C. Variational Optic Flow Computation with a Spatio-Temporal Smoothness Constraint. In J. Math. Imaging and Vision, 14 (3): 245-255, 2001. BibTeX
  12. Weickert, J. and Schnörr, C. A Theoretical Framework for Convex Regularizers in PDE--Based Computation of Image Motion. In Int. J. Computer Vision, 45 (3): 245-264, 2001. BibTeX
  13. Wiehler, K.; Heers, J.; Schnörr, C.; Stiehl, H.--S. and Grigat, R.--R. A 1D analog VLSI implementation for non-linear real-time signal preprocessing. In Real--Time Imaging, 7 (1): 127-142, 2001. BibTeX

2000

  1. Cremers, D.; Schnörr, C.; Weickert, J. and Schellewald, C. Learning Translation Invariant Shape Knowledge for Steering Diffusion-Snakes. In 3rd Workshop on Dynamic Perception, pages 117-122, Akad. Verlagsges., Berlin, Germany, Proc. in Artificial Intelligence 9, 2000. BibTeX
  2. Cremers, D.; Schnörr, C.; Weickert, J. and Schellewald, C. Diffusion Snakes Using Statistical Shape Knowledge. In Proc. Algebraic Frames for the Perception-Action Cycle, pages 164-174, Springer, Kiel, lncs 1888, 2000. BibTeX
  3. Schnörr, C. Variational Adaptive Smoothing and Segmentation. In Computer Vision and Applications: A Guide for Students and Practitioners, pages 459-482, Academic Press, San Diego, 2000. BibTeX
  4. Schnörr, C. and Weickert, J. Variational Image Motion Computation: Theoretical Framework, Problems and Perspectives. In Mustererkennung 2000, Springer, Kiel, Germany, Informatik aktuell , 2000. BibTeX
  5. Weickert, J. and Schnörr, C. PDE--Based Preprocessing of Medical Images. In Künstliche Intelligenz, 3: 5-10, 2000. BibTeX
  6. Wulf, M.; Stiehl, H.S. and Schnörr, C. On the computational r^ole of the primate retina. In Proc. 2nd ICSC Symposium on Neural Computation (NC 2000), Berlin, Germany, 2000. BibTeX
  7. Schnörr, C., ed. Künstliche Intelligenz: Special Issue on Medical Computer Vision. , 2000. BibTeX

1999

  1. Heers, J.; Schnörr, C. and Stiehl, H.S. Investigating a class of iterative schemes and their parallel implementation for nonlinear variational image smoothing and segmentation. Technical Report 283/99, Comp. Sci. Dept., AB KOGS, University of Hamburg, Germany, 1999. BibTeX
  2. Peckar, W.; Schnörr, C.; Rohr, K. and Stiehl, H.--S. Parameter-Free Elastic Deformation Approach for 2D and 3D Registration Using Prescribed Displacements. In J. Math. Imaging and Vision, 10 (2): 143-162, 1999. BibTeX
  3. Schnörr, C. Variational Methods for Adaptive Image Smoothing and Segmentation. In Handbook on Computer Vision and Applications: Signal Processing and Pattern Recognition, pages 451-484, Academic Press, San Diego, 1999. BibTeX
  4. Weickert, J. and Schnörr, C. Räumlich--zeitliche Berechnung des optischen Flusses mit nichtlinearen flussabhängigen Glattheitstermen. In Mustererkennung 1999, pages 317-324, Springer, Informatik aktuell , 1999. BibTeX
  5. Wulf, M.; Stiehl, H.S. and Schnörr, C. A model of spatiotemporal receptive fields in the primate retina. In Proc. 1st Göttingen Conf. German Neurosci. Soc., 1999. BibTeX
  6. Wulf, M.; Stiehl, H.S. and Schnörr, C. Modeling spatiotemporal receptive fields in the primate retina. In Proc. Cognitive Neurosci. Conf., Bremen, Germany, 1999. BibTeX

1998

  1. Heers, J.; Schnörr, C. and Stiehl, H.S. A class of parallel algorithms for nonlinear variational image segmentation. In Proc. Noblesse Workshop on Non--Linear Model Based Image Analysis (NMBIA'98), Glasgow, Scotland, 1998. BibTeX
  2. Heers, J.; Schnörr, C. and Stiehl, H.--S. Investigation of Parallel and Globally Convergent Iterative Schemes for Nonlinear Variational Image Smoothing and Segmentation. In Proc. IEEE Int. Conf. Image Proc., Chicago, 1998. BibTeX
  3. Heers, J.; Schnörr, C. and Stiehl, H.--S. Parallele und global konvergente iterative Minimierung nichtlinearer Variationsansätze zur adaptiven Glättung und Segmentation von Bildern. In Mustererkennung 1998, Springer, Heidelberg, Informatik aktuell , 1998. BibTeX
  4. Peckar, W.; Schnörr, C.; Rohr, K. and Stiehl, H.S. Non-Rigid Image Registration Using a Parameter-Free Elastic Model. In 9th British Machine Vision Conference (BMVC`98), pages 134-143, Southampton/UK, 1998. BibTeX
  5. Peckar, W.; Schnörr, C.; Rohr, K.; Stiehl, H.--S. and Spetzger, U. Linear and Incremental Estimation of Elastic Deformations in Medical Registration Using Prescribed Displacements. In Machine Graphics & Vision, 7 (4): 807-829, 1998. BibTeX
  6. Schnörr, C. Variational approaches to Image Segmentation and Feature Extraction. Ph.D. Thesis, University of Hamburg, Comp. Sci. Dept., Hamburg, Germany, 1998. BibTeX
  7. Schnörr, C. A Study of a Convex Variational Diffusion Approach for Image Segmentation and Feature Extraction. In J. of Math. Imag. and Vision, 8 (3): 271-292, 1998. BibTeX
  8. Wiehler, K.; Grigat, R.--R.; Heers, J.; Schnörr, C. and Stiehl, H.S. Dynamic Circular Cellular Networks for Adaptive Smoothing of Multi--Dimensional Signals. In Proc. 5th IEEE Int. Workshop on Cellular Neural Networks and their Applications, London, 1998. BibTeX
  9. Wiehler, K.; Grigat, R.--R.; Heers, J.; Schnörr, C. and Stiehl, H.--S. Real--Time Adaptive Smoothing with a 1D Nonlinear Relaxation Network in Analogue VLSI Technology. In Mustererkennung 1998, Springer, Heidelberg, Informatik aktuell , 1998. BibTeX

1997

  1. Fornland, P. 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. BibTeX
  2. Gerloff, S.; Hagemann, A.; Schnörr, C.; Tieck, S.; Stiehl, H.S.; Dombrowski, R.; Dreyer, M. and Wiesendanger, R. Semi--Automated Analysis of SXM Images. In Proc. 9th Int. Conf. on Scanning Tunneling Microscopy/Spectroscopy and Related Techniques (STM'97), Hamburg, Germany, 1997. BibTeX
  3. Peckar, W.; Schnörr, C.; Rohr, K. and Stiehl, H.S. Two-Step Parameter-Free Elastic Image Registration with Prescribed Point Displacements. In Proc. 9th Int. Conf. on Image Analysis and Processing (ICIAP'97), Florence, Italy, 1997. BibTeX

1996

  1. Schnörr, C. Repräsentation von Bilddaten mit einem konvexen Variationsansatz. In Mustererkennung 1996, pages 21-28, Springer-Verlag, Berlin, Heidelberg, Informatik aktuell , 1996. BibTeX
  2. Schnörr, C. Representation of Images by a Convex Variational Diffusion Approach. Technical Report FBI-HH-M-256/96, FB Informatik, Universität Hamburg, 1996. BibTeX
  3. Schnörr, C.; Sprengel, R. and Neumann, B. A Variational Approach to the Design of Early Vision Algorithms. In Computing Suppl., 11: 149-165, 1996. BibTeX
  4. Schnörr, C.; Stiehl, H.-S. and Grigat, R.-R. On Globally Asymptotically Stable Continuous-Time CNNs for Adaptive Smoothing of Multidimensional Signals. In Proc. 4th IEEE Int. Workshop on Cellular Neural Networks and their Applications, Seville, Spain, 1996. BibTeX
  5. Schnörr, C. Convex Variational Segmentation of Multi-Channel Images. In Proc. 12th Int. Conf. on Analysis and Optimization of Systems: Images, Wavelets and PDE's, Springer-Verlag, Paris, Lect. Notes in Control and Information Sciences 219, 1996. BibTeX

1995

  1. Schnörr, C. and Peckar, W. Motion-Based Identification of Deformable Templates. In Proc. 6th Int. Conf. on Computer Analysis of Images and Patterns (CAIP '95), pages 122-129, Springer Verlag, Prague, Czech Republic, Lect. Notes in Comp. Sci. 970, 1995. BibTeX

1994

  1. Heikkonen, J.; Koikkalainen, P. and Schnörr, C. Building Trajectories via Selforganization from Spatiotemporal Features. In 12th Int. Conf. on Pattern Recognition, Jerusalem, Israel, 1994. BibTeX
  2. Schnörr, C. Bewegungssegmentation von Bildfolgen durch die Minimierung konvexer nicht-quadratischer Funktionale. In Mustererkennung 1994, pages 178-185, Technische Universität Wien, Informatik Xpress 5, 1994. BibTeX
  3. Schnörr, C. Segmentation of Visual Motion by Minimizing Convex Non-Quadratic Functionals. In 12th Int. Conf. on Pattern Recognition, Jerusalem, Israel, 1994. BibTeX
  4. Schnörr, C. Unique Reconstruction of Piecewise Smooth Images by Minimizing Strictly Convex Non-Quadratic Functionals. In jmiv, 4: 189-198, 1994. BibTeX
  5. Schnörr, C. and Sprengel, R. A Nonlinear Regularization Approach to Early Vision. In Biol. Cybernetics, 72: 141-149, 1994. BibTeX
  6. Sprengel, R.; Schnörr, C. and Neumann, B. Detection of Visual Data Transitions in Nonlinear Parameter Space. In Mustererkennung 1994, pages 315-323, Technische Universität Wien, Informatik Xpress 5, 1994. BibTeX

1993

  1. Rohr, K. and Schnörr, C. An Efficient Approach to the Identification of Characteristic Intensity Variations. In ivc, 11 (5): 273-277, 1993. BibTeX
  2. Schnörr, C. On Functionals with Greyvalue-Controlled Smoothness Terms for Determining Optical Flow. In pami, 15 (10): 1074-1079, 1993. BibTeX
  3. Schnörr, C.; Niemann, H. and Kopecz, J. Architekturkonzepte zur Bildauswertung. In Grundlagen und Anwendungen der Künstlichen Intelligenz, 17. Fachtagung für Künstliche Intelligenz, pages 268-274, Springer-Verlag, Berlin, 1993. BibTeX
  4. Sprengel, R. and Schnörr, C. Nichtlineare Diffusion zur Integration visueller Daten - Anwendung auf Kernspintomogramme. In Mustererkennung 1993, 15. DAGM-Symposium, pages 134-141, Springer Verlag, 1993. BibTeX

1992

  1. Schnörr, C. Computation of Discontinuous Optical Flow by Domain Decomposition and Shape Optimization. In ijcv, 8 (2): 153-165, 1992. BibTeX
  2. Schnörr, C. and Neumann, B. Ein Ansatz zur effizienten und eindeutigen Rekonstruktion stückweise glatter Funktionen. In Mustererkennung 1992, 14. DAGM-Symposium, pages 411-416, Springer-Verlag, Dresden, 1992. BibTeX

1991

  1. Schnörr, C. Funktionalanalytische Methoden zur Bestimmung von Bewegungsinformation aus TV-Bildfolgen. Ph.D. Thesis, Fakultät für Informatik, Universität Karlsruhe (TH), 1991. BibTeX
  2. Schnörr, C. Determining Optical Flow for Irregular Domains by Minimizing Quadratic Functionals of a Certain Class. In ijcv, 6 (1): 25-38, 1991. BibTeX

1990

  1. Bister, D.; Rohr, K. and Schnörr, C. Automatische Bestimmung der Trajektorien von sich bewegenden Objekten aus einer Grauwertbildfolge. In Mustererkennung 1990, 12. DAGM-Symposium, pages 44-51, Springer-Verlag, Oberkochen-Aalen, Informatik-Fachberichte 254, 1990. BibTeX
  2. Schnörr, C. Computation of Discontinuous Optical Flow by Domain Decomposition and Shape Optimization. In Proc. British Machine Vision Conference, pages 109-114, Oxford/UK, 1990. BibTeX

1989

  1. Schnörr, C. Zur Schätzung von Geschwindigkeitsvektorfeldern in Bildfolgen mit einer richtungsabhängigen Glattheitsforderung. In Mustererkennung 1989, 11. DAGM-Symposium, pages 294-301, Springer-Verlag, Hamburg, Informatik-Fachberichte 219, 1989. BibTeX

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