Publications

2016

  1. Silvestri, F.; Reinelt, G. and Schnörr, C.
    Symmetry-free SDP Relaxations for Affine Subspace Clustering. ArXiv, preprint 2016, www pdf
  2. 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, in press, 2016, www pdf
  3. Desana, M. and Schnörr, C.
    Expectation Maximization for Sum-Product Networks as Exponential Family Mixture Models. 2016, www pdf
  4. 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, 2016, pdf
  5. Zisler, M.; Kappes, J.H.; Schnörr, Cl.; Petra, S. and Schnörr, Ch.
    Ch. Non-Binary Discrete Tomography by Continuous Non-Convex Optimization. In IEEE Comp. Imaging, in press, 2016,
  6. Åström, F. and Schnörr, C.
    A Geometric Approach to Color Image Regularization. 2016, pdf arXiv
  7. Åström, F.; Petra, S.; Schmitzer, B. and Schnörr, C.
    Image Labeling by Assignment, 2016. pdf arXiv
  8. Jaroudi, R.; Baravdish, G.; Åström, F. and Johansson, B.T.
    Source Localization of Reaction-Diffusion Models for Brain Tumors.
    In German Conference on Pattern Recognition (GCPR), Hannover, Germany, Springer, 2016.
  9. Åström, F.
    Color Image Regularization via Channel Mixing and Half Quadratic Minimization.
    In International Conference on Image Processing (ICIP), Phoenix, USA, 2016.
  10. Åström, 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) held in conjunction with CVPR, Las Vegas, USA, (Grenander best paper award), 2016. [pdf]
  11. Berger, J. and Schnörr, C.
    Joint Recursive Monocular Filtering of Camera Motion and Disparity Map.
    In 38th German Conference on Pattern Recognition, Springer, Hannover, 2016. arXiv  pdf  Supplemental Material
  12. Bodnariuc, E.; Petra, S.; Poelma, C. and Schnörr, C.
    Parametric Dictionary-Based Velocimetry for Echo PIV.
    In 38th German Conference on Pattern Recognition, Springer, Hannover, 2016.
  13. 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

2015

  1. Kappes, J.H.; Petra, S.; Schnörr, C. and Zisler, M.
    TomoGC: Binary Tomography by Constrained Graph Cuts..
    In Proc. GCPR, 2015. pdf
  2. Åström, F.; Felsberg, M. and Scharr, H.
    Adaptive Sharpening of Multimodal Distributions.
    In Colour and Visual Computing Symposium (CVCS), Gjovik, Norway, 2015. doi
  3. Åström, F. and Schnörr, C.
    On Coupled Regularization for Non-Convex Variational Image Enhancement.
    In Asian Conference on Pattern Recognition (ACPR), Kuala Lumpur, Malaysia, 2015. doi
  4. Silvestri, F.; Reinelt, G. and Schnörr, C.
    A Convex Relaxation Approach to the Affine Subspace Clustering Problem.
    In Proc. GCPR, 2015. pdf
  5. 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
  6. 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
  7. Bodnariuc, E.; Gurung, A.; Petra, S. and Schnörr, C.
    Adaptive Dictionary-Based Spatio-temporal Flow Estimation for Echo PIV.
    In EMMCVPR, Springer, 2015. pdf doi
  8. 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
  9. 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), Springer, 2015. doi
  10. 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, 2015. arXiv  pdf 
  11. 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 38th German Conference on Pattern Recognition, Springer, Aachen, 2016. pdf doi  
  12. 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
  13. 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
  14. Lenzen, F. and Berger, J.
    Solution-Driven Adaptive Total Variation Regularization.
    In 38th German Conference on Pattern Recognition, Springer, 2015. doi 
  15. 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
  16. 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

2014

  1. Becker, F.; Petra, S. and Schnörr, C.
    Optical Flow. In Handbook of Mathematical Methods in Imaging, Springer, 2014. (in press) 
  2. Beier, T.; Kroeger, T.; Kappes, J. H.; Koethe, U. and Hamprecht, F.
    Cut, Glue & Cut: A Fast, Approximate Solver for Multicut Partitioning.
    In IEEE Conference on Computer Vision and Pattern Recognition 2014, 2014. (Accepted as Oral) 
  3. Denictiu, 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, Springer, LNCS , 2014.
  4. Eigenstetter, A.; Takami, M. and Ommer, B.
    Randomized Max-Margin Compositions for Visual Recognition.
    In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014.
  5. 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 
  6. 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. (urlhttp://hci.iwr.uni-heidelberg.de/opengm2/
  7. Kroeger, T.; Kappes, J. H.; Beier, T.; Koethe, K. and Hamprecht, F. A.
    Asymmetric Cuts: Joint Image Labeling and Partitioning.
    In 36th German Conference on Pattern Recognition, 2014.
  8. Màté, G. and Heermann, D. W.
    Persistence Intervals of Fractals.
    In Physica A, in press, 2014.
  9. Màté, G.; Hofmann, A.; Wenzel, N. and Heermann, D. W.
    A Topological Similarity Measure for Proteins.
    In Biochimica et Biophysica Acta (BBA) - Biomembranes, 1838 (4): 1180-1190, 2014. doi 
  10. Petra, S. and Schnörr, C.
    Average Case Recovery Analysis of Tomographic Compressive Sensing.
    In Linear Algebra and its Applications, 441: 168-198, 2014. (Special issue on Sparse Approximate Solution of Linear Systems) 
  11. 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.
  12. Savchynskyy, B. and Schmidt, S.
    Getting Feasible Variable Estimates From Infeasible Ones: MRF Local Polytope Study.
    In Advanced Structured Prediction, MIT Press, 2014.
  13. Schmitzer, B. and Schnörr, C. Globally Optimal Joint Image Segmentation and Shape Matching based on Wasserstein Modes. Preprint. pdf  
  14. 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. (Oral - Best student Paper Award

2013

  1. Antic, B.; Milbich, T. and Ommer, B.
    Less is More: Video Trimming for Action Recognition.
    In Proceedings of the International Conference on Computer Vision (ICCV-HACI), 2013.
  2. Bachl, F. E.; Fieguth, P. and Garbe, C. S.
    Bayesian Inference on Integrated Continuity Fluid Flows and their Application to Dust Aerosols.
    In Proceedings of the International Geoscience and Remote Sensing Symposium 2013, pages 2246-2249, 2013.
  3. Bachl, F. E.; Lenkoski, A. and Thorarinsdottir, Thordis.~Land G. Christoph.~S.
    Bayesian Motion Estimation for Dust Aerosols.
    In ArXiv e-prints, 2013.
  4. 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. doi 
  5. Bendel, V.; Ueltzhöffer, K.J.; Freitag, J.; Kipfstuhl, S.; Kuhs, W.F.; Garbe, C.~S. and Faria, S.H.
    High-resolution variations in size, number, and arrangement of air bubbles in the EPICA DML ice core.
    In Journal of Glaciology, 59: 972-980, 2013.
  6. Binder, T.; Garbe, C.~S.; Wagenbach, D.; Freitag, J. and Kipfstuhl, S.
    Extraction and parameterization of grain boundary networks, using a dedicated method of automatic image analysis.
    In Journal of Microscopy, 250: 130-141, 2013.
  7. Binder, T.; Weikusat, I.; Freitag, J.; Garbe, C.~S.; Wagenbach, D. and Kipfstuhl, S.
    Microstructure through an ice sheet.
    In Materials Science Forum, 753: 481-484, 2013.
  8. Bonato, T.; Jünger, M.; Reinelt, G. and Rinaldi, G.
    Lifting and separation procedures for the cut polytope.
    In Mathematical Programming, 2013. (DOI: 10.1007/s10107-013-0688-2) 
  9. Bruno, F.; Cocchi, D.; Greco, F. and Scardovi, E.
    Spatial reconstruction of rainfall fields from rain gauge and radar data.
    In Stoch Environ Res Risk Assess, 2013.
  10. Buttgereit, A.; Weber, C.; Garbe, C.~S. and Friedrich, O.
    From chaos to split ups - SHG microscopy reveals a specific remodeling mechanism in aging dystrophic muscle.
    In Journal of Pathology, 229: 477-485, 2013.
  11. Dahlhaus, R. and Neddermeyer, J. C.
    Online Spot Volatility-Estimation and Decomposition with Nonlinear Market Microstructure Noise Models.
    In Journal of Financial Econometrics, 2013.
  12. Denictiu, A.; Petra, S.; Schnörr, C. and Schnörr, C.
    Phase Transitions and Cosparse Tomographic Recovery of Compound Solid Bodies From Few Projections.
    .
  13. Didden, E.-M.; Thorarinsdottir, T. L.; Lenkoski, A. and Schnörr, C.
    Shape from Texture using Locally Scaled Point Processes.
    In ArXiv e-prints, abs/1311.7041, 2013.
  14. Diego, F. and Hamprecht, F. A.
    Learning Multi-Level Sparse Representation.
    In NIPS. Proceedings, 2013. (in press) 
  15. Diego, F. and Hamprecht, F. A.
    Learning Multi-Level Sparse Representation for Identifying Neuronal Activity.
    In Signal Processing with Adaptive Sparse Structured Representations workshop (SPARS). Book of Abstracts., 2013.
  16. Dueck, J.; Edelmann, D.; Gneiting, T. and Richards, D.
    The Affinely Invariant Distance Correlation.
    In Bernoulli, in press, 2013. pdf 
  17. Edelmann, D. and Wichelhaus, C.
    Nonparametric Inference for Queueing Networks of $Geom^X/G/infty$-Queues in Discrete Time.
    In Advances in Applied Probability, in press, 2013.
  18. Feinauer, C. J.; Hofmann, A.; Goldt, S.; Liu, L.; Màté, G. and Heermann, D. W.
    Chapter Three - Zinc Finger Proteins and the 3D Organization of Chromosomes.
    In Organisation of Chromosomes, pages 67-117, Academic Press, Advances in Protein Chemistry and Structural Biology 90, 2013.
  19. Fiedler, J.
    From Fourier to Gegenbauer: Dimension walks on spheres.
    In ArXiv e-prints: 1-12, 2013. pdf 
  20. Garbe, C.~S. and Ommer, B.
    Parameter Estimation in Image Processing and Computer Vision.
    In Model Based Parameter Estimation: Theory and Applications, pages 311-334, Springer-Verlag, Contributions 311 in Mathematical and Computational Sciences , 2013.
  21. Gneiting, T.
    Strictly and non-strictly positive definite functions on spheres.
    In Bernoulli (in press), 2013.
  22. 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.
    A Comparative Study of Modern Inference Techniques for Discrete Energy Minimization Problems.
    In CVPR, 2013. pdf 
  23. Kappes, J. H.; Speth, M.; Reinelt, G. and Schnörr, C.
    Higher-order Segmentation via Multicuts.
    In ArXiv e-prints, 2013. pdf  (http://arxiv.org/abs/1305.6387
  24. 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 
  25. Kroeger, T.; Mikula, S.; Denk, W.; Koethe, U. and Hamprecht, F. A.
    Learning to Segment Neurons with non-local Quality Measures.
    In MICCAI, 2013.
  26. Kröger, T.; Mikula, S.; Denk, W.; Köthe, U. and Hamprecht, F. A.
    Learning to Segment Neurons with Non-local Quality Measures.
    In MICCAI 2013. Proceedings, in press, 2013.
  27. 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.
  28. Lellmann, J.; Lellmann, B.; Widmann, F. and Schnörr, C.
    Discrete and Continuous Models for Partitioning Problems.
    In Int.~J.~Comp.~Vision, 2013. pdf  (in press) 
  29. 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: 1-19, 2013. pdf 
  30. Lenkoski, A.
    A Direct Sampler for G-Wishart Variates.
    In Stat, 2: 119-128, 2013.
  31. Lenkoski, A.; Eicher, T.~S. and Raftery, A.~E.
    Two-stage Bayesian Model Averaging and the Endogenous Variable Model.
    In Accepted, Econometric Reviews, 2013.
  32. 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, Springer, LNCS , 2013. pdf 
  33. Lenzen, F.; Becker, F.; Lellmann, J.; Petra, S. and Schnörr, C.
    A Class of Quasi-Variational Inequalities for Adaptive Image Denoising and Decomposition.
    In Comput. Optim. Appl., 54 (2): 371-398, 2013. pdf 
  34. Lenzen, F.; Kim, K.I.; Nair, R.; Meister, S.; Schäfer, H.; Becker, F.; Garbe, C.~S. and Theobalt, C.
    Denoising Strategies for Time-of-Flight Data.
    In Time-of-Flight Imaging: Algorithms, Sensors and Applications, Springer-Verlag, LNCS , 2013. (accepted) 
  35. Lenzen, F.; Kim, K. In; Schäfer, H.; Nair, R.; Meister, S.; Becker, F.; Garbe, C. 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.
  36. Möller, A.; Lenkoski, A. and Thorarinsdottir, T.~L.
    Multivariate probabilistic forecasting using Bayesian model averaging and copulas.
    In Quarterly Journal of the Royal Meteorological Society, 139 (673): 982-991, 2013.
  37. Màté, G.; Hofmann, A.; Wenzel, N. and Heermann, D. W.
    A topological similarity measure for proteins.
    In Biochimica et Biophysica Acta (BBA) - Biomembranes, 2013. doi 
  38. Nair, R.; Ruhl, K.; Lenzen, F.; Meister, S.; Schäfer, H.; Garbe, C.~S.; Eisemann, M. and Kondermann, D.
    A Survey on Time-of-Flight Stereo Fusion.
    In Time-of-Flight Imaging: Algorithms, Sensors and Applications, Springer-Verlag, LNCS , 2013. (accepted) 
  39. Nair, R.; Ruhl, K.; Lenzen, F.; Meister, S.; Schäfer, H.; Garbe, C. 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.
  40. 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 
  41. Petra, S.; Schnörr, C.; Becker, F. and Lenzen, F.
    B-SMART: Bregman-Based First-Order Algorithms for Non-Negative Compressed Sensing Problems.
    In SSVM, pages 110-124, Springer, LNCS 7893, 2013.
  42. 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. (Accepted) 
  43. Savchynskyy, B. and Schmidt, S.
    Getting Feasible Variable Estimates From Infeasible Ones: MRF Local Polytope Study.
    In Workshop on Inference for Probabilistic Graphical Models at ICCV 2013, 2013.
  44. Scheuerer, M.
    Probabilistic Quantitative Precipitation Forecasting Using Ensemble Model Output Statistics.
    In Quart. J. Roy. Meteor. Soc., 2013. (to appear) 
  45. Scheuerer, M. and König, G.
    Gridded locally calibrated, probabilistic temperature forecasts based on ensemble model output statistics.
    In Quarterly Journal of the Royal Meteorological Society: n/a-n/a, 2013.
  46. Schiegg, M.; Hanslovsky, P.; Kausler, B. X.; Hufnagel, L. and Hamprecht, F. A.
    Conservation Tracking.
    In ICCV 2013. Proceedings, in press, 2013.
  47. 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 
  48. 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 
  49. Schmitzer, B. and Schnörr, C.
    Contour Manifolds and Optimal Transport.
    , preprint. pdf  (preprint) 
  50. Schmitzer, B. and Schnörr, C.
    A Hierarchical Approach to Optimal Transport.
    In Scale Space and Variational Methods (SSVM 2013), pages 452-464, 2013. pdf 
  51. Schnieders, J.; Garbe, C.~S.; Peirson, W.L.; Smith, G.B. and Zappa, C.J.
    Analyzing the footprints of near surface aqueous turbulence - an image processing based approach.
    In Journal of Geophysical Research, 118: 1272-1286, 2013.
  52. Schäfer, H.; Lenzen, F. and Garbe, C. S.
    Depth and Intensity based Edge Detection in Time-of-Flight Images.
    In 3DTV-Conference, 2013 International Conference on, pages 111-118, 2013.
  53. Swoboda, P.; Savchynskyy, B.; Kappes, J. H. and Schnörr, C.
    Partial Optimality via Iterative Pruning for the Potts Model.
    In SSVM, 2013. pdf 
  54. Swoboda, P. and Schnörr, C.
    Variational Image Segmentation and Cosegmentation with the Wasserstein Distance.
    In EMMCVPR, Springer, 2013.
  55. Tek, B. F.; Kroeger, T.; Mikula, S. and Hamprecht, F. A.
    Automated Cell Nucleus Detection for Large-Volume Electron Microscopy of Neural Tissue.
    , 2013.
  56. Tsai, W.-T.; Chen, S.-m.; Lu, G.-h. and Garbe, C.~S.
    Characteristics of interfacial signatures on a wind-driven gravity-capillary wave.
    In Journal of Geophysical Research, 118: 1715–1735, 2013.

2012

  1. Andres, B.; Beier, T. and Kappes, J. H.
    OpenGM: A C++ Library for Discrete Graphical Models.
    In ArXiv e-prints, 2012.
  2. Andres, B.; Köthe, U.; Kröger, T.; Helmstaedter, M.; Briggman, K.L.; Denk, W. and Hamprecht, F. A.
    3D Segmentation of SBFSEM Images of Neuropil by a Graphical Model over Supervoxel Boundaries.
    In Medical Image Analysis, 16 (2012): 796-805, 2012. (1) 
  3. Andres, B.; Kappes, J. H.; Beier, T.; Köthe, U. and Hamprecht, F. A.
    The Lazy Flipper: Efficient Depth-limited Exhaustive Search in Discrete Graphical Models.
    In ECCV 2012. Proceedings, 2012. pdf  (1) 
  4. Andres, B.; Kröger, T.; Briggmann, K. L.; Denk, W.; Norogod, N.; Knott, G.; Köthe, U. and Hamprecht, F. A.
    Globally Optimal Closed-Surface Segmentation for Connectomics.
    In ECCV 2012. Proceedings, Part 3, pages 778-791, LNCS , 2012. (1) 
  5. Antic, B. and Ommer, B.
    Robust Multiple-Instance Learning with Superbags.
    In Proceedings of the Asian Conference on Computer Vision (ACCV), 2012.
  6. Bachl, F. E.; Fieguth, P. and Garbe, C. S.
    A Bayesian Approach to Spaceborn Hyperspectral Optical Flow Estimation on Dust Aerosols.
    In Proceedings of the International Geoscience and Remote Sensing Symposium 2012, pages 256-259, 2012.
  7. Bachl, F. E. and Garbe, C. S.
    Classifying and Tracking Dust Plumes from Passive Remote Sensing.
    In Proceedings of the ESA, SOLAS & EGU Joint Conference 'Earth Observation for Ocean-Atmosphere Interaction Science', pages S1-3, European Space Agency Communications, Frascati, Italy, ESA Special Publication 703, 2012.
  8. Becker, F.; Wieneke, B.; Petra, S.; Schröder, A. and Schnörr, C.
    Variational Adaptive Correlation Method for Flow Estimation.
    In IEEE Trans. Image Processing, 21 (6): 3053-3065, 2012.
  9. Cheng, Y. and Lenkoski, A.
    Hierarchical Gaussian Graphical Models: Beyond Reversible Jump.
    In Electronic Journal of Statistics, 6: 2309-2331, 2012.
  10. Contreras, I.; Fernandez, E. and Reinelt, G.
    Minimizing the maximum travel time in a combined model of facility location and network design.
    In Omega, 40 (6): 847-860, 2012.
  11. Dahlhaus, R.
    Locally Stationary Processes.
    In Handbook of Statistics, 30: 351-413, 2012.
  12. Dahlhaus, R. and Neddermeyer, J.C.
    On the relationship between the theory of cointegration and the theory of phase synchronization.
    In ArXiv e-prints, 2012.
  13. Eicher, T.~S.; Helfman, L. and Lenkoski, A.
    Robust FDI Determinants.
    In Journal of Macroeconomics, 34, 2012.
  14. Eicher, T. S.; Helfman, L. and Lenkoski, A.
    Robust FDI Determinants.
    In Journal of Maroeconomics, 34: 637-651, 2012.
  15. Eigenstetter, A. and Ommer, B.
    Visual Recognition using Embedded Feature Selection for Curvature Self-Similarity.
    In Advances in Neural Information Processing Systems (NIPS), MIT Press, 2012.
  16. Eigenstetter, A.; Yarlagadda, P. and Ommer, B.
    Max-Margin Regularization for Reducing Accidentalness in Chamfer Matching.
    In Proceedings of the Asian Conference of Computer Vision (ACCV), Springer, 2012.
  17. Funke, J.; Andres, B.; Hamprecht, F. A.; Cardona, A. and Cook, M.
    Efficient Automatic 3D-Reconstruction of Branching Neurons from EM Data.
    In CVPR 2012. Proceedings, 2012. pdf  (1) 
  18. Garbe, C.~S.; Buttgereit, A.; Schürmann, S. and Friedrich, O.
    Automated Multiscale Morphometry of Muscle Disease From Second Harmonic Generation Microscopy Using Tensor-Based Image Processing.
    In Biomedical Engineering, IEEE Transactions on, 59 (1): 39-44, 2012.
  19. Herzog, A.G.; Voss, B.M.; Keilberg, D.; Hot, E.; Søgaard-Andersen, L.; Garbe, C.~S. and Kostina, E.A.
    A strategy for identifying fluorescence intensity profiles of single rod-shaped cells.
    In Journal of Bioinformatics and Computational Biology, 2012. (in print) 
  20. Kappes, J. H.; Savchynskyy, B. and Schnörr, C.
    A Bundle Approach To Efficient MAP-Inference by Lagrangian Relaxation.
    In CVPR 2012, 2012. pdf 
  21. Kausler, B. X.; Schiegg, M.; Andres, B.; Lindner, M.; Köthe, U.; Leitte, H.; Wittbrodt, J.; Hufnagel, L. and Hamprecht, F. A.
    A Discrete Chain Graph Model for 3d+t Cell Tracking with High Misdetection Robustness.
    In ECCV 2012. Proceedings, 2012. pdf  (1) 
  22. Lenkoski, A.; Eicher, T. S. and Raftery, A. E.
    Two-Stage Bayesian Model Averaging in the Endogenous Variable Model.
    In Accepted, Econometric Reviews, 2012.
  23. 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 (SSVM) 2011, pages 206-217, Springer, LNCS 6667, 2012. pdf 
  24. Liu, C.; Fieguth, P. and Garbe, C.~S.
    Background Subtraction and Dust Storm Detection.
    In IEEE International Geoscience and Remote Sensing Symposium, pages 2179-2181, IEEE, Munich, Germany, 2012.
  25. Lou, X. and Hamprecht, F. A.
    Structured Learning from Partial Annotations.
    In ICML 2012. Proceedings, 2012. (1) 
  26. Monroy, A. and Ommer, B.
    Beyond Bounding-Boxes: Learning Object Shape by Model-driven Grouping.
    In Proceedings of the European Conference on Computer Vision (ECCV), pages 582-595, Springer, 2012.
  27. Nair, R.; Lenzen, F.; Meister, S.; Schäfer, H.; Garbe, C. 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, Lecture Notes in Computer Science 7584, 2012.
  28. Nicola, A.; Petra, S.; Popa, C. and Schnörr, C.
    A General Extending and Constraining Procedure for Linear Iterative Methods.
    In Int.~J.~Comp.~Math., 89 (2): 231-253, 2012.
  29. Savchynskyy, B. and Schmidt, S.
    Getting Feasible Variable Estimates From Infeasible Ones: MRF Local Polytope Study.
    Technical Report, arXiv:1210.4081, 2012. pdf 
  30. Savchynskyy, B.; Schmidt, S.; Kappes, J. H. and Schnörr, C.
    Efficient MRF Energy Minimization via Adaptive Diminishing Smoothing.
    In UAI 2012, 2012. pdf 
  31. Scardovi, E.; Bruno, F.; Amorati, R. and Cocchi, D.
    Rainfall spatial modeling from different data sources.
    In Proceedings of the VI International Workshop on Spatio-Temporal Modelling (METMA6). Guimares, Portugal, 12-14 September 2012, 1-4, 2012.
  32. Scheuerer, M. and Schlather, M.
    Covariance models for divergence-free and curl-free random vector fields.
    In Stoch. Models, 28 (3): 433-451, 2012.
  33. 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 
  34. Thorarinsdottir, T.~L.; Scheuerer, M. and Feldmann, K.
    Statistical post-processing of ensemble forecasts.
    In PROMET, 37 ((3/4)): 43-52, 2012.
  35. Voss, B.; Stapf, J.; Berthe, A. and Garbe, C.~S.
    Bichromatic Particle Streak Velocimetry bPSV.
    In Experiments in Fluids, 53: 1405-1420, 2012.
  36. Yarlagadda, P. and Ommer, B.
    From Meaningful Contours to Discriminative Object Shape.
    In Proceedings of the European Conference on Computer Vision, 2012.
  37. Zeller, W.; Mayle, M.; Bonato, T.; Reinelt, G. and Schmelcher, P.
    Spectra and ground states of one- and two-dimensional laser-driven lattices of ultracold Rydberg atoms.
    In Physical Review A, 85(6), 2012.

2011

  1. Andres, B.; Kappes, J. H.; Beier, T.; Köthe, U. and Hamprecht, F. A.
    Probabilistic Image Segmentation with Closedness Constraints.
    In ICCV, Proceedings, pages 2611-2618, 2011. pdf  (1) 
  2. Antic, B. and Ommer, B.
    Video Parsing for Abnormality Detection.
    In Proceedings of the International Conference on Computer Vision (ICCV), 2011.
  3. 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 Proceedings of the 2011 International Conference on Computer Vision, pages 1692-1699, IEEE Computer Society, Washington, DC, USA, ICCV '11 , 2011.
  4. Dobra, A. and Lenkoski, A.
    Copula Gaussian graphical models and their application to modeling functional disability data.
    In Annals of Applied Statistics, 5: 969-993, 2011.
  5. Dobra, A.; Lenkoski, A. and Rodriguez, A.
    Bayesian inference for general Gaussian graphical models with application to multivariate lattice data.
    In Journal of the American Statistical Association, 106: 1418-1433, 2011.
  6. Gneiting, T.
    Making and evaluating point forecasts.
    In Journal of the American Statistical Association: 106:746-762, 2011.
  7. Gottfried, J.-M.; Fehr, J. and Garbe, C.~S.
    Computing range flow from multi-modal Kinect data.
    In Advances in Visual Computing, ISVC 2011, pages 758-767, Springer, 2011.
  8. Hansen, L.~V.; Thorarinsdottir, T.~L. and Gneiting, T.
    L'evy particles: Modelling and simulating star-shaped random sets, Research Report 2011/04, Centre for Stochastic Geometry and Advanced Bioimaging, University of Aarhus.
    . pdf 
  9. Kappes, J. H.; Speth, M.; Andres, B.; Reinelt, G. and Schnörr, C.
    Globally Optimal Image Partitioning by Multicuts.
    In EMMCVPR, pages 31-44, Springer, 2011. pdf 
  10. Kaster, F.; Menze, B. H.; Weber, M.-A. and Hamprecht, F. A.
    Comparative Validation of Graphical Models for Learning Tumor Segmentations from Noisy Manual Annotations.
    In MICCAI 2010 Workshop MCV, pages 74-85, Springer, Heidelberg, LNCS LNCS 6533, 2011. (1) 
  11. Lellmann, J.; Lenzen, F. and Schnörr, C.
    Optimality Bounds for a Variational Relaxation of the Image Partitioning Problem.
    In EMMCVPR, pages 132-146, Springer, LNCS 6819, 2011. pdf 
  12. Lenkoski, A. and Dobra, A.
    Computational aspects related to inference in Gaussian graphical models with the G-Wishart prior.
    In Journal of Computational and Graphical Statistics, 20: 140-157, 2011.
  13. Lenzen, F.; Schäfer, H. and Garbe, C.
    Denoising time-of-flight data with adaptive total variation.
    In Advances in Visual Computing: 337-346, 2011.
  14. Lou, X. and Hamprecht, F. A.
    Structured Learning for Cell Tracking.
    In NIPS 2011. Proceedings, pages 1296-1304, 2011. (1) 
  15. Lou, X.; Kaster, F.; Lindner, M.; Kausler, B.; Köthe, U.; Höckendorf, B.; Wittbrodt, J.; Jänicke, H. and Hamprecht, F. A.
    DELTR: Digital Embryo Lineage Tree Reconstructor.
    In Eighth IEEE International Symposium on Biomedical Imaging (ISBI). Proceedings, pages 1557-1560, 2011. pdf  (1) 
  16. Monroy, A.; Eigenstetter, A. and Ommer, B.
    Beyond Straight Lines - Object Detection using Curvature.
    In Proceedings of the IEEE International Conference on Image Processing (ICIP), pages 3622-3625, IEEE, 2011.
  17. Obradovic, R.; Janev, M.; Antic, B.; Crnojevic, V. and Petrovic, N. I.
    Robust Sparse Image Denoising.
    In Proceedings of the International Conference on Image Processing (ICIP), 2011. pdf 
  18. Röder, J.; Tolosana-Delgado, R. and Hamprecht, F. A.
    Gaussian process classification: singly versus doubly stochastic models, and new computational schemes.
    In Stochastic Environmental Research & Risk Assessment, 25 (7): 865-879, 2011. (1) 
  19. Rathke, F.; Schmidt, S. and Schnörr, C.
    Order Preserving and Shape Prior Constrained Intra-Retinal Layer Segmentation in Optical Coherence Tomography.
    In MICCAI, Springer, 2011. pdf 
  20. Rodriguez, A.; Lenkoski, A. and Dobra, A.
    Sparse covariance estimation in heterogeneous samples.
    In Electronic Journal of Statistics, 5: 981-1014, 2011.
  21. 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 
  22. Scheuerer, M.
    An alternative procedure for selecting a good value for the parameter c in RBF-interpolation.
    In Adv. Comput. Math., 34 (1): 105-126, 2011.
  23. Schlecht, J. and Ommer, B.
    Contour-based Object Detection.
    In Proceedings of the British Machine Vision Conference (BMVC), 2011.
  24. Schmidt, S.; Savchynskyy, B.; Kappes, J. H. and Schnörr, C.
    Evaluation of a First-Order Primal-Dual Algorithm for MRF Energy Minimization.
    In EMMCVPR, Springer, 2011. pdf 

2010

  1. Andres, B.; 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 Proceedings of the 32nd DAGM Symposium on Pattern Recognition, Darmstadt, Germany, pages 353-362, LNCS , 2010. pdf  (1) 
  2. Bergtholdt, M.; Kappes, J. H.; Schmidt, S. and Schnörr, C.
    A Study of Parts-Based Object Class Detection Using Complete Graphs.
    In International Journal of Computer Vision, 87: 93-117, 2010. pdf 
  3. Gesemann, S.; Schanz, D.; Schröder, A.; Petra, S. and Schnörr, C.
    Recasting Tomo-PIV Reconstruction as Constrained and L1-Regularized Non-Linear Least Squares Problem.
    In 15th International Symposium on Application Laser Techniques to Fluid Mechanics, pages 1-12, Lisbon, 2010.
  4. 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, LNCS 6313, 2010. pdf 
  5. Scheuerer, M.
    Regularity of the sample paths of a general second order random field.
    In Stoch. Proc. Appl., 120: 1879-1897, 2010.
  6. Yarlagadda, P.; Monroy, A. and Ommer, B.
    Voting by Grouping Dependent Parts.
    In Proceedings of the European Conference on Computer Vision (ECCV), pages 197-210, Springer, 2010.