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

J.  Berger and Schnörr, C., ?Joint Recursive Monocular Filtering of Camera Motion and Disparity Map?, in 38th German Conference on Pattern Recognition, Hannover, 2016.

J.  Berger and Schnörr, C., ?Joint Recursive Monocular Filtering of Camera Motion and Disparity Map?, in 38th German Conference on Pattern Recognition, 2016.

J. Hendrik Kappes, Swoboda, P., Savchynskyy, B., Hazan, T., and Schnörr, C., ?Multicuts and Perturb & MAP for Probabilistic Graph Clustering?, Journal of Mathematical Imaging and Vision, vol. 56, pp. 221?237, 2016.

M.  Zisler, Kappes, J. H., Schnörr, C., Petra, S., and Schnörr, C., ?Non-Binary Discrete Tomography by Continuous Non-Convex Optimization?, IEEE Comp. Imaging, vol. 2, pp. 335-347, 2016.

P.  Swoboda, Shekhovtsov, A., Kappes, J. Hendrik, Schnörr, C., and Savchynskyy, B., ?Partial Optimality by Pruning for MAP-Inference with General Graphical Models?, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 38, pp. 1370?1382, 2016.

E.  Bodnariuc, Schiffner, M. F., Petra, S., and Schnörr, C., ?Plane Wave Acoustic Superposition for Fast Ultrasound Imaging?, International Ultrasonics Symposium. 2016.

A.  Biesdorf, Wörz, S., von Tengg-Kobligk, H., Rohr, K., and Schnörr, C., ?3D Segmentation of Vessels by Incremental Implicit Polynomial Fitting and Convex Optimization?, in Proc.~ISBI, 2015.

E.  Bodnariuc, Gurung, A., Petra, S., and Schnörr, C., ?Adaptive Dictionary-Based Spatio-temporal Flow Estimation for Echo PIV?, in Proc.~EMMCVPR, 2015, vol. 8932, p. 378--391.

E.  Bodnariuc, Gurung, A., Petra, S., and Schnörr, C., ?Adaptive Dictionary-Based Spatio-temporal Flow Estimation for Echo PIV?, in EMMCVPR, 2015.

N.  Gianniotis, Schnörr, C., Molkenthin, C., and Bora, S. S., ?Approximate variational inference based on a finite sample of Gaussian latent variables?, Patt.~Anal.~Appl., 2015.

J. H.  Kappes, 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?, International Journal of Computer Vision, pp. 1-30, 2015.

J. H.  Kappes, 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?, Int.~J.~Comp.~Vision, 2015.

J. H.  Kappes, 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?, International Journal of Computer Vision, vol. 115, pp. 155?184, 2015.

J. H.  Kappes, 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?, International Journal of Computer Vision, vol. 115, pp. 155?184, 2015.

J. H.  Kappes, 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?, International Journal of Computer Vision, vol. 115, pp. 155?184, 2015.

F.  Rathke and Schnörr, C., ?A Computational Approach to Log-Concave Density Estimation?, An. St. Univ. Ovidius Constanta, vol. 23, pp. 151-166, 2015.

F.  Rathke and Schnörr, C., ?A Computational Approach to Log-Concave Density Estimation?, An. St. Univ. Ovidius Constanta, vol. 23, pp. 151-166, 2015.

F.  Silvestri, Reinelt, G., and Schnörr, C., ?A Convex Relaxation Approach to the Affine Subspace Clustering Problem?, in Proc.~GCPR, 2015.

A.  Neufeld, 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.

B.  Schmitzer and Schnörr, C., ?Globally Optimal Joint Image Segmentation and Shape Matching based on Wasserstein Modes?, J.~Math.~Imag.~Vision, vol. 52, p. 436--458, 2015.

J. H.  Kappes, Swoboda, P., Savchynskyy, B., Hazan, T., and Schnörr, C., ?Probabilistic Correlation Clustering and Image Partitioning Using Perturbed Multicuts?, in Proc.~SSVM, 2015.

J. Hendrik Kappes, Swoboda, P., Savchynskyy, B., Hazan, T., and Schnörr, C., ?Probabilistic correlation clustering and image partitioning using perturbed Multicuts?, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2015, vol. 9087, pp. 231?242.

J.  Berger, 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.

J.  Berger, 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.

J.  Berger, 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.

J.  Berger, 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.

J. H.  Kappes, Petra, S., Schnörr, C., and Zisler, M., ?TomoGC: Binary Tomography by Constrained Graph Cuts?, in Proc.~GCPR, 2015.

S.  Petra and Schnörr, C., ?Average Case Recovery Analysis of Tomographic Compressive Sensing?, Linear Algebra and its Applications, vol. 441, pp. 168-198, 2014.

J. H.  Kappes, 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 StructuredDiscrete Energy Minimization Problems?, CoRR, 2014.

J. H.  Kappes, 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?, CoRR, vol. abs/1404.0533, 2014.

A.  Denitiu, Petra, S., Schnörr, C., and Schnörr, C., ?An Entropic Perturbation Approach to TV-Minimization for Limited-Data Tomography?, in Discrete Geometry for Computer Imagery (DGCI) 2014, 2014, p. 262--274.

A.  Denitiu, Petra, S., Schnörr, C., and Schnörr, C., ?An Entropic Perturbation Approach to TV-Minimization for Limited-Data Tomography?, in Discrete Geometry for Computer Imagery (DGCI) 2014, 2014, pp. 262?274.

B.  Schmitzer and Schnörr, C., ?Globally Optimal Joint Image Segmentation and Shape Matching based on Wasserstein Modes?. 2014.

B.  Schmitzer and Schnörr, C., ?Globally Optimal Joint Image Segmentation and Shape Matching based on Wasserstein Modes?. 2014.

J. H.  Kappes, Beier, T., and Schnörr, C., ?MAP-Inference on Large Scale Higher-Order Discrete Graphical Models by Fusion Moves?, in Computer Vision - {ECCV} 2014 Workshops - Zurich, Switzerland, September 6-7 and 12, 2014, Proceedings, Part {II}, 2014.

J. Hendrik Kappes, 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.

F.  Becker, Petra, S., and Schnörr, C., ?Optical Flow?, Handbook of Mathematical Methods in Imaging. Springer, 2014.

P.  Swoboda, Savchynskyy, B., Kappes, J. H., and Schnörr, C., ?Partial Optimality by Pruning for MAP-inference with General GraphicalModels?, CVPR. Proceedings. pp. 1170-1177, 2014.

P.  Swoboda, 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.

P.  Swoboda, 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.

A.  Denitiu, Petra, S., Schnörr, C., and Schnörr, C., ?Phase Transitions and Cosparse Tomographic Recovery of Compound Solid Bodies from Few Projections?, Fundamenta Informaticae, vol. 135, p. 73--102, 2014.

A.  Denitiu, Petra, S., Schnörr, C., and Schnörr, C., ?Phase Transitions and Cosparse Tomographic Recovery of Compound Solid Bodies from Few Projections?, Fundamenta Informaticae, vol. 135, pp. 73?102, 2014.

F.  Rathke, Schmidt, S., and Schnörr, C., ?Probabilistic Intra-Retinal Layer Segmentation in 3-D OCT Images Using Global Shape Regularization?, Med. Image Anal., vol. 18, pp. 781?794, 2014.

F.  Rathke, Schmidt, S., and Schnörr, C., ?Probabilistic Intra-Retinal Layer Segmentation in 3-D OCT Images Using Global Shape Regularization?, Medical Image Analysis, vol. 18, pp. 781-794, 2014.

F.  Lenzen, Lellmann, J., Becker, F., and Schnörr, C., ?Solving Quasi-Variational Inequalities for Image Restoration with Adaptive Constraint Sets?, SIAM J.~Imag.~Sci., vol. 7, p. 2139--2174, 2014.

F.  Lenzen, Lellmann, J., Becker, F., and Schnörr, C., ?Solving QVIs for Image Restoration with Adaptive Constraint Sets?, SIAM Journal on Imaging Sciences (SIIMS), in press, 2014.

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

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

F.  Lenzen, Becker, F., Lellmann, J., Petra, S., and Schnörr, C., ?A Class of Quasi-Variational Inequalities for Adaptive Image Denoisingand Decomposition?, Computational Optimization and Applications (COAP), vol. 54 (2), pp. 371-398, 2013.

F.  Lenzen, Becker, F., Lellmann, J., Petra, S., and Schnörr, C., ?A class of quasi-variational inequalities for adaptive image denoising and decomposition?, Computational Optimization and Applications, vol. 54, pp. 371-398, 2013.

D.  Breitenreicher, Lellmann, J., and Schnörr, C., ?COAL: a generic modelling and prototyping framework for convex optimization problems of variational image analysis?, Optimization Methods and Software, vol. 28, pp. 1081-1094, 2013.

J. H.  Kappes, Andres, B., Hamprecht, F. A., Schnörr, C., Nowozin, S., Batra, D., Sungwoong, K., Kausler, B. X., Lellmann, J., Komodakis, N., and Rother, C., ?A Comparative Study of Modern Inference Techniques for Discrete Energy Minimization Problems?, in CVPR 2013. Proceedings, 2013.

J. H.  Kappes, Andres, B., Hamprecht, F. A., Schnörr, C., Nowozin, S., Batra, D., Kim, S., Kausler, B. X., Lellmann, J., Komodakis, N., and Rother, C., ?A Comparative Study of Modern Inference Techniques for Discrete Energy Minimization Problem?, in CVPR, 2013.

B.  Schmitzer and Schnörr, C., ?Contour Manifolds and Optimal Transport?. 2013.

P.  Swoboda and Schnörr, C., ?Convex Variational Image Restoration with Histogram Priors?, SIAM J.~Imag.~Sci., vol. 6, pp. 1719-1735, 2013.

S.  Petra, Schnörr, C., and Schröder, A., ?Critical Parameter Values and Reconstruction Propertiesof Discrete Tomography: Application to Experimental FluidDynamics?, Fundamenta Informaticae, vol. 125, p. 285--312, 2013.

J.  Lellmann, Lellmann, B., Widmann, F., and Schnörr, C., ?Discrete and Continuous Models for Partitioning Problems?, Int.~J.~Comp.~Visionz, vol. 104, pp. 241-269, 2013.

B.  Savchynskyy, Kappes, J. H., Swoboda, P., and Schnörr, C., ?Global MAP-Optimality by Shrinking the Combinatorial Search Areawith Convex Relaxation?, in NIPS. Proceedings, 2013, pp. 1950-1958.

B.  Savchynskyy, Kappes, J. H., Swoboda, P., and Schnörr, C., ?Global MAP-Optimality by Shrinking the Combinatorial Search Area with Convex Relaxation?, in NIPS, 2013.

B.  Savchynskyy, Kappes, J. Hendrik, Swoboda, P., and Schnörr, C., ?Global MAP-Optimality by Shrinking the Combinatorial Search Area with Convex Relaxation?, in NIPS, 2013.

B.  Schmitzer and Schnörr, C., ?A Hierarchical Approach to Optimal Transport?, in Scale Space and Variational Methods (SSVM 2013), 2013, pp. 452-464.

J. H.  Kappes, Speth, M., Reinelt, G., and Schnörr, C., ?Higher-order Segmentation via Multicuts?, ArXiv e-prints. 2013.

J. Hendrik Kappes, Speth, M., Reinelt, G., and Schnörr, C., ?Higher-order Segmentation via Multicuts?, ArXiv e-prints. 2013.

B.  Schmitzer and Schnörr, C., ?Modelling convex shape priors and matching based on the Gromov-Wasserstein distance?, Journal of Mathematical Imaging and Vision, vol. 46, pp. 143-159, 2013.

B.  Schmitzer and Schnörr, C., ?Modelling convex shape priors and matching based on the Gromov-Wasserstein distance?, Journal of Mathematical Imaging and Vision, vol. 46, pp. 143-159, 2013.

B.  Schmitzer and Schnörr, C., ?Object Segmentation by Shape Matching with Wasserstein Modes?, in Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR 2013), 2013, pp. 123-136.

J.  Lellmann, Lenzen, F., and Schnörr, C., ?Optimality Bounds for a Variational Relaxation of the Image PartitioningProblem?, Journal of Mathematical Imaging and Vision, vol. 47 (3), pp. 239-257, 2013.

P.  Swoboda, Savchynskyy, B., Kappes, J. H., and Schnörr, C., ?Partial Optimality via Iterative Pruning for the Potts Model?, in Proceedings of the 4th International Conference on Scale Space andVariational Methods in Computer Vision SSVM, 2013, pp. 477-488.

P.  Swoboda, 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.

P.  Swoboda, 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.

P.  Swoboda, Savchynskyy, B., Kappes, J. H., and Schnörr, C., ?Persistency by Pruning for General Graphical Models?, in submitted to NIPS 2013., 2013.

J. H.  Kappes, 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.

J. H.  Kappes, 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.

P.  Swoboda and Schnörr, C., ?Variational Image Segmentation and Cosegmentation with the Wasserstein Distance?, in Energy Minimization Methods in Computer Vision and Pattern Recognition, 2013, vol. 8081, p. 321--334.

P.  Swoboda and Schnörr, C., ?Variational Image Segmentation and Cosegmentation with the Wasserstein Distance?, in Energy Minimization Methods in Computer Vision and Pattern Recognition, 2013, vol. 8081, pp. 321?334.

F.  Becker, Lenzen, F., Kappes, J. H., and Schnörr, C., ?Variational Recursive Joint Estimation of Dense Scene Structure andCamera Motion from Monocular High Speed Traffic Sequences?, International Journal of Computer Vision, vol. 105 (3), pp. 269-297, 2013.

F.  Becker, 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?, International Journal of Computer Vision, vol. 105, no. 3, p. 269--297, 2013.

F.  Becker, 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?, International Journal of Computer Vision, vol. 105, pp. 269?297, 2013.

J. H.  Kappes, Savchynskyy, B., and Schnörr, C., ?A Bundle Approach To Efficient MAP-Inference by Lagrangian Relaxation?, in CVPR. Proceedings, 2012, pp. 1688-1695.

J. H.  Kappes, Savchynskyy, B., and Schnörr, C., ?A Bundle Approach To Efficient MAP-Inference by Lagrangian Relaxation?, in CVPR, 2012.

B.  Savchynskyy, Schmidt, S., Kappes, J. H., and Schnörr, C., ?Efficient MRF Energy Minimization via Adaptive Diminishing Smoothing?, UAI. Proceedings, pp. 746-755, 2012.

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

J.  Lellmann, Lenzen, F., and Schnörr, C., ?Optimality Bounds for a Variational Relaxation of the Image Partitioning Problem?, Journal of Mathematical Imaging and Vision, vol. 47, pp. 239-257, 2012.

J.  Lellmann, Lenzen, F., and Schnörr, C., ?Optimality Bounds for a Variational Relaxation of the Image Partitioning Problem?, Journal of Mathematical Imaging and Vision, vol. 47, pp. 239-257, 2012.

F.  Becker, Wieneke, B., Petra, S., Schröder, A., and Schnörr, C., ?Variational Adaptive Correlation Method for Flow Estimation?, IEEE Transactions on Image Processing, vol. 21, p. 3053 -- 3065, 2012.

F.  Becker, Wieneke, B., Petra, S., Schröder, A., and Schnörr, C., ?Variational Adaptive Correlation Method for Flow Estimation?, IEEE Transactions on Image Processing, vol. 21, pp. 3053 ? 3065, 2012.

F.  Lenzen, Becker, F., Lellmann, J., Petra, S., and Schnörr, C., ?Variational Image Denoising with Adaptive Constraint Sets?, in LNCS, 2012, pp. 206-217.

F.  Lenzen, 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, 2012, pp. 206-217.

B.  Schmitzer and Schnörr, C., ?Weakly Convex Coupling Continuous Cuts and Shape Priors?, in Scale Space and Variational Methods (SSVM 2011), 2012, pp. 423-434.

J.  Lellmann and Schnörr, C., ?Continuous Multiclass Labeling Approaches and Algorithms?, SIAM J.~Imag.~Sci., vol. 4, pp. 1049-1096, 2011.

S.  Schmidt, Savchynskyy, B., Kappes, J. H., and Schnörr, C., ?Evaluation of a First-Order Primal-Dual Algorithm for MRF EnergyMinimization?, in EMMCVPR 2011, 2011, vol. 6819, pp. 89-103.

S.  Schmidt, Savchynskyy, B., Kappes, J. H., and Schnörr, C., ?Evaluation of a First-Order Primal-Dual Algorithm for MRF Energy Minimization?, in EMMCVPR, 2011, vol. 6819, pp. 89-103.

S.  Schmidt, Savchynskyy, B., Kappes, J. Hendrik, and Schnörr, C., ?Evaluation of a First-Order Primal-Dual Algorithm for MRF Energy Minimization?, in EMMCVPR, 2011, vol. 6819, pp. 89-103.

A.  Nicola, Petra, S., Popa, C., and Schnörr, C., ?A general extending and constraining procedure for linear iterative methods?, Int.~J.~Comp.~Math., 2011.

J. H.  Kappes, Speth, M., Andres, B., Reinelt, G., and Schnörr, C., ?Globally Optimal Image Partitioning by Multicuts?, in EMMCVPR, 2011, pp. 31-44.

J. H.  Kappes, Speth, M., Andres, B., Reinelt, G., and Schnörr, C., ?Globally Optimal Image Partitioning by Multicuts?, in EMMCVPR, 2011.

J. Hendrik Kappes, Speth, M., Andres, B., Reinelt, G., and Schnörr, C., ?Globally Optimal Image Partitioning by Multicuts?, in EMMCVPR, 2011.

D.  Breitenreicher and Schnörr, C., ?Model-Based Multiple Rigid Object Detection and Registration in Unstructured Range Data?, Int.~J.~Comp.~Vision, vol. 92, p. 32--52, 2011.

D.  Breitenreicher and Schnörr, C., ?Model-Based Multiple Rigid Object Detection and Registration in Unstructured Range Data?, Int. J. Comp. Vision, vol. 92, pp. 32?52, 2011.

J.  Lellmann, Lenzen, F., and Schnörr, C., ?Optimality Bounds for a Variational Relaxation of the Image PartitioningProblem?, in Energy Min. Meth. Comp. Vis. Patt. Recogn., 2011, pp. 132-146.

J.  Lellmann, Lenzen, F., and Schnörr, C., ?Optimality Bounds for a Variational Relaxation of the Image Partitioning Problem?, in Energy Min. Meth. Comp. Vis. Patt. Recogn., 2011, vol. 6819, p. 132--146.

J.  Lellmann, Lenzen, F., and Schnörr, C., ?Optimality Bounds for a Variational Relaxation of the Image Partitioning Problem?, in Energy Min. Meth. Comp. Vis. Patt. Recogn., 2011, vol. 6819, pp. 132?146.

J.  Lellmann, Lenzen, F., and Schnörr, C., ?Optimality Bounds for a Variational Relaxation of the Image Partitioning Problem?, IPA group, Heidelberg University, 2011.

F.  Rathke, Schmidt, S., and Schnörr, C., ?Order Preserving and Shape Prior Constrained Intra-Retinal LayerSegmentation in Optical Coherence Tomography?, in MICCAI 2011, Proceedings, 2011, vol. 6893, pp. 370-377.

F.  Rathke, Schmidt, S., and Schnörr, C., ?Order Preserving and Shape Prior Constrained Intra-Retinal Layer Segmentation in Optical Coherence Tomography?, in MICCAI, 2011, vol. 6893, p. 370--377.

F.  Rathke, Schmidt, S., and Schnörr, C., ?Order preserving and shape prior constrained intra-retinal layer segmentation in optical coherence tomography?, Medical Image Computing and Computer-Assisted Intervention (MICCAI 2011), vol. 6893. Springer, pp. 370?377, 2011.

F.  Rathke, Schmidt, S., and Schnörr, C., ?Order Preserving and Shape Prior Constrained Intra-Retinal Layer Segmentation in Optical Coherence Tomography?, in MICCAI, 2011, vol. 6893, pp. 370?377.

D.  Breitenreicher, Lellmann, J., and Schnörr, C., ?Sparse Template-Based Variational Image Segmentation?, Advances in Adaptive Data Analysis, vol. 3, pp. 149-166, 2011.

D.  Breitenreicher, Lellmann, J., and Schnörr, C., ?Sparse Template-Based Variational Image Segmentation?, Advances in Adaptive Data Analysis, vol. 3, pp. 149-166, 2011.

B.  Savchynskyy, Kappes, J. H., Schmidt, S., and Schnörr, C., ?A Study of Nesterov's Scheme for Lagrangian Decomposition and MAPLabeling?, IEEE International Conference on Computer Vision and Pattern Recognition(CVPR), accepted as oral presentation, pp. 1817 - 1823, 2011.

B.  Savchynskyy, 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.

F.  Becker, Wieneke, B., Petra, S., Schröder, A., and Schnörr, C., ?Variational Adaptive Correlation Method for Flow Estimation?, IEEE Transactions on Image Processing, vol. 21, 6, pp. 3053 - 3065, 2011.

F.  Lenzen, Becker, F., Lellmann, J., Petra, S., and Schnörr, C., ?Variational Image Denoising with Adaptive Constraint Sets?, in Proceedings of the 3nd International Conference on Scale Space andVariational Methods in Computer Vision 2011, in press, 2011, vol. 6667, pp. 206-217.

F.  Becker, Lenzen, F., Kappes, J. H., and Schnörr, C., ?Variational Recursive Joint Estimation of Dense Scene Structure andCamera Motion from Monocular High Speed Traffic Sequences?, in 2011 IEEE International Conference on Computer Vision ICCV, 2011, pp. 1692-1699.

F.  Becker, 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), 2011, p. 1692 -- 1699.

F.  Becker, 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), 2011, pp. 1692 ? 1699.

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

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

J.  Lellmann, Breitenreicher, D., and Schnörr, C., ?Fast and Exact Primal-Dual Iterations for Variational Problems in Computer Vision?, in European Conference on Computer Vision (ECCV), 2010, vol. 6312, p. 494--505.

J. H.  Kappes, Schmidt, S., and Schnörr, C., ?MRF Inference by k-Fan Decomposition and Tight Lagrangian Relaxation?, in European Conference on Computer Vision (ECCV), 2010, vol. 6313, p. 735--747.

J. H.  Kappes, Schmidt, S., and Schnörr, C., ?MRF Inference by k-Fan Decomposition and Tight Lagrangian Relaxation?, in European Conference on Computer Vision (ECCV), 2010, vol. 6313, p. 735--747.

J.  Lellmann, Lenzen, F., and Schnörr, C., ?Optimality Bounds for Variational Relaxations of Optimal PartitionProblems?, 2010.

A.  Vlasenko and Schnörr, C., ?Physically Consistent and Efficient Variational Denoising of Image Fluid Flow Estimates?, IEEE Trans.~Image Proc., vol. 19, pp. 586-595, 2010.

D.  Breitenreicher and Schnörr, C., ?Robust 3D object registration without explicit correspondence using geometric integration?, Machine Vision and Applications, vol. 21, pp. 601-611, 2010.

D.  Breitenreicher and Schnörr, C., ?Robust 3D object registration without explicit correspondence using geometric integration?, Machine Vision and Applications, vol. 21, pp. 601-611, 2010.

M.  Bergtholdt, Kappes, J. H., Schmidt, S., and Schnörr, C., ?A Study of Parts-Based Object Class Detection Using Complete Graphs?, Int.~J.~Comp.~Vision, vol. 87, pp. 93-117, 2010.

M.  Bergtholdt, Kappes, J. H., Schmidt, S., and Schnörr, C., ?A Study of Parts-Based Object Class Detection Using Complete Graphs?, Int. J. Comp. Vision, vol. 87, pp. 93-117, 2010.

D.  Heitz, Mémin, E., and Schnörr, C., ?Variational fluid flow measurements from image sequences: synopsis and perspectives?, Exp.~Fluids, vol. 48, pp. 369-393, 2010.

S.  Petra, Schröder, A., and Schnörr, C., ?3D Tomography from Few Projections in Experimental Fluid Mechanics?, Imaging Measurement Methods for Flow Analysis, vol. 106. Springer, pp. 63-72, 2009.

S.  Petra, Popa, C., and Schnörr, C., ?Accelerating Constrained SIRT with Applications in Tomographic Particle Image Reconstruction?, IWR, University of Heidelberg, 2009.

J.  Yuan, Schnörr, C., and Steidl, G., ?Convex Hodge Decomposition and Regularization of Image Flows?, J.~Math.~Imag.~Vision, vol. 33, pp. 169-177, 2009.

J.  Lellmann, Kappes, J. H., Yuan, J., Becker, F., Schnörr, C., Mórken, K., and Lysaker, M., ?Convex Multi-Class Image Labeling by Simplex-Constrained Total Variation?, in Scale Space and Variational Methods in Computer Vision (SSVM 2009), 2009, vol. 5567, pp. 150-162.

J.  Lellmann, Kappes, J. H., Yuan, J., Becker, F., and Schnörr, C., ?Convex Multi-Class Image Labeling by Simplex-Constrained Total Variation?, in Scale Space and Variational Methods in Computer Vision (SSVM 2009), 2009, vol. 5567, pp. 150-162.

J.  Lellmann, Becker, F., and Schnörr, C., ?Convex Optimization for Multi-Class Image Labeling with a Novel Familyof Total Variation Based Regularizers?, in Proceedings of the IEEE Conference on Computer Vision (ICCV 09) Kyoto, Japan, 2009, pp. 646-653.

J.  Lellmann, Becker, F., and Schnörr, C., ?Convex Optimization for Multi-Class Image Labeling with a Novel Family of Total Variation Based Regularizers?, in IEEE International Conference on Computer Vision (ICCV), 2009, p. 646 -- 653.

A.  Nicola, Petra, S., Popa, C., and Schnörr, C., ?On a general extending and constraining procedure for linear iterative methods?, IWR, University of Heidelberg, 2009.

D.  Breitenreicher 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), 2009, vol. 5681, pp. 274-287.

D.  Breitenreicher 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), 2009, vol. 5681, pp. 274-287.

F.  Lauer and Schnörr, C., ?Spectral Clustering of Linear Subspaces for Motion Segmentation?, in Proceedings of the IEEE Conference on Computer Vision (ICCV 09) Kyoto, Japan, in press, 2009, pp. 678-685.

F.  Lauer and Schnörr, C., ?Spectral Clustering of Linear Subspaces for Motion Segmentation?, in Proc.~IEEE Int.~Conf.~Computer Vision (ICCV'09), 2009.

S.  Petra and Schnörr, C., ?TomoPIV meets Compressed Sensing?, IWR, University of Heidelberg, 2009.

S.  Petra and Schnörr, C., ?TomoPIV meets Compressed Sensing?, Pure Math.~Appl., vol. 20, p. 49 -- 76, 2009.

J.  Yuan, Schnörr, C., and Steidl, G., ?Total-Variation Based Piecewise Affine Regularization?, in Scale Space and Variational Methods in Computer Vision (SSVM 2009), 2009, vol. 5567, pp. 552-564.

J.  Yuan, Schnörr, C., and Steidl, G., ?Total-Variation Based Piecewise Affine Regularization?, in Scale Space and Variational Methods in Computer Vision (SSVM 2009), 2009, vol. 5567, pp. 552-564.

A.  Vlasenko and Schnörr, C., ?Variational Approaches for Model-Based PIV and Visual Fluid Analysis?, Imaging Measurement Methods for Flow Analysis, vol. 106. Springer, pp. 247-256, 2009.

K.  Fundana, Heyden, A., Gosch, C., and Schnörr, C., ?Continuous Graph Cuts for Prior-Based Object Segmentation?, in 19th Int.~Conf.~Patt.~Recog.~(ICPR), 2008, p. 1--4.

J.  Yuan, Steidl, G., and Schnörr, C., ?Convex Hodge Decomposition of Image Flows?, in Pattern Recognition -- 30th DAGM Symposium, 2008, vol. 5096, p. 416--425.

J.  Lellmann, Kappes, J. H., Yuan, J., Becker, F., and Schnörr, C., ?Convex Multi-Class Image Labeling by Simplex-Constrained Total Variation?, IWR, University of Heidelberg, 2008.

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

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

S.  Petra, Popa, C., and Schnörr, C., ?Extended and Constrained Cimmino-type Algorithms with Applications in Tomographic Image Reconstruction?, IWR, University of Heidelberg, 2008.

S.  Petra, Popa, C., and Schnörr, C., ?Extended and Constrained Cimmino-type Algorithms with Applications in Tomographic Image Reconstruction?, IWR, University of Heidelberg, 2008.

J. H.  Kappes and Schnörr, C., ?MAP-Inference for Highly-Connected Graphs with DC-Programming?, in Pattern Recognition -- 30th DAGM Symposium, 2008, vol. 5096, p. 1--10.

A.  Vlasenko and Schnörr, C., ?Physically Consistent Variational Denoising of Image Fluid Flow Estimates?, in Pattern Recognition -- 30th DAGM Symposium, 2008, vol. 5096, p. 406--415.

S.  Petra, Schröder, A., Wieneke, B., and Schnörr, C., ?On Sparsity Maximization in Tomographic Particle Image Reconstruction?, in Pattern Recognition -- 30th DAGM Symposium, 2008, vol. 5096, p. 294--303.

F.  Becker, Wieneke, B., Yuan, J., and Schnörr, C., ?A Variational Approach to Adaptive Correlation for Motion Estimationin Particle Image Velocimetry"?, in Pattern Recognition -- 30th DAGM Symposium, 2008, vol. 5096, pp. 335-344.

F.  Becker, 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, 2008, vol. 5096, p. 335--344.

F.  Becker, 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, 2008, vol. 5096, pp. 335?344.

F.  Becker, Wieneke, B., Yuan, J., and Schnörr, C., ?Variational Correlation Approach to Flow Measurement with WindowAdaption?, in 14th International Symposium on Applications of Laser Techniquesto Fluid Mechanics, 2008, p. 1.1.8.

F.  Becker, 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, 2008, p. 1.1.3.

F.  Becker, 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, 2008, p. 1.1.3.

C.  Gosch, Fundana, K., Heyden, A., and Schnörr, C., ?View Point Tracking of Rigid Object Based on Shape Sub-Manifolds?, in Computer Vision -- ECCV 2008, 2008, vol. 5302, p. 251--263.

C.  Gosch, Fundana, K., Heyden, A., and Schnörr, C., ?View Point Tracking of Rigid Object Based on Shape Sub-Manifolds?, in Computer Vision ? ECCV 2008, 2008, vol. 5302, pp. 251?263.

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

J.  Yuan, Schnörr, C., and Mémin, E., ?Discrete Orthogonal Decomposition and Variational Fluid Flow Estimation?, J.~Math.~Imag.~Vision, vol. 28, pp. 67-80, 2007.

C.  Schellewald, Roth, S., and Schnörr, C., ?Evaluation of a convex relaxation to a quadratic assignment matching approach for relational object views?, Image Vision Comp., vol. 25, p. 1301--1314, 2007.

C. S.  Garbe, Schnörr, C., and Jähne, B., ?Fluid flow estimation through integration of physical flow configurations?, in Proceedings of the 29th DAGM Symposium on Pattern Recognition, 2007, p. 92--101.

R.  Karim, 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, 2007, vol. 4713, pp. 395-404.

J.  Gall, Potthoff, J., Schnörr, C., Rosenhahn, B., and Seidel, H. - P., ?Interacting and Annealing Particle Filters: Mathematics and a Recipe for Applications?, J.~Math.~Imag.~Vision, vol. 28, p. 1--18, 2007.

J.  Gall, Potthoff, J., Schnörr, C., Rosenhahn, B., and Seidel, H. - P., ?Interacting and Annealing Particle Filters: Mathematics and a Recipe for Applications?, J. Math. Imag. Vision, vol. 28, pp. 1?18, 2007.

M.  Welk, Weickert, J., Becker, F., Schnörr, C., Feddern, C., and Burgeth, B., ?Median and related local filters for tensor-valued images?, Signal Processing, vol. 87, pp. 291-308, 2007.

P.  Ruhnau and Schnörr, C., ?Optical Stokes Flow Estimation: An Imaging-Based Control Approach?, Exp.~in Fluids, vol. 42, p. 61--78, 2007.

F. A.  Hamprecht, Schnörr, C., and Jähne, B., Eds., ?Pattern Recognition ? 29th DAGM Symposium?, LCNS, vol. 4713. Springer, 2007.

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

C.  Schnörr and Jähne, B., ?Pattern Recognition, 29th DAGM Symposium, Heidelberg, Germany, September 12-14?, vol. 4713. Springer, 2007.

B.  Andres, Hamprecht, F. A., and Garbe, C. S., ?Selection of Local Optical Flow Models by Means of Residual Analysis?, in Pattern Recognition, 2007, vol. 4713, pp. 72-81.

B.  Andres, Garbe, C. S., Schnörr, C., and Jähne, B., ?Selection of local optical flow models by means of residual analysis?, in Proceedings of the 29th DAGM Symposium on Pattern Recognition, 2007, p. 72--81.

L.  Görlitz, Menze, B. H., Weber, M. - A., and Kelm, B. Michael, ?Semi-Supervised Tumor Detection in MRSI With Discriminative Random Fields?, in Pattern Recognition, 2007, vol. 4713, pp. 224-233.

C.  Schnörr, ?Signal and Image Approximation with Level-Set Constraints?, Computing, vol. 81, pp. 137-160, 2007.

J.  Yuan, Schnörr, C., and Steidl, G., ?Simultaneous Optical Flow Estimation and Decomposition?, SIAM J.~Scientific Computing, vol. 29, pp. 2283-2304, 2007.

S.  Schmidt, 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), 2007, vol. 4584, pp. 122-133.

P.  Ruhnau, Stahl, A., and Schnörr, C., ?Variational Estimation of Experimental Fluid Flows with Physics-Based Spatio-Temporal Regularization?, Measurement Science and Technology, vol. 18, pp. 755-763, 2007.

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

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

M.  Heiler and Schnörr, C., ?Controlling Sparseness in Non-negative Tensor Factorization?, in Computer Vision -- ECCV 2006, 2006, vol. 3951, pp. 56-67.

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

M.  Bergtholdt, Kappes, J. H., and Schnörr, C., ?Learning of Graphical Models and Efficient Inference for Object Class Recognition?, in Proc. DAGM 2006, 2006, vol. 375-388, pp. 375-388.

A.  Bruhn, Weickert, J., Kohlberger, T., and Schnörr, C., ?A Multigrid Platform for Real-Time Motion Computation with Discontinuity-Preserving Variational Methods?, Int.~J.~Computer Vision, vol. 70, pp. 257-277, 2006.

A.  Bruhn, Weickert, J., Kohlberger, T., and Schnörr, C., ?A Multigrid Platform for Real-Time Motion Computation with Discontinuity-Preserving Variational Methods?, Int. J. Computer Vision, vol. 70, pp. 257-277, 2006.

D.  Cremers, Sochen, N., and Schnörr, C., ?Multiphase Dynamic Labeling for Variational Recognition-Driven Image Segmentation?, ijcv, vol. 66, pp. 67-81, 2006.

P.  Ruhnau, Stahl, A., and Schnörr, C., ?On-Line Variational Estimation of Dynamical Fluid Flows with Physics-Based Spatio-Temporal Regularization?, in Proc.~DAGM 2006, 2006, vol. 375-388, pp. 375-388.

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

A.  Bruhn, Weickert, J., and Schnörr, C., ?Lucas/Kanade Meets Horn/Schunck: Combining Local and Global Optic Flow Methods?, vol. 61, pp. 211-231, 2005.

M.  Welk, Becker, F., Schnörr, C., and Weickert, J., ?Matrix-Valued Filters as Convex Programs?, in Scale-Space 2005, 2005, vol. 3459, pp. 204?216.

M.  Bergtholdt and Schnörr, C., ?Shape Priors and Online Appearance Learning for Variational Segmentation and Object Recognition in Static Scenes?, Pattern Recognition, Proc. 27th DAGM Symposium, vol. 3663. Springer, pp. 342?350, 2005.

J.  Yuan, 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, 2005, vol. 3752, pp. 1?12.

A.  Bruhn, Weickert, J., Feddern, C., Kohlberger, T., and Schnörr, C., ?Variational optic flow computation in real-time?, IEEE Trans. Image Proc., vol. 14, pp. 608?615, 2005.

P.  Ruhnau, Kohlberger, T., Nobach, H., and Schnörr, C., ?Variational Optical Flow Estimation for Particle Image Velocimetry?, Experiments in Fluids, vol. 38, p. 21--32, 2005.

M.  Bergtholdt, Cremers, D., and Schnörr, C., ?Variational Segmentation with Shape Priors?, Handbook of Mathematical Models in Computer Vision. Springer, pp. 147-160, 2005.

A.  Bruhn, Jakob, T., Fischer, M., Weickert, J., Brüning, U., and Schnörr, C., ?High performance cluster computing with 3-D nonlinear diffusion filters?, Real-Time Imaging, vol. 10, pp. 41?51, 2004.

D.  Cremers, Sochen, N., and Schnörr, C., ?Multiphase Dynamic Labeling for Variational Recognition-Driven Image Segmentation?, in Computer Vision ? ECCV 2004, 2004, vol. 3024, pp. 74-86.

A.  Bruhn, 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), 2003, vol. 2756, pp. 222-229.

D.  Cremers, Kohlberger, T., and Schnörr, C., ?Shape Statistics in Kernel Space for Variational Image Segmentation?, Pattern Recognition, vol. 36, p. 1929--1943, 2003.

D.  Cremers, Kohlberger, T., and Schnörr, C., ?Shape Statistics in Kernel Space for Variational Image Segmentation?, Pattern Recognition, vol. 36, pp. 1929?1943, 2003.

D.  Cremers and Schnörr, C., ?Statistical Shape Knowledge in Variational Motion Segmentation?, Image and Vision Comp., vol. 21, pp. 77-86, 2003.

D.  Cremers, Sochen, N., and Schnörr, C., ?Towards Recognition-Based Variational Segmentation Using Shape Priors and Dynamic Labeling?, in Scale Space Methods in Computer Vision, 2003, vol. 2695, p. 388--400.

D.  Cremers, Sochen, N., and Schnörr, C., ?Towards Recognition-Based Variational Segmentation Using Shape Priors and Dynamic Labeling?, in Scale Space Methods in Computer Vision, 2003, vol. 2695, pp. 388?400.

A.  Bruhn, Weickert, J., Feddern, C., Kohlberger, T., and Schnörr, C., ?Variational Optic Flow Computation in Real-Time?, Dept. Math. and Comp. Science, Saarland University, Germany, 89, 2003.

W.  Hinterberger, Scherzer, O., Schnörr, C., and Weickert, J., ?Analysis of Optical Flow Models in the Framework of Calculus of Variations?, Numer. Funct. Anal. Optimiz., vol. 23, pp. 69?89, 2002.

A.  Bruhn, Weickert, J., and Schnörr, C., ?Combining the Advantages of Local and Global Optic Flow Methods?, in Pattern Recognition, Proc. 24th DAGM Symposium, Zürich, Switzerland, 2002, vol. 2449, pp. 454?462.

A.  Bruhn, Jakob, T., Fischer, M., Kohlberger, T., Weickert, J., Brüning, U., and Schnörr, C., ?Designing 3?D Nonlinear Diffusion Filters for High Performance Cluster Computing?, in Pattern Recognition, Proc. 24th DAGM Symposium, Zürich, Switzerland, 2002, vol. 2449, pp. 290?297.

D.  Cremers, Tischhäuser, F., Weickert, J., and Schnörr, C., ?Diffusion Snakes: Introducing Statistical Shape Knowledge into the Mumford?Shah functional?, Int. J. Computer Vision, vol. 50, pp. 295?313, 2002.

D.  Cremers and Schnörr, C., ?Motion Competition: Variational Integration of Motion Segmentation and Shape Regularization?, in Pattern Recognition, Proc. 24th DAGM Symposium, Zürich, Switzerland, 2002, vol. 2449, pp. 472?480.

D.  Cremers, Kohlberger, T., and Schnörr, C., ?Nonlinear Shape Statistics in Mumford-Shah Based Segmentation?, in Computer Vision -- ECCV 2002), 2002, vol. 2351, p. 93--108.

D.  Cremers, Kohlberger, T., and Schnörr, C., ?Nonlinear Shape Statistics in Mumford-Shah Based Segmentation?, in Computer Vision ? ECCV 2002), 2002, vol. 2351, pp. 93?108.

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

D.  Cremers, Kohlberger, T., and Schnörr, C., ?Nonlinear Shape Statistics via Kernel Spaces?, in Mustererkennung 2001, 2001, vol. 2191, p. 269--276.

D.  Cremers, Kohlberger, T., and Schnörr, C., ?Nonlinear Shape Statistics via Kernel Spaces?, in Mustererkennung 2001, Munich, Germany, 2001, vol. 2191, pp. 269?276.

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

D.  Cremers, Schnörr, C., Weickert, J., and Schellewald, C., ?Learning Translation Invariant Shape Knowledge for Steering Diffusion-Snakes?, in 3rd Workshop on Dynamic Perception, Berlin, Germany, 2000, vol. 9, pp. 117?122.

P.  Fornland and Schnörr, C., ?Determining the Dominant Plane from Uncalibrated Stereo Vision by a Robust and Convergent Iterative Approach without Correspondence?, in Proc. Int. Conf. Comp. Vision and Patt. Recog. (CVPR'97), San Juan, Puerto Rico, 1997.

K.  Rohr and Schnörr, C., ?An Efficient Approach to the Identification of Characteristic Intensity Variations?, vol. 11, pp. 273?277, 1993.