Conference Paper
O. Hosseini Jafari, Groth, O., Kirillov, A., Yang, M. Ying, and Rother, C.,
“Analyzing modular CNN architectures for joint depth prediction and semantic segmentation”, in
Proceedings - IEEE International Conference on Robotics and Automation, 2017, pp. 4620–4627.
S. Karthik Mustikovela, Yang, M. Ying, and Rother, C.,
“Can ground truth label propagation from video help semantic segmentation?”, in
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2016, vol. 9915 LNCS, pp. 804–820.
F. Kluger, Brachmann, E., Ackermann, H., Rother, C., Yang, M. Ying, and Rosenhahn, B.,
“CONSAC: Robust Multi-Model Fitting by Conditional Sample Consensus”, in
CVPR 2020, 2020.
PDF (9.95 MB) 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.
Technical Report (290.72 KB) 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.
Technical Report (1.75 MB) 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. Yuan, Schnörr, C., Kohlberger, T., and Ruhnau, P.,
“Convex Set-Based Estimation of Image Flows”, in
ICPR 2004 – 17th Int. Conf. on Pattern Recognition, Cambridge, UK, 2004, vol. 1, pp. 124-127.
S. Nowozin, Rother, C., Bagon, S., Sharp, T., Yao, B., and Kohli, P.,
“Decision tree fields”, in
Proceedings of the IEEE International Conference on Computer Vision, 2011, pp. 1668–1675.
J. Yuan, Ruhnau, P., Mémin, E., and Schnörr, C.,
“Discrete Orthogonal Decomposition and Variational Fluid Flow Estimation”, in
Scale-Space 2005, 2005, vol. 3459, pp. 267–278.
P. Yarlagadda and Ommer, B.,
“From Meaningful Contours to Discriminative Object Shape”, in
Proceedings of the European Conference on Computer Vision, 2012, vol. 7572, p. 766--779.
Technical Report (4.58 MB) A. Krull, Brachmann, E., Michel, F., Yang, M. Ying, Gumhold, S., and Rother, C.,
“Learning analysis-by-synthesis for 6d pose estimation in RGB-D images”, in
Proceedings of the IEEE International Conference on Computer Vision, 2015, vol. 2015 Inter, pp. 954–962.
D. L. Richmond, Kainmueller, D., Yang, M. Y., Myers, E. W., and Rother, C.,
“Mapping auto-context decision forests to deep convnets for semantic segmentation”, in
British Machine Vision Conference 2016, BMVC 2016, 2016, vol. 2016-Septe, pp. 144.1–144.12.
D. L. Richmond, Kainmueller, D., Yang, M. Y., Myers, E. W., and Rother, C.,
“Mapping auto-context decision forests to deep convnets for semantic segmentation”, in
British Machine Vision Conference 2016, BMVC 2016, 2016, vol. 2016-Septe, pp. 144.1–144.12.
D. L. Richmond, Kainmueller, D., Yang, M. Y., Myers, E. W., and Rother, C.,
“Mapping auto-context decision forests to deep convnets for semantic segmentation”, in
British Machine Vision Conference 2016, BMVC 2016, 2016, vol. 2016-Septe, pp. 144.1–144.12.
J. Yarkony, Beier, T., Baldi, P., and Hamprecht, F. A.,
“Parallel Multicut Segmentation via Dual Decomposition”, in
New Frontiers in Mining Complex Patterns - Third International Workshop, {NFMCP} 2014, Held in Conjunction with {ECML-PKDD} 2014, Nancy, France, September 19, 2014, Revised Selected Papers, 2014.
F. Michel, Krull, A., Brachmann, E., Yang, M. Ying, Gumhold, S., and Rother, C.,
“Pose Estimation of Kinematic Chain Instances via Object Coordinate Regression”, 2015, pp. 181.1–181.11.
O. Hosseini Jafari and Yang, M. Ying,
“Real-time RGB-D based template matching pedestrian detection”, in
Proceedings - IEEE International Conference on Robotics and Automation, 2016, vol. 2016-June, pp. 5520–5527.
P. Yarlagadda, Monroy, A., Carque, B., and Ommer, B.,
“Recognition and Analysis of Objects in Medieval Images”, in
Proceedins of the Aian Conference on Computer Vision, Workshop on e-Heritage, 2010, p. 296--305.
Technical Report (2.76 MB) 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.
P. Yarlagadda, Monroy, A., Carque, B., and Ommer, B.,
“Top-down Analysis of Low-level Object Relatedness Leading to Semantic Understanding of Medieval Image Collections”, in
Conference on Computer Vision and Image Analysis of Art II, 2011, vol. 7869, p. 61--69.
Technical Report (11.06 MB) 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.
Technical Report (478.04 KB) P. Yarlagadda, Monroy, A., Carque, B., and Ommer, B.,
“Towards a Computer-based Understanding of Medieval Images”, in
Scientific Computing & Cultural Heritage, 2009, p. 89--97.
L. Maier-Hein, Franz, A. M., Fangerau, M., Schmidt, M., Seitel, A., Mersmann, S., Kilgus, T., Groch, A., Yung, K., Santos, T. R. dos, and Meinzer, H. - P.,
“Towards mobile augmented reality for on-patient visualization of medical images”, in
Bildverarbeitung für die Medizin (2011), 2011, p. 389--393.
E. Brachmann, Michel, F., Krull, A., Yang, M. Ying, Gumhold, S., and Rother, C.,
“Uncertainty-Driven 6D Pose Estimation of Objects and Scenes from a Single RGB Image”, in
Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016, vol. 2016-Decem, pp. 3364–3372.
E. Brachmann, Michel, F., Krull, A., Yang, M. Ying, Gumhold, S., and Rother, C.,
“Uncertainty-Driven 6D Pose Estimation of Objects and Scenes from a Single RGB Image”, in
Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016, vol. 2016-Decem, pp. 3364–3372.
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.,
“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.
Technical Report (1.82 MB) 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 Window
Adaption”, in
14th International Symposium on Applications of Laser Techniques
to Fluid Mechanics, 2008, p. 1.1.8.
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