All Publications

2009

Lauer, F and Schnörr, C (2009). Spectral Clustering of Linear Subspaces for Motion Segmentation. Proc.~IEEE Int.~Conf.~Computer Vision (ICCV'09)PDF icon Technical Report (1.12 MB)
Lellmann, J, Becker, F and Schnörr, C (2009). Convex Optimization for Multi-Class Image Labeling with a Novel Family of Total Variation Based Regularizers. IEEE International Conference on Computer Vision (ICCV). 646 -- 653PDF icon Technical Report (930.18 KB)
Lellmann, J, Kappes, J H, Yuan, J, Becker, F and Schnörr, C (2009). Convex Multi-Class Image Labeling by Simplex-Constrained Total Variation. Scale Space and Variational Methods in Computer Vision (SSVM 2009). Springer. 5567 150-162PDF icon Technical Report (1.75 MB)
Nicola, A, Petra, S, Popa, C and Schnörr, C (2009). On A General Extending And Constraining Procedure For Linear Iterative Methods. IWR, University of Heidelberg. http://www.ub.uni-heidelberg.de/archiv/9761PDF icon Technical Report (799.47 KB)
Petra, S, Popa, C and Schnörr, C (2009). Accelerating Constrained Sirt With Applications In Tomographic Particle Image Reconstruction. IWR, University of Heidelberg. http://www.ub.uni-heidelberg.de/archiv/9477PDF icon Technical Report (3.33 MB)
Petra, S and Schnörr, C (2009). Tomopiv Meets Compressed Sensing. IWR, University of Heidelberg. http://www.ub.uni-heidelberg.de/archiv/9760PDF icon Technical Report (646.75 KB)
Petra, S and Schnörr, C (2009). TomoPIV meets Compressed Sensing. Pure Math.~Appl. 20 49 -- 76. http://www.mat.unisi.it/newsito/puma/public_html/contents.phpPDF icon Technical Report (409.1 KB)
Petra, S, Schröder, A and Schnörr, C (2009). 3D Tomography from Few Projections in Experimental Fluid Mechanics. Imaging Measurement Methods for Flow Analysis. Springer. 106 63-72PDF icon Technical Report (411.51 KB)
Vlasenko, A and Schnörr, C (2009). Variational Approaches for Model-Based PIV and Visual Fluid Analysis. Imaging Measurement Methods for Flow Analysis. Springer. 106 247-256PDF icon Technical Report (3.39 MB)
Yuan, J, Schnörr, C and Steidl, G (2009). Convex Hodge Decomposition and Regularization of Image Flows. J.~Math.~Imag.~Vision. 33 169-177PDF icon Technical Report (1003.75 KB)
Yuan, J, Schnörr, C and Steidl, G (2009). Total-Variation Based Piecewise Affine Regularization. Scale Space and Variational Methods in Computer Vision (SSVM 2009). Springer. 5567 552-564PDF icon Technical Report (478.04 KB)
Hansen, K, Rathke, F, Schroeter, T, Rast, G, Fox, T, Kriegl, J M and Mika, S (2009). Bias-correction of regression models: a case study on hERG inhibition. J. Chem. Inf. Model. 49 1486–1496
Singaraju, D, Rother, C and Rhemann, C (2009). Supplementary Material For New Appearance Models For Image Matting
Singaraju, D, Rother, C and Rhemann, C (2009). New appearance models for natural image matting. 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009. 2009 IEEE 659–666
Rhemann, C, Rother, C, Wang, J, Gelautz, M, Kohli, P and Rott, P (2009). A perceptually motivated online benchmark for image matting. 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009. 2009 IEEE 1826–1833. www.alphamatting.com.
Rhemann, C, Rother, C, Wang, J, Gelautz, M, Kohli, P and Rott, P (2009). A perceptually motivated online benchmark for image matting. 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009. 2009 IEEE 1826–1833. www.alphamatting.com.
Bleyer, M, Gelautz, M, Rother, C and Rhemann, C (2009). A stereo approach that handles the matting problem via imagewarping. 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009. 2009 IEEE 501–508
Rhemann, C, Rother, C, Wang, J, Gelautz, M, Kohli, P and Rott, P (2009). A perceptually motivated online benchmark for image matting. 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009. 2009 IEEE 1826–1833
Shotton, J, Winn, J, Rother, C and Criminisi, A (2009). TextonBoost for image understanding: Multi-class object recognition and segmentation by jointly modeling texture, layout, and context. International Journal of Computer Vision. 81 2–23. http://jamie.shotton.org/work/code.html
Woodford, O J (2009). A Global Perspective on MAP Inference for Low-Level Vision Supplementary material to ICCV submission \# 1536. Optimization
Shesh, A, Criminisi, A, Rother, C and Smyth, G (2009). 3D-aware image editing for out of bounds photography. Proceedings - Graphics Interface. 47–54. http://www.flickr.com/groups/oob/
Nguyen, M Hoai, Torresani, L, De La Torre, F and Rother, C (2009). Weakly supervised discriminative localization and classification: A joint learning process. Proceedings of the IEEE International Conference on Computer Vision. 1925–1932
Nguyen, M Hoai, Torresani, L, De La Torre, F and Rother, C (2009). Weakly supervised discriminative localization and classification: A joint learning process. Proceedings of the IEEE International Conference on Computer Vision. 1925–1932
Vicente, S, Kolmogorov, V and Rother, C (2009). Joint optimization of segmentation and appearance models. Proceedings of the IEEE International Conference on Computer Vision. 755–762
Lempitsky, V, Kohli, P, Rother, C and Sharp, T (2009). Image segmentation with a bounding box prior. Proceedings of the IEEE International Conference on Computer Vision. 277–284
Becker, F (2009). Variational Correlation and Decomposition Methods for Particle Image Velocimetry. Heidelberg University, Faculty of Mathematics and Computer Sciences, Heidelberg, Germany. http://www.ub.uni-heidelberg.de/archiv/9766/
Breitenreicher, D and Schnörr, C (2009). Intrinsic Second-Order Geometric Optimization for Robust Point Set Registration Without Correspondence. Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR 2009). Springer. 5681 274-287. http://www.springerlink.com/content/1470n7577713069q/
Gosch, C (2009). Contour Methods for View Point Tracking. University of Heidelberg. http://www.ub.uni-heidelberg.de/archiv/9684/
Lauer, F and Schnörr, C (2009). Spectral Clustering of Linear Subspaces for Motion Segmentation. Proc. IEEE Int. Conf. Computer Vision (ICCV'09). Kyoto, Japan
Nicola, A, Petra, S, Popa, C and Schnörr, C (2009). On A General Extending And Constraining Procedure For Linear Iterative Methods. IWR, University of Heidelberg. http://www.ub.uni-heidelberg.de/archiv/9761
Petra, S and Schnörr, C (2009). Tomopiv Meets Compressed Sensing. IWR, University of Heidelberg. http://www.ub.uni-heidelberg.de/archiv/9760
Petra, S and Schnörr, C (2009). TomoPIV meets Compressed Sensing. Pure Math. Appl. 20 49 – 76. http://www.mat.unisi.it/newsito/puma/public_html/contents.php
Vlasenko, A and Schnörr, C (2009). Variational Approaches for Model-Based PIV and Visual Fluid Analysis. Imaging Measurement Methods for Flow Analysis. Springer. 106 247-256
Yuan, J, Schnörr, C and Steidl, G (2009). Total-Variation Based Piecewise Affine Regularization. Scale Space and Variational Methods in Computer Vision (SSVM 2009). Springer. 5567 552-564
Kondermann, D (2009). Modular Optical Flow Estimation with Applications to Fluid Dynamics. IWR, Fakultät für Mathematik und Informatik, Univ. Heidelberg
Kondermann, C (2009). Postprocessing and Restoration of Optical Flows. IWR, Fakultät für Mathematik und Informatik, Univ. Heidelberg

2008

Ommer, B (2008). Seeing The Objects Behind The Parts: Learning Compositional Models For Visual Recognition. VDM Verlag. http://www.amazon.com/Seeing-Objects-Behind-Parts-Compositional/dp/3639021444/ref=sr_1_1?ie=UTF8&s=books&qid=1232659136&sr=1-1
Andres, B, Köthe, U, Helmstaedter, M, Denk, W and Hamprecht, F A (2008). Segmentation of SBFSEM Volume Data of Neural Tissue by Hierarchical Classification. Pattern Recognition. 30th DAGM Symposium Munich, Germany, June 10-13, 2008. Proceedings. Springer. 5096 142-152PDF icon Technical Report (1.21 MB)
Andres, B, Kondermann, C, Kondermann, D, Köthe, U, Hamprecht, F A and Garbe, C S (2008). On errors-in-variables regression with arbitrary covariance and its application to optical flow estimation. Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on. 1-6PDF icon Technical Report (1.58 MB)
Boppel, S (2008). Peak Identification For Liquid Chromatography And Mass Spectrometry. University of Heidelberg

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