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A

D. Gonzalez-Alvarado, Zeilmann, A., and Schnörr, C., “Assignment Flows and Nonlocal PDEs on Graphs”, GCPR, in press. 2021.

A. Zern, Zeilmann, A., and Schnörr, C., “Assignment Flows for Data Labeling on Graphs: Convergence and Stability”, preprint: arXiv, 2020.

A. Zern, Zeilmann, A., and Schnörr, C., “Assignment Flows for Data Labeling on Graphs: Convergence and Stability”, preprint: arXiv, 2020.

A. - S. Wahl, Omlor, W., Rubio, J. C., Chen, J. L., Zheng, H., Schröter, A., Gullo, M., Weinmann, O., Kobayashi, K., Helmchen, F., Ommer, B., and Schwab, M. E., “Asynchronous Therapy Restores Motor Control by Rewiring of the Rat Corticospinal Tract after Stroke”, Science, vol. 344, p. 1250--1255, 2014.

B. Michael Kelm, Menze, B. H., Zechmann, C. M., Baudendistel, K. T., and Hamprecht, F. A., “Automated Estimation of Tumor Probability in Prostate MRSI: Pattern Recognition vs. Quantification”, Magnetic Resonance in Medicine, vol. 57, pp. 150-159, 2007.

Technical Report (348.05 KB)

C. M. Zechmann, Menze, B. H., Kelm, B. Michael, Zamecnik, P., Ikinger, U., Waldherr, R., Delorme, S., Hamprecht, F. A., and Bachert, P., “Automated vs. manual pattern recognition of 3D 1H MRSI data of patients with prostate cancer”, Academic Radiology, vol. 19, 6, pp. 675-684, 2012.

C. M. Zechmann, Menze, B. H., Kelm, B. Michael, Zamecnik, P., Ikinger, U., Waldherr, R., Delorme, S., Hamprecht, F. A., and Bachert, P., “Automated vs. manual pattern recognition of 3D 1H MRSI data of patients with prostate cancer”, Academic Radiology, vol. 19, 6, pp. 675-684, 2012.

B

S. Weber, Schüle, T., Hornegger, J., and Schnörr, C., “Binary Tomography by Iterating Linear Programs from Noisy Projections”, in Combinatorial Image Analysis, Proc. Int. Workshop on Combinatorial Image Analysis (IWCIA'04), 2004, vol. 3322, pp. 38–51.

T. Hodaň, Michel, F., Brachmann, E., Kehl, W., Buch, A. Glent, Kraft, D., Drost, B., Vidal, J., Ihrke, S., Zabulis, X., Sahin, C., Manhardt, F., Tombari, F., Kim, T. Kyun, Matas, J., and Rother, C., “BOP: Benchmark for 6D object pose estimation”, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2018, vol. 11214 LNCS, pp. 19–35.

C

C. M. Zechmann, Kelm, B. Michael, Zamecnik, P., Ikinger, U., Waldherr, R., Röll, S., Delorme, S., Hamprecht, F. A., and Bachert, P., “Can man still beat the machine? Automated vs. manual pattern recognition of 3D MRSI data of prostate cancer patients”, in Proceedings of the 16th ISMRM, 2006.

Technical Report (664.38 KB)

C. M. Zechmann, Kelm, B. Michael, Zamecnik, P., Ikinger, U., Waldherr, R., Röll, S., Delorme, S., Hamprecht, F. A., and Bachert, P., “Can man still beat the machine? Automated vs. manual pattern recognition of 3D MRSI data of prostate cancer patients”, in Proceedings of the 16th ISMRM, 2006.

Technical Report (664.38 KB)

C. Zhang, Yarkony, J., and Hamprecht, F. A., “Cell detection and segmentation using correlation clustering”, in MICCAI. Proceedings, 2014, pp. 9-16.

Technical Report (8.06 MB)

B. Michael Kelm, Menze, B. H., Neff, T., Zechmann, C. M., and Hamprecht, F. A., “CLARET: a tool for fully automated evaluation of MRSI with pattern recognition methods.”, in Bildverarbeitung für die Medizin 2006 - Algorithmen, Systeme, Anwendungen, 2006, pp. 51-55.

Technical Report (275.25 KB)

F. O. Kaster, Kelm, B. Michael, Zechmann, C. M., Weber, M. - A., Hamprecht, F. A., and Nix, O., “Classification of Spectroscopic Images in the DIROlab Environment”, in World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany, 2009, vol. 25/V, p. 252--255.

Technical Report (145.73 KB)

R. Szeliski, Zabih, R., Scharstein, D., Veksler, O., Kolmogorov, V., Agarwala, A., Tappen, M., and Rother, C., “A comparative study of energy minimization methods for Markov random fields with smoothness-based priors”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 30, pp. 1068–1080, 2008.

R. Szeliski, Zabih, R., Scharstein, D., Veksler, O., Kolmogorov, V., Agarwala, A., Tappen, M., and Rother, C., “A comparative study of energy minimization methods for Markov random fields with smoothness-based priors”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 30, pp. 1068–1080, 2008.

C. Weber, Zechmann, C. M., Kelm, B. Michael, Zamecnik, R., Hendricks, D., Waldherr, R., Hamprecht, F. A., Delorme, S., Bachert, P., and Ikinger, U., “Comparison of correctness of manuel and automatic evaluation of MR-spectrum with prostrate cancer”, Der Urologe, vol. 46, p. 1252, 2007.

C. Weber, Zechmann, C. M., Kelm, B. Michael, Zamecnik, R., Hendricks, D., Waldherr, R., Hamprecht, F. A., Delorme, S., Bachert, P., and Ikinger, U., “Comparison of correctness of manuel and automatic evaluation of MR-spectrum with prostrate cancer”, Der Urologe, vol. 46, p. 1252, 2007.

A. Arnab, Zheng, S., Jayasumana, S., Romera-paredes, B., Kirillov, A., Savchynskyy, B., Rother, C., Kahl, F., and Torr, P., “Conditional Random Fields Meet Deep Neural Networks for Semantic Segmentation”, Cvpr, vol. XX, pp. 1–15, 2018.

D

S. Zheng, Cheng, M. Ming, Warrell, J., Sturgess, P., Vineet, V., Rother, C., and Torr, P. H. S., “Dense semantic image segmentation with objects and attributes”, in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2014, pp. 3214–3221.

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.

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

E

C. J. Zappa, Asher, W. E., Jessup, A. T., Klinke, J., and Long, S. R., “Effect of microscale wave breaking on air-water gas transfer”, in Gas Transfer at Water Surfaces, 2002, vol. 127, p. 23--29.

M. Kandemir, Zhang, C., and Hamprecht, F. A., “Empowering multiple instance histopathology cancer diagnosis by cell graphs”, in MICCAI. Proceedings, 2014, vol. 8674, pp. 228-235.

Technical Report (1.76 MB)

L. Cerrone, Zeilmann, A., and Hamprecht, F. A., “End-to-End Learned Random Walker for Seeded Image Segmentation”, CVPR. Proceedings. pp. 12559-12568, 2019.

B. Michael Kelm, Menze, B. H., Nix, O., Zechmann, C. M., and Hamprecht, F. A., “Estimating Kinetic Parameter Maps from Dynamic Contrast-Enhanced MRI using Spatial Prior Knowledge”, IEEE Transaction on Medical Imaging, vol. 28:10, pp. 1534-1547, 2009.

Technical Report (419.8 KB)

M. Schmidt, Erz, M., Zimmermann, K., and Jähne, B., “Exact modelling of time-of-flight cameras for optimal depth maps”, in International Conference on Computational Photography (ICCP) 2010, 2010.

F

J. Weickert, Heers, J., Schnörr, C., Zuiderveld, K. –J., Scherzer, O., and Stiehl, H. –S., “Fast parallel algorithms for a broad class of nonlinear variational diffusion approaches”, Real–Time Imaging, vol. 7, pp. 31–45, 2001.

G

V. Gulshan, Rother, C., Criminisi, A., Blake, A., and Zisserman, A., “Geodesic star convexity for interactive image segmentation”, in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2010, pp. 3129–3136.

A. Zern, Rohr, K., and Schnörr, C., “Geometric Image Labeling with Global Convex Labeling Constraints”, in Proc. EMMCVPR, 2017.

A. Zern, Rohr, K., and Schnörr, C., “Geometric Image Labeling with Global Convex Labeling Constraints”, in EMMCVPR, 2018, vol. 10746, pp. 533–547.

A. Zeilmann, Savarino, F., Petra, S., and Schnörr, C., “Geometric Numerical Integration of the Assignment Flow”, Inverse Problems, vol. 36, p. 034004 (33pp), 2020.

A. Zeilmann, Savarino, F., Petra, S., and Schnörr, C., “Geometric Numerical Integration of the Assignment Flow”, preprint: arXiv, 2018.

A. Zeilmann, Savarino, F., Petra, S., and Schnörr, C., “Geometric Numerical Integration of the Assignment Flow”, Inverse Problems, 2019.

M. Zisler, Savarino, F., Petra, S., and Schnörr, C., “Gradient Flows on a Riemannian Submanifold for Discrete Tomography”, in Proc. GCPR, 2017.

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