Publications

Y. Censor, Petra, S., and Schnörr, C., “Superiorization vs. Accelerated Convex Optimization: The Superiorized/Regularized Least Squares Case”, J. Appl. Numer. Optimization (in press; arXiv:1911.05498), vol. 2, pp. 15-62, 2020.

Y. Censor, Petra, S., and Schnörr, C., “Superiorization vs. Accelerated Convex Optimization: The Superiorized/Regularized Least Squares Case”, preprint: arXiv, 2019.

B. Goldlücke, Aubry, M., Kolev, K., and Cremers, D., “A super-resolution framework for high-accuracy multiview reconstruction”, Int. J. Comp. Vision, vol. 106, p. 172--191, 2014.

S. Nowozin and Sharp, T., “Supplementary Material : Decision Tree Fields”, Iccv, 2011.

C. S. Garbe, Schimpf, U., and Jähne, B., “A surface renewal model to analyze infrared image sequences of the ocean surface for the study of air-sea heat and gas exchange”, J. Geophys. Res., vol. 109, pp. 1-18, 2004.

J. Shotton, Winn, J., Rother, C., and Criminisi, A., “TextonBoost for image understanding: Multi-class object recognition and segmentation by jointly modeling texture, layout, and context”, International Journal of Computer Vision, vol. 81, pp. 2–23, 2009.

M. Frank, Plaue, M., Rapp, H., Köthe, U., Jähne, B., and Hamprecht, F. A., “Theoretical and Experimental Error Analysis of Continuous-Wave Time-Of-Flight Range Cameras”, Optical Engineering, vol. 48, 013602, 2009.

Technical Report (2.03 MB)

M. Frank, Plaue, M., Rapp, H., Köthe, U., Jähne, B., and Hamprecht, F. A., “Theoretical and experimental error analysis of continuous-wave time-of-flight range cameras”, Opt. Eng., vol. 48, p. 013602, 2009.

H. Rapp, Frank, M., Hamprecht, F. A., and Jähne, B., “A Theoretical and Experimental Investigation of the Systematic Errors and Statistical Uncertainties of Time-of-Flight Cameras”, Int. J. Intelligent Systems Technologies and Applications, vol. 5, pp. 402-413, 2008.

Technical Report (798.23 KB)

H. Rapp, Frank, M., Hamprecht, F. A., and Jähne, B., “A theoretical and experimental investigation of the systematic errors and statistical uncertainties of time-of-flight cameras”, Int. J. Intelligent Systems Technologies and Applications, vol. 5, p. 402--413, 2008.

J. Weickert and Schnörr, C., “A Theoretical Framework for Convex Regularizers in PDE–Based Computation of Image Motion”, Int. J. Computer Vision, vol. 45, pp. 245–264, 2001.

M. Bühl and Hamprecht, F. A., “Theoretical Investigation of NMR Chemical Shifts and Reactivities of Oxovanadium (V) Compounds”, Journal of Computational Chemistry, vol. 19, pp. 113-122, 1998.

C. Cali, Baghabra, J., Boges, D. J., Holst, G. R., Kreshuk, A., Hamprecht, F. A., Srinivasan, M., Lehväslaiho, H., and Magistretti, P. J., “Three-dimensional immersive virtual reality for studying cellular compartments in 3D models from EM preparations of neural tissues”, Journal of Comparative Neurology, vol. 524, pp. 23-38, 2015.

J. Schmähling, Hamprecht, F. A., and Hoffmann, D. M. P., “A three-dimensional measure of surface roughness based on mathematical morphology”, International Journal of Machine Tools and Manufacture, vol. 46 (14), pp. 1764-1769, 2006.

Technical Report (524.97 KB)

H. Kubinyi, Hamprecht, F. A., and Mietzner, T., “Threedimensional Quantitative Similarity-Activity Relationships (3DQSiAR) from SEAL Similarity Matrices”, Journal of Medicinal Chemistry, vol. 41, pp. 2553-2564, 1998.

A. Berthe, Kondermann, D., Christensen, C., Goubergrits, L., Garbe, C. S., Affeld, K., and Kertzscher, U., “Three-dimensional, three-component wall-PIV”, Exp. Fluids, vol. 48, p. online, 2010.

B. Goldlücke, Strekalovskiy, E., and Cremers, D., “Tight convex relaxations for vector-valued labeling”, SIAM Journal on Imaging Sciences, 2013.

J. Davis, Jähne, B., Kolb, A., Raskar, R., Theobalt, C., Davis, J., Jähne, B., Raskar, R., Theobalt, C., and Kolb, A., Eds., “Time-of-Flight Imaging: Algorithms, Sensors and Applications (Dagstuhl Seminar 12431)”, Dagstuhl Reports, vol. 2, p. 79--104, 2013.

S. Petra and Schnörr, C., “TomoPIV meets Compressed Sensing”, Pure Math. Appl., vol. 20, pp. 49 – 76, 2009.

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

Technical Report (409.1 KB)

H. Meine, Köthe, U., and Stelldinger, P., “A Topological Sampling Theorem for Robust Boundary Reconstruction and Image Segmentation”, Discrete Applied Mathematics, vol. 157, pp. 524-541, 2008.

K. Bredies, Holler, M., Storath, M., and Weinmann, A., “Total Generalized Variation for Manifold-valued Data”, SIAM Journal on Imaging Sciences, vol. 11, no. 3, pp. 1785 - 1848, 2018.

M. Esposito, Hennersperger, C., Göbl, R., Demaret, L., Storath, M., Navab, N., Baust, M., and Weinmann, A., “Total variation regularization of pose signals with an application to 3D freehand ultrasound”, IEEE Transactions on Medical Imaging, vol. 38(10), pp. 2245-2258, 2019.

J. P. Kauppi, Kandemir, M., Saarinen, V. M., Hirvenkari, L., Parkkonen, L., Klami, A., Hari, R., and Kaski, S., “Towards brain-activity-controlled information retrieval: Decoding image relevance from MEG signals”, NeuroImage, vol. 112, pp. 288-298, 2015.

Technical Report (2.39 MB)

M. Hanselmann, Köthe, U., Kirchner, M., Renard, B. Y., Amstalden, E. R., Glunde, K., Heeren, R. M. A., and Hamprecht, F. A., “Towards Digital Staining using Imaging Mass Spectrometry and Random Forests”, Journal of Proteome Research, vol. 8, pp. 3558-3567, 2009.

Technical Report (1.47 MB)

P. Bell and Ommer, B., “Training Argus”, Kunstchronik. Monatsschrift für Kunstwissenschaft, Museumswesen und Denkmalpflege, vol. 68, p. 414--420, 2015.

S. Lang and Ommer, B., “Transforming Information Into Knowledge: How Computational Methods Reshape Art History”, Digital Humanities Quaterly (DHQ), vol. 15, no. 3, 2021.

B. Jähne and Riemer, K., “Two-dimensional wave number spectra of small-scale water surface waves”, J. Geophys. Res., vol. 95, p. 11531--11646, 1990.

A. Lifermann, Jähne, B., and Ramamonjiarisoa, A., “Une ètude en soufflerie de la rèflexion des hyperfrèquences par des champs de houles et de vagues”, Oceanologia Acta, vol. SP, p. 15--22, 1987.

C. Schnörr, “Unique Reconstruction of Piecewise Smooth Images by Minimizing Strictly Convex Non-Quadratic Functionals”, vol. 4, pp. 189–198, 1994.

A. Zern, Zisler, M., Petra, S., and Schnörr, C., “Unsupervised Assignment Flow: Label Learning on Feature Manifolds by Spatially Regularized Geometric Assignment”, Journal of Mathematical Imaging and Vision, 2020.

A. Zern, Zisler, M., Petra, S., and Schnörr, C., “Unsupervised Assignment Flow: Label Learning on Feature Manifolds by Spatially Regularized Geometric Assignment”, preprint: arXiv, 2019.

B. Brattoli, Büchler, U., Dorkenwald, M., Reiser, P., Filli, L., Helmchen, F., Wahl, A. - S., and Ommer, B., “Unsupervised behaviour analysis and magnification (uBAM) using deep learning”, Nature Machine Intelligence, 2021.

T. Milbich, Ghori, O., and Ommer, B., “Unsupervised Representation Learning by Discovering Reliable Image Relations”, Pattern Recognition, vol. 102, 2020.

Pages