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Censor, Y, Petra, S and Schnörr, C (2020). Superiorization vs. Accelerated Convex Optimization: The Superiorized/Regularized Least Squares Case. J. Appl. Numer. Optimization (in press; arXiv:1911.05498). 2 15-62. http://jano.biemdas.com/archives/1060

Censor, Y, Petra, S and Schnörr, C (2019). Superiorization vs. Accelerated Convex Optimization: The Superiorized/Regularized Least Squares Case. preprint: arXiv. https://arxiv.org/abs/1911.05498

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

Nowozin, S and Sharp, T (2011). Supplementary Material : Decision Tree Fields. Iccv

Garbe, C S, Schimpf, U and Jähne, B (2004). 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. 109 1-18

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

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

Technical Report (2.03 MB)

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

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

Technical Report (798.23 KB)

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

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

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

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

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

Technical Report (524.97 KB)

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

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

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

(2013). Time-of-Flight Imaging: Algorithms, Sensors and Applications (Dagstuhl Seminar 12431). Dagstuhl Reports. Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik. 2 79--104. http://drops.dagstuhl.de/opus/volltexte/2013/3904

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

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

Technical Report (409.1 KB)

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

Bredies, K, Holler, M, Storath, M and Weinmann, A (2018). Total Generalized Variation for Manifold-valued Data. SIAM Journal on Imaging Sciences. 11 1785 - 1848

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

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

Technical Report (2.39 MB)

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

Technical Report (1.47 MB)

Bell, P and Ommer, B (2015). Training Argus. Kunstchronik. Monatsschrift für Kunstwissenschaft, Museumswesen und Denkmalpflege. Zentralinstitut für Kunstgeschichte. 68 414--420

Lang, S and Ommer, B (2021). Transforming Information Into Knowledge: How Computational Methods Reshape Art History. Digital Humanities Quaterly (DHQ). 15

Lang, S and Ommer, B (2021). Transforming Information Into Knowledge: How Computational Methods Reshape Art History. Digital Humanities Quaterly (DHQ). 15. http://digitalhumanities.org/dhq/vol/15/3/000560/000560.html

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

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

Schnörr, (1994). Unique Reconstruction of Piecewise Smooth Images by Minimizing Strictly Convex Non-Quadratic Functionals. 4 189–198

Zern, A, Zisler, M, Petra, S and Schnörr, C (2020). Unsupervised Assignment Flow: Label Learning on Feature Manifolds by Spatially Regularized Geometric Assignment. Journal of Mathematical Imaging and Vision. https://doi.org/10.1007/s10851-019-00935-7

Zern, A, Zisler, M, Petra, S and Schnörr, C (2019). Unsupervised Assignment Flow: Label Learning on Feature Manifolds by Spatially Regularized Geometric Assignment. preprint: arXiv. https://arxiv.org/abs/1904.10863

Brattoli, B, Büchler, U, Dorkenwald, M, Reiser, P, Filli, L, Helmchen, F, Wahl, A - S and Ommer, B (2021). Unsupervised behaviour analysis and magnification (uBAM) using deep learning. Nature Machine Intelligence. https://rdcu.be/ch6pL

Milbich, T, Ghori, O and Ommer, B (2020). Unsupervised Representation Learning by Discovering Reliable Image Relations. Pattern Recognition. 102. http://arxiv.org/abs/1911.07808

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