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Journal Article

B. Savchynskyy, Schmidt, S., Kappes, J. H., and Schnörr, C., “Efficient MRF Energy Minimization via Adaptive Diminishing Smoothing”, UAI. Proceedings, pp. 746-755, 2012.

A. Criminisi, Blake, A., Rother, C., Shotton, J., and Torr, P. H. S., “Efficient dense stereo with occlusions for new view-synthesis by four-state dynamic programming”, International Journal of Computer Vision, vol. 71, pp. 89–110, 2007.

A. Criminisi, Shotton, J., Blake, A., and Torr, P., “Efficient dense stereo and novel-view synthesis for gaze manipulation in one-to-one teleconferencing”, 2004.

J. Funke, Andres, B., Hamprecht, F. A., Cardona, A., and Cook, M., “Efficient Automatic 3D-Reconstruction of Branching Neurons from EM Data”, CVPR 2012. Proceedings, pp. 1004-1011, 2012.

Technical Report (1.64 MB)

K. Rohr and Schnörr, C., “An Efficient Approach to the Identification of Characteristic Intensity Variations”, vol. 11, pp. 273–277, 1993.

L. Kiefer, Storath, M., and Weinmann, A., “An efficient algorithm for the piecewise affine-linear Mumford-Shah model based on a Taylor jet splitting”, IEEE Transactions on Image Processing, vol. 29, 2019.

Technical Report (2.04 MB)

M. Storath, Brandt, C., Hofmann, M., Knopp, T., Salamon, J., Weber, A., and Weinmann, A., “Edge preserving and noise reducing reconstruction for magnetic particle imaging”, IEEE Transactions on Medical Imaging, vol. 36, no. 1, pp. 74 - 85, 2017.

Technical Report (1.43 MB)

A. - S. Wahl, Erlebach, E., Brattoli, B., Büchler, U., Kaiser, J., Ineichen, V. B., Mosberger, A. C., Schneeberger, S., Imobersteg, S., Wieckhorst, M., Stirn, M., Schroeter, A., Ommer, B., and Schwab, M. E., “Early reduced behavioral activity induced by large strokes affects the efficiency of enriched environment in rats”, Sage Journals, vol. Journal of Cerebral Blood Flow & Metabolism, 2018.

0271678x18777661.pdf (770.87 KB)

P. Swoboda, Kuske, J., and Savchynskyy, B., “A Dual Ascent Framework for Lagrangean Decomposition of Combinatorial Problems”, arXiv, preprint, 2016.

J. Scholz, Wiersbinski, T., Ruhnau, P., Kondermann, D., Garbe, C. S., Hain, R., and Beushausen, V., “Double-pulse planar-LIF investigations using fluorescence motion analysis for mixture formation investigation”, Exp. Fluids, vol. 45, p. 583--593, 2008.

T. Kohlberger, Schnörr, C., Bruhn, A., and Weickert, J., “Domain decomposition for variational optical flow computation”, IEEE Trans. Image Proc., vol. 14, pp. 1125-1137, 2005.

V. Uhlmann, Haubold, C., Hamprecht, F. A., and Unser, M., “Diverse Shortest Paths for Bioimage Analysis”, Bioinformatics, pp. 1-3, 2017.

T. Schüle, Schnörr, C., Weber, S., and Hornegger, J., “Discrete Tomography By Convex-Concave Regularization and D.C. Programming”, Discr. Appl. Math., vol. 151, pp. 229-243, 2005.

J. Yuan, Schnörr, C., and Mémin, E., “Discrete Orthogonal Decomposition and Variational Fluid Flow Estimation”, J.~Math.~Imag.~Vision, vol. 28, pp. 67-80, 2007.

Technical Report (752.44 KB)

B. Savchynskyy, “Discrete Graphical Models — An Optimization Perspective”, Foundations and Trends® in Computer Graphics and Vision, vol. 11, pp. 160–429, 2019.

J. Lellmann, Lellmann, B., Widmann, F., and Schnörr, C., “Discrete and Continuous Models for Partitioning Problems”, Int.~J.~Comp.~Visionz, vol. 104, pp. 241-269, 2013.

Technical Report (4.74 MB)

A. Vijayan, Tofanelli, R., Strauss, S., Cerrone, L., Wolny, A., Strohmeier, J., Kreshuk, A., Hamprecht, F. A., Smith, R. S., and Schneitz, K., “A Digital 3D Reference Atlas Reveals Cellular Growth Patterns Shaping the Arabidopsis Ovule”, eLife, 2021.

D. Cremers, Tischhäuser, F., Weickert, J., and Schnörr, C., “Diffusion Snakes: Introducing Statistical Shape Knowledge into the Mumford–Shah functional”, Int. J. Computer Vision, vol. 50, pp. 295–313, 2002.

J. A. J. Steen, Steen, H., Georgi, A., Parker, K. C., Springer, M., Kirchner, M., Hamprecht, F. A., and Kirschner, M. W., “Different Phosphorylation States of the Anaphase Promoting Complex in Response to Anti-Mitotic Drugs: A Quantitative Proteomic Analysis”, Proceedings of the National Academy of Sciences, vol. 105, pp. 6069-6074, 2008.

Technical Report (173.02 KB)

F. A. Hamprecht, Cohen, A. J., Tozer, D. J., and Handy, N. C., “Development and assessment of new exchange-correlation functionals”, Journal of Chemical Physics, vol. 109, pp. 6264-6271, 1998.

X. Lou, Kirchner, M., Renard, B. Y., Köthe, U., Graf, C., Lee, C., Steen, J. A. J., Steen, H., Mayer, M. P., and Hamprecht, F. A., “Deuteration Distribution Estimation with Improved Sequence Coverage for HX/MS Experiments”, Bioinformatics, vol. 26(12), pp. 1535-1541, 2010.

Technical Report (518.01 KB)

C. Schnörr, “Determining Optical Flow for Irregular Domains by Minimizing Quadratic Functionals of a Certain Class”, ijcv, vol. 6, pp. 25–38, 1991.

E. Eyjolfsdottir, Branson, S., Burgos-Artizzu, X. P., Hoopfer, E. D., Schor, J., Anderson, D. J., and Perona, P., “Detection of social actions in fruit flies”, Lecture Notes in Computer Science, vol. 8690, pp. 772–787, 2014.

B. H. Menze, Ur, J. A., and Sherratt, A. G., “Detection of ancient settlement mounds - Archaeological survey based on the SRTM terrain model”, Photgrammetric Engineering & Remote Sensing, vol. 3, pp. 321-327, 2006.

Technical Report (643.89 KB)

H. Schilling, Diebold, M., Gutsche, M., and Jähne, B., “On the design of a fractal calibration pattern for improved camera calibration”, tm - Technisches Messen, vol. 84, pp. 440–451, 2017.

M. Frank, Plaue, M., and Hamprecht, F. A., “Denoising of Continuous-Wave Time-Of-Flight Depth Images Using Confidence Measures”, Optical Engineering, vol. 48, 077003, 2009.

Technical Report (2.5 MB)

S. Bollweg, Haußmann, M., Kasieczka, G., Luchmann, M., Plehn, T., and Thompson, J., “Deep-Learning Jets with Uncertainties and More”, SciPost Phys, vol. 8, no. 1, 2020.

Technical Report (1.65 MB)

A. Sanakoyeu, Bautista, M., and Ommer, B., “Deep Unsupervised Learning of Visual Similarities”, Pattern Recognition, vol. 78, 2018.

PDF (8.35 MB)

J. Kleesiek, Urban, G., Hubert, A., Schwarz, D., Maier-Hein, K., Bendszus, M., and Biller, A., “Deep MRI brain extraction: A 3D convolutional neural network for skull stripping.”, NeuroImage, vol. 129, pp. 460-469, 2016.

Technical Report (1.14 MB)

T. Dencker, Klinkisch, P., Maul, S. M., and Ommer, B., “Deep learning of cuneiform sign detection with weak supervision using transliteration alignment”, PLoS ONE, vol. 15, no. 12, 2020.

G. -hung Lu, Tsai, W. -ting, and Jähne, B., “Decomposing infrared images of wind waves for quantitative separation into characteristic flow processes”, IEEE Transactions on Geoscience and Remote Sensing, vol. 57, pp. 8304–8316, 2019.

S. Petra, Schnörr, C., and Schröder, A., “Critical Parameter Values and Reconstruction Propertiesof Discrete Tomography: Application to Experimental FluidDynamics”, Fundamenta Informaticae, vol. 125, p. 285--312, 2013.

Technical Report (1.42 MB)

B. Maco, Holtmaat, A., Cantoni, M., Kreshuk, A., Straehle, C. N., Hamprecht, F. A., and Knott, G. W., “Correlative in vivo 2 photon and focused ion beam scanning electron microscopy of cortical neurons”, PloS one, vol. 8 (2), 2013.

Technical Report (2.13 MB)

M. Hering, Körner, K., and Jähne, B., “Correlated speckle noise in white-light interferometry: theoretical analysis of measurement uncertainty”, Appl. Optics, vol. 48, p. 525--538, 2009.

P. Swoboda and Schnörr, C., “Convex Variational Image Restoration with Histogram Priors”, SIAM J.~Imag.~Sci., vol. 6, pp. 1719-1735, 2013.

Technical Report (553.54 KB)

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