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Journal Article
M. Geese, Ruhnau, P., and Jähne, B., “PRNU and DSNU Maximum Likelihood Estimation Using Sensor Statistics”, tm --- Technisches Messen, vol. 80, p. 321--328, 2013.
S. Weber, Schüle, T., and Schnörr, C., “Prior Learning and Convex-Concave Regularization of Binary Tomography”, Electr. Notes in Discr. Math., vol. 20, pp. 313-327, 2005.
B. Jähne, “Prinzipien und Verfahren zur Aufnahme spektraler Bilddaten - Vereinfachte Bildanalyse”, QZ, vol. 53, p. 45--48, 2008.
M. Jäger and Hamprecht, F. A., “Principal Component Imagery for the Quality Monitoring of Dynamic Laser Welding Processes”, IEEE Transactions on Industrial Electronics, vol. 56:4, pp. 1307-1313, 2008.
F. A. Hamprecht, Jost, D., Rüttimann, M., Calamai, F., and Kowalski, J. J., “Preliminary results on the prediction of countershock success with fibrillation power”, Resuscitation, vol. 50, pp. 297-299, 2001.
F. Besse, Rother, C., Fitzgibbon, A., and Kautz, J., “PMBP: PatchMatch Belief Propagation for correspondence field estimation”, International Journal of Computer Vision, vol. 110, pp. 2–13, 2014.
F. Besse, Rother, C., Fitzgibbon, A., and Kautz, J., “PMBP: PatchMatch Belief Propagation for correspondence field estimation”, International Journal of Computer Vision, vol. 110, pp. 2–13, 2014.
F. Besse, Rother, C., Fitzgibbon, A., and Kautz, J., “PMBP: PatchMatch Belief Propagation for correspondence field estimation”, International Journal of Computer Vision, vol. 110, pp. 2–13, 2014.
A. Vlasenko and Schnörr, C., “Physically Consistent and Efficient Variational Denoising of Image Fluid Flow Estimates”, IEEE Trans. Image Proc., vol. 19, pp. 586-595, 2010.
A. Vlasenko and Schnörr, C., “Physically Consistent and Efficient Variational Denoising of Image Fluid Flow Estimates”, IEEE Trans.~Image Proc., vol. 19, pp. 586-595, 2010.PDF icon Technical Report (2.65 MB)
A. Denitiu, Petra, S., Schnörr, C., and Schnörr, C., “Phase Transitions and Cosparse Tomographic Recovery of Compound Solid Bodies from Few Projections”, Fundamenta Informaticae, vol. 135, p. 73--102, 2014.PDF icon Technical Report (2.24 MB)
A. Denitiu, Petra, S., Schnörr, C., and Schnörr, C., “Phase Transitions and Cosparse Tomographic Recovery of Compound Solid Bodies from Few Projections”, Fundamenta Informaticae, vol. 135, pp. 73–102, 2014.
T. Preusser, Droske, M., Garbe, C. S., Rumpf, M., and Telea, A., “A phase field method for joint denoising, edge detection, and motion estimation in image sequence processing.”, SIAM Journal of Applied Mathematics, vol. 68, pp. 599-618, 2007.
S. Munder, Schnörr, C., and Gavrila, D. M., “Pedestrian Detection and Tracking Using a Mixture of View-Based Shape-Texture Models”, IEEE Trans. Intell. Transp. Systems, vol. 9, pp. 333-343, 2008.
J. Weickert and Schnörr, C., “PDE–Based Preprocessing of Medical Images”, Künstliche Intelligenz, vol. 3, pp. 5–10, 2000.
F. Hering, Wierzimok, D., Leue, C., and Jähne, B., “Particle tracking velocimetry beneath water waves. part II : water waves”, Exp. Fluids, vol. 24, pp. 10-16, 1998.
F. Hering, Wierzimok, D., Leue, C., and Jähne, B., “Particle tracking velocimetry beneath water waves. Part I: visualization and tracking algorithms”, Exp. Fluids, vol. 23, p. 472--482, 1997.
P. Swoboda, Shekhovtsov, A., Kappes, J. Hendrik, Schnörr, C., and Savchynskyy, B., “Partial Optimality by Pruning for MAP-Inference with General Graphical Models”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 38, pp. 1370–1382, 2016.
P. Swoboda, Shekhovtsov, A., Kappes, J. H., Schnörr, C., and Savchynskyy, B., “Partial Optimality by Pruning for MAP-Inference with General Graphical Models”, IEEE Trans. Patt. Anal. Mach. Intell., vol. 38, pp. 1370–1382, 2016.
B. Jähne, Münnich, K. O., Bösinger, R., Dutzi, A., Huber, W. A., and Libner, P., “On the parameters influencing air-water gas exchange”, J. Geophys. Res., vol. 92, p. 1937--1950, 1987.
W. Peckar, Schnörr, C., Rohr, K., and Stiehl, H. –S., “Parameter-Free Elastic Deformation Approach for 2D and 3D Registration Using Prescribed Displacements”, J. Math. Imaging and Vision, vol. 10, pp. 143–162, 1999.
N. J. Mitra, Stam, J., Xu, K., Cheng, M. - M., Prisacariu, V. Adrian, Zheng, S., Torr, P. H. S., and Rother, C., “Pacific Graphics 2015 DenseCut: Densely Connected CRFs for Realtime GrabCut”, vol. 34, 2015.
B. Y. Renard, Xu, B., Kirchner, M., Zickmann, F., Winter, D., Korten, S., Brattig, N., Tzur, A., Hamprecht, F. A., and Steen, H., “Overcoming species boundaries in peptide identification with BICEPS”, Molecular and Cellular Proteomics, vol. 11, 2012.PDF icon Technical Report (444.6 KB)
S. Meister, Jähne, B., and Kondermann, D., “Outdoor Stereo Camera System for the Generation of Real-World Benchmark Data Sets”, Optical Engineering, vol. 51, pp. 021107-1, 2012.
S. Meister, Jähne, B., and Kondermann, D., “Outdoor stereo camera system for the generation of real-world benchmark data sets”, Opt. Eng., vol. 51, p. 021107, 2012.
A. - S. Wahl, Büchler, U., Brändli, A., Brattoli, B., Musall, S., Kasper, H., Ineichen, B. V., Helmchen, F., Ommer, B., and Schwab, M. E., “Optogenetically stimulating the intact corticospinal tract post-stroke restores motor control through regionalized functional circuit formation”, Nature Communications, p. (ASW & UB contributed equally; BO and MES contributed equally), 2017.
C. Rother, Kolmogorov, V., Lempitsky, V., and Szummer, M., “Optimizing Binary MRFs via Extended Roof Duality Technical Report MSR-TR-2007-46”, Computing, 2007.
J. Lellmann, Lenzen, F., and Schnörr, C., “Optimality Bounds for Variational Relaxations of Optimal Partition Problems”, 2010.
J. Lellmann, Lenzen, F., and Schnörr, C., “Optimality Bounds for a Variational Relaxation of the Image Partitioning Problem”, Journal of Mathematical Imaging and Vision, vol. 47 (3), pp. 239-257, 2013.
J. Lellmann, Lenzen, F., and Schnörr, C., “Optimality Bounds for a Variational Relaxation of the Image Partitioning Problem”, Journal of Mathematical Imaging and Vision, vol. 47, pp. 239-257, 2012.
J. Lellmann, Lenzen, F., and Schnörr, C., “Optimality Bounds for a Variational Relaxation of the Image Partitioning Problem”, Journal of Mathematical Imaging and Vision, vol. 47, pp. 239-257, 2012.PDF icon Technical Report (616.16 KB)
M. Erz and Jähne, B., “Optimale Kameraauswahl für maschinelles Sehen durch standardisierte Charakterisierung”, tm --- Technisches Messen, vol. 78, p. 377--383, 2011.
H. R. Künsch, Agrell, E., and Hamprecht, F. A., “Optimal lattices for sampling”, IEEE Transactions on Information Theory, vol. 51, pp. 634-647, 2005.
F. Jug, Pietzsch, T., Kainmüller, D., Funke, J., Kaiser, M., van Nimwegen, E., Rother, C., and Myers, G., “Optimal joint segmentation and tracking of escherichia coli in the mother machine”, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 8677, pp. 25–36, 2014.
M. Atif and Jähne, B., “Optimal Depth Estimation from a Single Image by Computational Imaging Using Chromatic Aberrations”, tm --- Technisches Messen, vol. 80, p. 343--348, 2013.

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