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Conference Paper
H. Schilling, Gutsche, M., Brock, A., Späth, D., Rother, C., and Krispin, K., “Mind the Gap – A Benchmark for Dense Depth Prediction beyond Lidar”, in 2nd Workshop on Safe Artificial Intelligence for Automated Driving, in conjunction with CVPR 2020, 2020.
M. Klar, Stybalkowski, P., Spies, H., and Jähne, B., “A miniaturized 3-D particle-tracking velocimetry system to measure the pore flow within a gravel layer”, in Proc. 11th Int. Symp. Applications of Laser Techniques to Fluid Mechanics, 2002, p. 2.3.
C. Rother, Kohli, P., Feng, W., and Jia, J., “Minimizing sparse higher order energy functions of discrete variables”, 2010, pp. 1382–1389.
M. Enzweiler and Gavrila, D. M., “A Mixed Generative-Discriminative Framework for Pedestrian Classification”, in Proc. Int. Conf. Comp. Vision and Patt. Recog. (CVPR), 2008.
C. S. Garbe, Spies, H., and Jähne, B., “Mixed OLS-TLS for the estimation of dynamic processes with a linear source term”, in Proceedings of the 24th DAGM Symposium on Pattern Recognition, 2002, vol. 2449, p. 463--471.
M. Wulf, Stiehl, H. S., and Schnörr, C., “A model of spatiotemporal receptive fields in the primate retina”, in Proc. 1st Göttingen Conf. German Neurosci. Soc., 1999, vol. II.
M. Wulf, Stiehl, H. S., and Schnörr, C., “Modeling spatiotemporal receptive fields in the primate retina”, in Proc. Cognitive Neurosci. Conf., Bremen, Germany, 1999.
H. Herrmann and Jähne, B., “Modulare Software für die höherdimensionale Bildverarbeitung”, in 5. ABW-Workshop, TA Esslingen 20.--21.01.1998, 1998.
D. van Halsema, Snoeij, P., Calkoen, C. J., Oost, W. A., Vogelzang, J., and Jähne, B., “Modulation of the microwave backscatter by long gravity waves as measured in a very large wind/wave flume”, in Proc. IGARSS '91, 1991, vol. 3, p. 1293--1296.
M. Enzweiler, Kanter, P., and Gavrila, D. M., “Monocular Pedestrian Recognition Using Motion Parallax”, in Proc. IEEE Symposium on Intelligent Vehicles, 2008, pp. 792-797.
A. Monroy, Bell, P., and Ommer, B., “A Morphometric Approach to Reception Analysis of Premodern Art”, in Scientific Computing & Cultural Heritage, 2013.PDF icon Technical Report (17.75 MB)
D. Cremers and Schnörr, C., “Motion Competition: Variational Integration of Motion Segmentation and Shape Regularization”, in Pattern Recognition, Proc. 24th DAGM Symposium, Zürich, Switzerland, 2002, vol. 2449, pp. 472–480.
B. Jähne, “Motion determination in space-time images”, in Proc. Computer Vision -- ECCV 90, Lecture Notes in Computer Science 427, 1990, p. 161--173.
B. Jähne, “Motion determination in space-time images”, in Image Processing III, SPIE Proceeding 1135, international congress on optical science and engineering, Paris, 24-28 April 1989, 1989, p. 147--152.
D. Kondermann, Kondermann, C., Berthe, A., Kertzscher, U., and Garbe, C. S., “Motion Estimation Based on a Temporal Model of Fluid Flows”, in 13th International Symposium on Flow Visualization, 2008, pp. 1-10.
C. Schnörr and Peckar, W., “Motion-Based Identification of Deformable Templates”, in Proc. 6th Int. Conf. on Computer Analysis of Images and Patterns (CAIP '95), Prague, Czech Republic, 1995, vol. 970, pp. 122-129.
M. Becker, Baron, M., Kondermann, D., Bussler, M., and Helzle, V., “Movie Dimensionalization Via Sparse User Annotations”, in submitted to 3DTV-Con, 2013.
D. Kondermann and Becker, M., “Movie Dimensionalization Via Sparse User Annotations”, in submitted to ICCV, 2013.
S. Tourani, Shekhovtsov, A., Rother, C., and Savchynskyy, B., “MPLP++: Fast, Parallel Dual Block-Coordinate Ascent for Dense Graphical Models”, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2018, vol. 11208 LNCS, pp. 264–281.
J. H. Kappes, Schmidt, S., and Schnörr, C., “MRF Inference by k-Fan Decomposition and Tight Lagrangian Relaxation”, in European Conference on Computer Vision (ECCV), 2010, vol. 6313, pp. 735–747.
J. H. Kappes, Schmidt, S., and Schnörr, C., “MRF Inference by k-Fan Decomposition and Tight Lagrangian Relaxation”, in European Conference on Computer Vision (ECCV), 2010, vol. 6313, p. 735--747.
J. H. Kappes, Schmidt, S., and Schnörr, C., “MRF Inference by k-Fan Decomposition and Tight Lagrangian Relaxation”, in European Conference on Computer Vision (ECCV), 2010, vol. 6313, p. 735--747.PDF icon Technical Report (1.49 MB)
P. Geißler and Jähne, B., “A multi-camera system for in-shore measurements of bubble size distributions beneath breaking waves”, in Optical 3-D Measurement Techniques IV, Zurich, Sept. 29 - Oct. 2, 1997, 1997, p. 251--258.
G. Balschbach, Klinke, J., and Jähne, B., “Multichannel shape from shading techniques for moving specular surfaces”, in ECCV 1998, 1998, vol. 1407, p. 170--184.
G. Balschbach, Klinke, J., and Jähne, B., “Multichannel shape from shading techniques for reconstruction of specular surfaces”, in Tagungsband Herbsttagung des Graduiertenkollegs "3D Bildanalyse und -synthese", 1997.
Z. Lin, Erz, M., and Jähne, B., “Multi-frequency multi-sampling fluorescence lifetime imaging using a high-speed line-scan camera”, in Optics, Photonics, and Digital Technologies for Multimedia Applications, 12--15 April 2010, Brussels, 2010, vol. 7723, p. 77231S.
G. Urban, Bendszus, M., Hamprecht, F. A., and Kleesiek, J., “Multi-modal Brain Tumor Segmentation using Deep Convolutional NeuralNetworks”, in MICCAI BraTS (Brain Tumor Segmentation) Challenge. Proceedings, winningcontribution, 2014, pp. 31-35.
D. Cremers, Sochen, N., and Schnörr, C., “Multiphase Dynamic Labeling for Variational Recognition-Driven Image Segmentation”, in Computer Vision – ECCV 2004, 2004, vol. 3024, pp. 74-86.
C. N. Straehle, Kandemir, M., Köthe, U., and Hamprecht, F. A., “Multiple instance learning with response-optimized random forests”, in ICPR. Proceedings, 2014, pp. 3768 - 3773.PDF icon Technical Report (296.66 KB)
B. Ommer and Malik, J., “Multi-scale Object Detection by Clustering Lines”, in Proceedings of the IEEE International Conference on Computer Vision, 2009, p. 484--491.PDF icon Technical Report (3.18 MB)
M. Hanselmann, Köthe, U., Renard, B. Y., Kirchner, M., Heeren, R. M. A., and Hamprecht, F. A., “Multivariate Watershed Segmentation of Compositional Data”, in Proceedings of the 15th International Conference on Discrete Geometry for Computer Imagery (DGCI), in press, 2009, vol. 5810, pp. 180-192.PDF icon Technical Report (1.25 MB)
S. Wolf, Pape, C., Bailoni, A., Rahaman, N., Kreshuk, A., Köthe, U., and Hamprecht, F. A., “The Mutex Watershed: Efficient, Parameter-Free Image Partitioning”, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2018, vol. 11208 LNCS, pp. 571–587.
M. Heiler and Schnörr, C., “Natural Statistics for Natural Image Segmentation”, in Proc. IEEE Int. Conf. Computer Vision (ICCV 2003), Nice, France, 2003, pp. 1259-1266.
R. Rombach, Esser, P., and Ommer, B., “Network Fusion for Content Creation with Conditional INNs”, in CVPRW 2020 (AI for Content Creation), 2020.
B. Jähne, “Neue Ansätze zur Bildfolgenanalyse”, in Proc. 9. DAGM-Symposium zur Mustererkennung 1987, 1987, vol. 149, p. 287.

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