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F. Becker, Petra, S., and Schnörr, C., “Optical Flow”, Handbook of Mathematical Methods in Imaging. Springer, 2014.
F. Becker, Wieneke, B., Petra, S., Schröder, A., and Schnörr, C., “Variational Adaptive Correlation Method for Flow Estimation”, IEEE Transactions on Image Processing, vol. 21, pp. 3053 – 3065, 2012.
F. Becker, Wieneke, B., Yuan, J., and Schnörr, C., “A Variational Approach to Adaptive Correlation for Motion Estimation in Particle Image Velocimetry”, in Pattern Recognition – 30th DAGM Symposium, 2008, vol. 5096, pp. 335–344.
F. Becker, Wieneke, B., Yuan, J., and Schnörr, C., “Variational Correlation Approach to Flow Measurement with Window Adaption”, in 14th International Symposium on Applications of Laser Techniques to Fluid Mechanics, 2008, p. 1.1.3.
A. Behl, Hosseini Jafari, O., Mustikovela, S. Karthik, Abu Alhaija, H., Rother, C., and Geiger, A., “Bounding Boxes, Segmentations and Object Coordinates: How Important is Recognition for 3D Scene Flow Estimation in Autonomous Driving Scenarios?”, in Proceedings of the IEEE International Conference on Computer Vision, 2017, vol. 2017-Octob, pp. 2593–2602.
A. Behl, Hosseini Jafari, O., Mustikovela, S. Karthik, Abu Alhaija, H., Rother, C., and Geiger, A., “Bounding Boxes, Segmentations and Object Coordinates: How Important is Recognition for 3D Scene Flow Estimation in Autonomous Driving Scenarios?”, in Proceedings of the IEEE International Conference on Computer Vision, 2017, vol. 2017-Octob, pp. 2593–2602.
T. Beier, Kröger, T., Kappes, J. H., Köthe, U., and Hamprecht, F. A., “Cut, Glue and Cut: A Fast, Approximate Solver for Multicut Partitioning”, in 2014 {IEEE} Conference on Computer Vision and Pattern Recognition, {CVPR} 2014, Columbus, OH, USA, June 23-28, 2014, 2014.PDF icon Technical Report (10.06 MB)
T. Beier, Hamprecht, F. A., and Kappes, J. H., “Fusion Moves for Correlation Clustering”, in CVPR. Proceedings, 2015, pp. 3507-3516.PDF icon Technical Report (1.19 MB)
T. Beier, “Graph based image analysis”, University of Heidelberg, 2014.
T. Beier, Andres, B., Köthe, U., and Hamprecht, F. A., “An Efficient Fusion Move Algorithm for the Minimum Cost Lifted Multicut Problem”, ECCV. Proceedings, vol. LNCS 9906. Springer, pp. 715-730, 2016.PDF icon Technical Report (4.89 MB)
T. Beier, Pape, C., Rahaman, N., Prange, T., Berg, S., Bock, D., Cardona, A., Knott, G. W., Plaza, S. M., Scheffer, L. K., Köthe, U., Kreshuk, A., and Hamprecht, F. A., “Multicut brings automated neurite segmentation closer to human performance”, Nature Methods, vol. 14, no. 2, pp. 101-102, 2017.
T. Beier, “Multicut Algorithms for Neurite Segmentation”. Heidelberg University, 2018.
P. Bell and Ommer, B., “Digital Connoisseur? How Computer Vision Supports Art History”, in Connoisseurship nel XXI secolo. Approcci, Limiti, Prospettive, A. Aggujaro & S. Albl (ed.), Rome: Artemide, 2016.
P. Bell and Ommer, B., “Computer Vision und Kunstgeschichte — Dialog zweier Bildwissenschaften”, in Computing Art Reader: Einführung in die digitale Kunstgeschichte, P. Kuroczyński et al. (ed.), 2018.PDF icon 413-17-83318-2-10-20181210.pdf (2.98 MB)
P. Bell and Ommer, B., “Training Argus”, Kunstchronik. Monatsschrift für Kunstwissenschaft, Museumswesen und Denkmalpflege, vol. 68, p. 414--420, 2015.
P. Bell, Schlecht, J., and Ommer, B., “Nonverbal Communication in Medieval Illustrations Revisited by Computer Vision and Art History”, Visual Resources Journal, Special Issue on Digital Art History, vol. 29, p. 26--37, 2013.
M. Bellagente, Haußmann, M., Luchmann, M., and Plehn, T., “Understanding Event-Generation Networks via Uncertainties”. arXiv preprint, 2021.
A. L. Bendinger, Debus, C., Glowa, C., Karger, C. P., Peter, J., and Storath, M., “Bolus arrival time estimation in dynamic contrast-enhanced magnetic resonance imaging of small animals based on spline models, in press”, Physics in Medicine and Biology, vol. 64, no. 4, 2019.
Y. Bengio, Deleu, T., Rahaman, N., Ke, R., Lachapelle, S., Bilaniuk, O., Goyal, A., and Pal, C., “A Meta-Transfer Objective for Learning to Disentangle Causal Mechanisms”, arXiv preprint arXiv:1901.10912, 2019.PDF icon Technical Report (871.59 KB)
M. Bentele, “Zeitliche Rekonstruktion und Visualisierung dynamischer Prozesse”, IWR, Fakultät für Physik und Astronomie, Univ.\ Heidelberg, 1998.
R. Beranek, “Vermessung von Blickpunkten durch automatische Bildanalyse für ergonomische Fragestellungen bei der Fahrzeugkonstruktion”, IWR, Fakultät für Physik und Astronomie, Univ.\ Heidelberg, 1996.
S. Berg, Kutra, D., Kroeger, T., Straehle, C. N., Kausler, B. X., Haubold, C., Schiegg, M., Ales, J., Beier, T., Rudy, M., Eren, K., Cervantes, J. I., Xu, B., Beuttenmüller, F., Wolny, A., Zhang, C., Köthe, U., Hamprecht, F. A., and Kreshuk, A., “ilastik: interactive machine learning for (bio)image analysis”, Nature Methods, vol. 16, pp. 1226-1232, 2019.
J. Berger and Schnörr, C., “Joint Recursive Monocular Filtering of Camera Motion and Disparity Map”, in 38th German Conference on Pattern Recognition, Hannover, 2016.PDF icon Technical Report (2.34 MB)
J. Berger, Lenzen, F., Becker, F., Neufeld, A., and Schnörr, C., “Second-Order Recursive Filtering on the Rigid-Motion Lie Group SE(3) Based on Nonlinear Observations”. 2015.PDF icon Technical Report (4.42 MB)
K. Berger, Meister, S., Nair, R., and Kondermann, D., “A State of the Art Report on Kinect Sensor Setups in Computer Vision”, in Time-of-Flight and Depth Imaging. Sensors, Algorithms, and Applications, 2013, vol. 8200, pp. 257-272.
J. Berger, Neufeld, A., Becker, F., Lenzen, F., and Schnörr, C., “Second Order Minimum Energy Filtering on SE(3) with Nonlinear Measurement Equations”, in Scale Space and Variational Methods in Computer Vision (SSVM 2015), 2015.PDF icon Technical Report (364.01 KB)
J. Berger, Lenzen, F., Becker, F., Neufeld, A., and Schnörr, C., “{Second-Order Recursive Filtering on the Rigid-Motion Lie Group SE(3) Based on Nonlinear Observations”, J. Math. Imag. Vision, vol. 58, pp. 102–129, 2017.
J. Berger and Schnörr, C., “Joint Recursive Monocular Filtering of Camera Motion and Disparity Map”, in 38th German Conference on Pattern Recognition, 2016.
J. Berger, Neufeld, A., Becker, F., Lenzen, F., and Schnörr, C., “Second Order Minimum Energy Filtering on SE(3) with Nonlinear Measurement Equations”, in Scale Space and Variational Methods in Computer Vision (SSVM 2015), 2015.
J. Berger, Lenzen, F., Becker, F., Neufeld, A., and Schnörr, C., “Second-Order Recursive Filtering on the Rigid-Motion Lie Group SE(3) Based on Nonlinear Observations”. 2015.
M. Bergtholdt, Cremers, D., and Schnörr, C., “Variational Segmentation with Shape Priors”, Handbook of Mathematical Models in Computer Vision. Springer, pp. 147-160, 2005.
M. Bergtholdt, Kappes, J. H., Schmidt, S., and Schnörr, C., “A Study of Parts-Based Object Class Detection Using Complete Graphs”, Int. J. Comp. Vision, vol. 87, pp. 93-117, 2010.
M. Bergtholdt, Kappes, J. H., and Schnörr, C., “Learning of Graphical Models and Efficient Inference for Object Class Recognition”, in Proc. DAGM 2006, 2006, vol. 375-388, pp. 375-388.
M. Bergtholdt and Schnörr, C., “Shape Priors and Online Appearance Learning for Variational Segmentation and Object Recognition in Static Scenes”, Pattern Recognition, Proc. 27th DAGM Symposium, vol. 3663. Springer, pp. 342–350, 2005.
M. Bergtholdt, Kappes, J. H., Schmidt, S., and Schnörr, C., “A Study of Parts-Based Object Class Detection Using Complete Graphs”, Int.~J.~Comp.~Vision, vol. 87, pp. 93-117, 2010.PDF icon Technical Report (2.18 MB)

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