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Author Title Type [ Year(Asc)]
2013
P. Swoboda, Savchynskyy, B., Kappes, J. H., and Schnörr, C., “Partial Optimality via Iterative Pruning for the Potts Model”, in Proceedings of the 4th International Conference on Scale Space and Variational Methods in Computer Vision SSVM, 2013, pp. 477-488.
P. Swoboda, Savchynskyy, B., Kappes, J. H., and Schnörr, C., “Partial Optimality via Iterative Pruning for the Potts Model”, in Scale Space and Variational Methods (SSVM 2013), 2013.PDF icon Technical Report (159.71 KB)
P. Swoboda, Savchynskyy, B., Kappes, J. H., and Schnörr, C., “Partial Optimality via Iterative Pruning for the Potts Model”, in Scale Space and Variational Methods (SSVM 2013), 2013.
P. Swoboda, Savchynskyy, B., Kappes, J. H., and Schnörr, C., “Persistency by Pruning for General Graphical Models”, in submitted to NIPS 2013., 2013.
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. Wanner and Goldlücke, B., “Reconstructing Reflective and Transparent Surfaces from Epipolar Plane Images”, Pattern Recognition. Springer, p. 1--10, 2013.
B. Ommer, “The Role of Shape in Visual Recognition”, in Shape Perception in Human Computer Vision: An Interdisciplinary Perspective, Springer, 2013, p. 373--385.PDF icon Technical Report (8.18 MB)
F. Herrmannsdörfer, “SimpleSTORM an Efficient Selfcalibrating Reconstruction Algorithm for Single and Multi-Channel Localisation Microscopy”, University of Heidelberg, 2013.
S. Meister, Nair, R., and Kondermann, D., “Simulation of Time-of-Flight Sensors using Global Illumination”, in Vision, Modeling and Visualization (VMV), 2013 International Workshop on. Proceedings, 2013, pp. 33-40.
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.
R. Nair, Ruhl, K., Lenzen, F., Meister, S., Schäfer, H., Garbe, C. S., Eisemann, M., and Kondermann, D., “A Survey on Time-of-Flight Stereo Fusion”, in Time-of-Flight Imaging: Algorithms, Sensors and Applications, 2013, vol. 8022, pp. 105-127.
R. Nair, Ruhl, K., Lenzen, F., Meister, S., Schäfer, H., Garbe, C. S., Eisemann, M., Magnor, M., and Kondermann, D., “A Survey on Time-of-Flight Stereo Fusion”, Time-of-Flight and Depth Imaging: Sensors, Algorithms, and Applications, vol. 8200. Springer, pp. 105-127, 2013.PDF icon Technical Report (6.05 MB)
R. Nair, Ruhl, K., Lenzen, F., Meister, S., Schäfer, H., Garbe, C. S., Eisemann, M., Magnor, M., and Kondermann, D., “A Survey on Time-of-Flight Stereo Fusion”, Time-of-Flight and Depth Imaging: Sensors, Algorithms, and Applications, vol. 8200. Springer, pp. 105-127, 2013.
D. Lefloch, Nair, R., Lenzen, F., Schäfer, H., Streeter, L., Cree, M. J., Koch, R., and Kolb, A., “Technical Foundation and Calibration Methods for Time-of-Flight Cameras”, Time-of-Flight and Depth Imaging: Sensors, Algorithms, and Applications, vol. 8200. Springer, pp. 3-24, 2013.
D. Lefloch, Nair, R., Lenzen, F., Schäfer, H., Streeter, L., and Cree, M. J., “Technical Foundation and Calibration Methods for Time-of-Flight Cameras”, in Time-of-Flight Imaging: Algorithms, Sensors and Applications, 2013, vol. 8200.
B. Goldlücke, Strekalovskiy, E., and Cremers, D., “Tight convex relaxations for vector-valued labeling”, SIAM Journal on Imaging Sciences, 2013.
J. Davis, Jähne, B., Kolb, A., Raskar, R., Theobalt, C., Davis, J., Jähne, B., Raskar, R., Theobalt, C., and Kolb, A., Eds., “Time-of-Flight Imaging: Algorithms, Sensors and Applications (Dagstuhl Seminar 12431)”, Dagstuhl Reports, vol. 2, p. 79--104, 2013.
P. Yarlagadda, Monroy, A., Carque, B., and Ommer, B., “Towards a Computer-based Understanding of Medieval Images”, in Scientific Computing & Cultural Heritage, Springer, 2013, p. 89--97.
J. H. Kappes, Speth, M., Reinelt, G., and Schnörr, C., “Towards Efficient and Exact MAP-Inference for Large Scale Discrete Computer Vision Problems via Combinatorial Optimization”, in CVPR, 2013.PDF icon Technical Report (623.84 KB)
J. H. Kappes, Speth, M., Reinelt, G., and Schnörr, C., “Towards Efficient and Exact MAP-Inference for Large Scale Discrete Computer Vision Problems via Combinatorial Optimization”, in CVPR, 2013.
B. X. Kausler, “Tracking-by-Assignment as a Probabilistic Graphical Model with Applications in Developmental Biology”. University of Heidelberg, 2013.
P. Swoboda and Schnörr, C., “Variational Image Segmentation and Cosegmentation with the Wasserstein Distance”, in Energy Minimization Methods in Computer Vision and Pattern Recognition, 2013, vol. 8081, p. 321--334.PDF icon Technical Report (8.06 MB)
P. Swoboda and Schnörr, C., “Variational Image Segmentation and Cosegmentation with the Wasserstein Distance”, in Energy Minimization Methods in Computer Vision and Pattern Recognition, 2013, vol. 8081, pp. 321–334.
F. Becker, Lenzen, F., Kappes, J. H., and Schnörr, C., “Variational Recursive Joint Estimation of Dense Scene Structure and Camera Motion from Monocular High Speed Traffic Sequences”, International Journal of Computer Vision, vol. 105, no. 3, p. 269--297, 2013.PDF icon Technical Report (15.4 MB)
F. Becker, Lenzen, F., Kappes, J. H., and Schnörr, C., “Variational Recursive Joint Estimation of Dense Scene Structure and Camera Motion from Monocular High Speed Traffic Sequences”, International Journal of Computer Vision, vol. 105, pp. 269–297, 2013.
F. Becker, Lenzen, F., Kappes, J. H., and Schnörr, C., “Variational Recursive Joint Estimation of Dense Scene Structure and Camera Motion from Monocular High Speed Traffic Sequences”, International Journal of Computer Vision, vol. 105 (3), pp. 269-297, 2013.
B. Goldlücke and Wanner, S., “The Variational Structure of Disparity and Regularization of 4D Light Fields”, in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013.
B. Jähne, “Von optischer 3D-Messtechnik zu lichtfeldbasierter Bildakquisition und -verarbeitung”, in Photogrammetrie, Laserscanning, Optische 3D-Messtechnik, Beiträge der Oldenburger 3D-Tage 2013, 2013, p. 1--8.
B. Xu, “Weakly supervised learning: Active schemes and partial annotations”, University of Heidelberg, 2013.
C. N. Straehle, Köthe, U., and Hamprecht, F. A., “Weakly supervised learning of image partitioning using decision trees with structured split criteria”, in ICCV 2013. Proceedings, 2013, pp. 1849-1856.PDF icon Technical Report (5.97 MB)
P. Márquez-Valle, Gil, D., Hernàndez-Sabaté, A., and Kondermann, D., “When is a confidence measure good enough?”, submitted to CVPR 2013, 2013.
2012
B. Andres, Köthe, U., Kröger, T., Helmstaedter, M., Briggmann, K. L., Denk, W., and Hamprecht, F. A., “3D Segmentation of SBFSEM Images of Neuropil by a Graphical Model over Supervoxel Boundaries”, Medical Image Analysis, vol. 16 (2012), pp. 796-805, 2012.PDF icon Technical Report (20.85 MB)
B. Jähne, “50. Heidelberger Bildverarbeitungsforum --- Jubiläumsinterview mit Prof. Dr. Bernd Jähne”. 2012.
M. Hanselmann, Röder, J., Köthe, U., Renard, B. Y., Heeren, R. M. A., and Hamprecht, F. A., “Active Learning for Convenient Annotation and Classification of Secondary Ion Mass Spectrometry Images”, Analytical Chemistry, vol. 85 (1), pp. 147-155, 2012.PDF icon Technical Report (2.58 MB)
J. Röder, Kunzmann, K., Nadler, B., and Hamprecht, F. A., “Active Learning with Distributional Estimates”, UAI 2012. Proceedings, pp. 715-725, 2012.PDF icon Technical Report (267.67 KB)

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