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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., 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., 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.
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.
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.
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, 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, 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 (3), pp. 269-297, 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. 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)
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)
A. Kreshuk, “Automated Analysis of Biomedical Data from Low to High Resolution”. University of Heidelberg, 2012.
C. M. Zechmann, Menze, B. H., Kelm, B. Michael, Zamecnik, P., Ikinger, U., Waldherr, R., Delorme, S., Hamprecht, F. A., and Bachert, P., “Automated vs. manual pattern recognition of 3D 1H MRSI data of patients with prostate cancer”, Academic Radiology, vol. 19, 6, pp. 675-684, 2012.
J. H. Kappes, Savchynskyy, B., and Schnörr, C., “A Bundle Approach To Efficient MAP-Inference by Lagrangian Relaxation”, in CVPR. Proceedings, 2012, pp. 1688-1695.
J. H. Kappes, Savchynskyy, B., and Schnörr, C., “A Bundle Approach To Efficient MAP-Inference by Lagrangian Relaxation”, in CVPR, 2012.PDF icon Technical Report (430.63 KB)
A. Shekhovtsov, Kohli, P., and Rother, C., “Curvature prior for MRF-based segmentation and shape inpainting”, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2012, vol. 7476 LNCS, pp. 41–51.
A. Shekhovtsov, Kohli, P., and Rother, C., “Curvature prior for MRF-based segmentation and shape inpainting”, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2012, vol. 7476 LNCS, pp. 41–51.
A. Shekhovtsov, Kohli, P., and Rother, C., “Curvature prior for MRF-based segmentation and shape inpainting”, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2012, vol. 7476 LNCS, pp. 41–51.
B. X. Kausler, Schiegg, M., Andres, B., Lindner, M., Köthe, U., Leitte, H., Wittbrodt, J., Hufnagel, L., and Hamprecht, F. A., “A Discrete Chain Graph Model for 3d+t Cell Tracking with High Misdetection Robustness”, in ECCV 2012. Proceedings, 2012, vol. 7574, pp. 144-157.PDF icon Technical Report (809.07 KB)
B. X. Kausler, Schiegg, M., Andres, B., Lindner, M., Köthe, U., Leitte, H., Wittbrodt, J., Hufnagel, L., and Hamprecht, F. A., “A Discrete Chain Graph Model for 3d+t Cell Tracking with High Misdetection Robustness”, in ECCV 2012. Proceedings, 2012, vol. 7574, pp. 144-157.PDF icon Technical Report (809.07 KB)
B. Savchynskyy, Schmidt, S., Kappes, J. H., and Schnörr, C., “Efficient MRF Energy Minimization via Adaptive Diminishing Smoothing”, UAI. Proceedings, pp. 746-755, 2012.
B. Savchynskyy, Schmidt, S., Kappes, J. H., and Schnörr, C., “Efficient MRF Energy Minimization via Adaptive Diminishing Smoothing”, in UAI 2012, 2012.PDF icon Technical Report (529 KB)
K. Ellen Krall, “Gas Exchange Test Campaign at the Kyoto High Speed Wind-Wave Tank 2011”, Institut für Umweltphysik, Universität Heidelberg, 2012.
B. Andres, Kröger, T., Briggmann, K. L., Denk, W., Norogod, N., Knott, G. W., Köthe, U., and Hamprecht, F. A., “Globally Optimal Closed-Surface Segmentation for Connectomics”, in ECCV 2012. Proceedings, Part 3, 2012, pp. 778-791.PDF icon Technical Report (2.72 MB)
B. Andres, Kröger, T., Briggmann, K. L., Denk, W., Norogod, N., Knott, G. W., Köthe, U., and Hamprecht, F. A., “Globally Optimal Closed-Surface Segmentation for Connectomics”, in ECCV 2012. Proceedings, Part 3, 2012, pp. 778-791.PDF icon Technical Report (2.72 MB)
B. Andres, Kröger, T., Briggmann, K. L., Denk, W., Norogod, N., Knott, G. W., Köthe, U., and Hamprecht, F. A., “Globally Optimal Closed-Surface Segmentation for Connectomics”, in ECCV 2012. Proceedings, Part 3, 2012, pp. 778-791.PDF icon Technical Report (2.72 MB)
R. Nair, Meister, S., Lambers, M., Balda, M., Hofmann, H., Kolb, A., Kondermann, D., and Jähne, B., “Ground truth for evaluating time of flight imaging”, Time-of-Flight and Depth Imaging. Sensors, Algorithms, and Applications, vol. 8200. Springer, p. 52--74, 2012.

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