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2015
A. Kirillov, Savchynskyy, B., Schlesinger, D., Vetrov, D., and Rother, C., “Inferring M-best diverse labelings in a single one”, in Proceedings of the IEEE International Conference on Computer Vision, 2015, vol. 2015 Inter, pp. 1814–1822.
A. Kirillov, Savchynskyy, B., Schlesinger, D., Vetrov, D., and Rother, C., “Inferring M-best diverse labelings in a single one”, in Proceedings of the IEEE International Conference on Computer Vision, 2015, vol. 2015 Inter, pp. 1814–1822.
K. Schelten, Nowozin, S., Jancsary, J., Rother, C., and Roth, S., “Interleaved regression tree field cascades for blind image deconvolution”, in Proceedings - 2015 IEEE Winter Conference on Applications of Computer Vision, WACV 2015, 2015, pp. 494–501.
B. Krolla, Diebold, M., and Stricker, D., “Light Field from Smartphone-Based Dual Video”, in Computer Vision - ECCV 2014 Workshops: Zurich, Switzerland, September 6-7 and 12, 2014, Proceedings, Part II, Cham: Springer International Publishing, 2015, pp. 600–610.
M. Diebold, Blum, O., Gutsche, M., Wanner, S., Garbe, C., Baker, H., and Jähne, B., “Light-field camera design for high-accuracy depth estimation”, in Videometrics, Range Imaging, and Applications XIII, 2015.
A. Kirillov, Schlesinger, D., Vetrov, D., Rother, C., and Savchynskyy, B., “M-best-diverse labelings for submodular energies and beyond”, in Advances in Neural Information Processing Systems, 2015, vol. 2015-Janua, pp. 613–621.
A. Kirillov, Schlesinger, D., Vetrov, D., Rother, C., and Savchynskyy, B., “M-best-diverse labelings for submodular energies and beyond”, in Advances in Neural Information Processing Systems, 2015, vol. 2015-Janua, pp. 613–621.
M. Schiegg, “Multi-Target Tracking with Probabilistic Graphical Models”. University of Heidelberg, 2015.
J. Stapf, “Novel learning-based techniques for dense fluid motion measurements”. IWR, Fakultät für Physik und Astronomie, Univ.\ Heidelberg, 2015.
S. Zheng, Prisacariu, V. Adrian, Averkiou, M., Cheng, M. Ming, Mitra, N. J., Shotton, J., Torr, P. H. S., and Rother, C., “Object proposals estimation in depth image using compact 3D shape manifolds”, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2015, vol. 9358, pp. 196–208.
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.
J. Kappes, Swoboda, P., Savchynskyy, B., Hazan, T., and Schnörr, C., “Probabilistic Correlation Clustering and Image Partitioning Using Perturbed Multicuts”, in Proc. SSVM, 2015.
J. Kappes, Swoboda, P., Savchynskyy, B., Hazan, T., and Schnörr, C., “Probabilistic Correlation Clustering and Image Partitioning Using Perturbed Multicuts”, in Proc. SSVM, 2015.
J. Kappes, Swoboda, P., Savchynskyy, B., Hazan, T., and Schnörr, C., “Probabilistic Correlation Clustering and Image Partitioning Using Perturbed Multicuts”, in Proc. SSVM, 2015.
J. Hendrik Kappes, Swoboda, P., Savchynskyy, B., Hazan, T., and Schnörr, C., “Probabilistic correlation clustering and image partitioning using perturbed Multicuts”, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2015, vol. 9087, pp. 231–242.
J. Hendrik Kappes, Swoboda, P., Savchynskyy, B., Hazan, T., and Schnörr, C., “Probabilistic correlation clustering and image partitioning using perturbed Multicuts”, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2015, vol. 9087, pp. 231–242.
J. Hendrik Kappes, Swoboda, P., Savchynskyy, B., Hazan, T., and Schnörr, C., “Probabilistic correlation clustering and image partitioning using perturbed Multicuts”, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2015, vol. 9087, pp. 231–242.
J. H. Kappes, Swoboda, P., Savchynskyy, B., Hazan, T., and Schnörr, C., “Probabilistic Correlation Clustering and Image Partitioning Using Perturbed Multicuts”, in Proc.~SSVM, 2015.PDF icon Technical Report (1.1 MB)
J. H. Kappes, Swoboda, P., Savchynskyy, B., Hazan, T., and Schnörr, C., “Probabilistic Correlation Clustering and Image Partitioning Using Perturbed Multicuts”, in Proc.~SSVM, 2015.PDF icon Technical Report (1.1 MB)
J. H. Kappes, Swoboda, P., Savchynskyy, B., Hazan, T., and Schnörr, C., “Probabilistic Correlation Clustering and Image Partitioning Using Perturbed Multicuts”, in Proc.~SSVM, 2015.PDF icon Technical Report (1.1 MB)
M. Schiegg, Heuer, B., Haubold, C., Wolf, S., Köthe, U., and Hamprecht, F. A., “Proof-reading Guidance in Cell Tracking by Sampling from Tracking-by-assignment Models”, in ISBI. Proceedings, 2015, pp. 394-398.PDF icon Technical Report (648.55 KB)
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, 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.PDF icon Technical Report (4.42 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.
E. - M. Didden, Thorarinsdottir, T. L., Lenkoski, A., and Schnörr, C., “Shape from Texture using Locally Scaled Point Processes”, Image Anal. Stereol., vol. 34, pp. 161-170, 2015.
B. Antic, Büchler, U., Wahl, A. - S., Schwab, M. E., and Ommer, B., “Spatiotemporal Parsing of Motor Kinematics for Assessing Stroke Recovery”, in Medical Image Computing and Computer-Assisted Intervention, 2015.PDF icon Article (2.24 MB)
C. Cali, Baghabra, J., Boges, D. J., Holst, G. R., Kreshuk, A., Hamprecht, F. A., Srinivasan, M., Lehväslaiho, H., and Magistretti, P. J., “Three-dimensional immersive virtual reality for studying cellular compartments in 3D models from EM preparations of neural tissues”, Journal of Comparative Neurology, vol. 524, pp. 23-38, 2015.
J. H. Kappes, Petra, S., Schnörr, C., and Zisler, M., “TomoGC: Binary Tomography by Constrained Graph Cuts”, in Proc.~GCPR, 2015.PDF icon Technical Report (2.46 MB)
J. H. Kappes, Petra, S., Schnörr, C., and Zisler, M., “TomoGC: Binary Tomography by Constrained Graph Cuts”, in Proc. GCPR, 2015.
J. P. Kauppi, Kandemir, M., Saarinen, V. M., Hirvenkari, L., Parkkonen, L., Klami, A., Hari, R., and Kaski, S., “Towards brain-activity-controlled information retrieval: Decoding image relevance from MEG signals”, NeuroImage, vol. 112, pp. 288-298, 2015.PDF icon Technical Report (2.39 MB)
D. Kiefhaber, Caulliez, G., Zappa, C. J., Schaper, J., and Jähne, B., “Water wave measurement from stereo images of specular reflections”, vol. 26, p. 115401, 2015.
2014
X. Lou, Schiegg, M., and Hamprecht, F. A., “Active Structured Learning for Cell Tracking: Algorithm, Framework and Usability”, IEEE Transactions on Medical Imaging, vol. 33 (4), pp. 849-860, 2014.PDF icon Technical Report (6.84 MB)
K. Ellen Krall, Schneider-Zapf, K., Reith, S., Kiefhaber, D., and Jähne, B., “Air-sea gas exchange under nature-like surfactant influence in the lab”, in 7th SOPRAN Annual Meeting, Bremen, Germany, 25-26 March 2014, 2014.
B. Güssefeld, Kondermann, D., Schwartz, C., and Klein, R., “Are reflectance field renderings appropriate for optical flow evaluation?”, in International Conference on Image Processing, ICIP 2014, 2014.

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