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Author Title Type [ Year(Desc)]
2013
H. Schäfer, Lenzen, F., and Garbe, C. S., “Depth and Intensity Based Edge Detection in Time-of-Flight Images”, in 3DV-Conference, 2013 International Conference on, 2013, pp. 111-118.PDF icon Technical Report (1.85 MB)
H. Schäfer, Lenzen, F., and Garbe, C. S., “Depth and Intensity Based Edge Detection in Time-of-Flight Images”, in 3D Imaging, Modeling, Processing, Visualization and Transmission (3DIMPVT), 2013 International Conference on, 2013, pp. 111-118.
M. Hornáček, Rhemann, C., Gelautz, M., and Rother, C., “Depth super resolution by rigid body self-similarity in 3D”, in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2013, pp. 1123–1130.
B. Jähne, “Der Standard EMVA 1288 zur Charakterisierung von Kameras und Bildsensoren: von 2D- zu 3D-Kameras”, in Photogrammetrie, Laserscanning, Optische 3D-Messtechnik, Beiträge der Oldenburger 3D-Tage 2013, 2013, p. 388--399.
P. Salomon Bauer, “Development of an imaging polarimeter for water wave slope measurements”, Institut für Umwelphysik, Univeristät Heidelberg, Germany, 2013.
J. Lellmann, Lellmann, B., Widmann, F., and Schnörr, C., “Discrete and Continuous Models for Partitioning Problems”, Int.~J.~Comp.~Visionz, vol. 104, pp. 241-269, 2013.PDF icon Technical Report (4.74 MB)
U. Schmidt, Rother, C., Nowozin, S., Jancsary, J., and Roth, S., “Discriminative Non-blind Deblurring”, 2013.
R. Haeusler, Nair, R., and Kondermann, D., “Ensemble Learning for Confidence Measures in Stereo Vision”, in CVPR 2013, in press, 2013, pp. 305-312.
M. Diebold and Goldlücke, B., “Epipolar Plane Image Refocusing for Improved Depth Estimation and Occlusion Handling.”, in VMV, 2013.
A. Hosni, Rhemann, C., Bleyer, M., Rother, C., and Gelautz, M., “Fast cost-volume filtering for visual correspondence and beyond”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, pp. 504–511, 2013.
K. Ellen Krall and Jähne, B., “First air-sea gas exchange laboratory study at hurricane wind speeds”, Ocean Sci. Discuss., vol. 10, p. 1971--1996, 2013.
B. Savchynskyy and Schmidt, S., “Getting Feasible Variable Estimates From Infeasible Ones: MRF Local Polytope Study”, in Workshop on Inference for Probabilistic Graphical Models at ICCV. Proceedings, 2013.
B. Savchynskyy, Kappes, J. H., Swoboda, P., and Schnörr, C., “Global MAP-Optimality by Shrinking the Combinatorial Search Area with Convex Relaxation”, in NIPS. Proceedings, 2013, pp. 1950-1958.
B. Savchynskyy, Kappes, J. H., Swoboda, P., and Schnörr, C., “Global MAP-Optimality by Shrinking the Combinatorial Search Area with Convex Relaxation”, in NIPS, 2013.PDF icon Technical Report (499.17 KB)
B. Savchynskyy, Kappes, J. Hendrik, Swoboda, P., and Schnörr, C., “Global MAP-Optimality by Shrinking the Combinatorial Search Area with Convex Relaxation”, in NIPS, 2013.
S. Wanner, Straehle, C. N., and Goldlücke, B., “Globally Consistent Multi-Label Assignment on the Ray Space of 4D Light Fields”, CVPR 2013. Proceedings, pp. 1011-1018, 2013.
S. Wanner, Straehle, C. N., and Goldlücke, B., “Globally consistent multi-label assignment on the ray space of 4D light fields”, in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013.
B. Schmitzer and Schnörr, C., “A Hierarchical Approach to Optimal Transport”, in Scale Space and Variational Methods (SSVM 2013), 2013, pp. 452-464.
V. Vineet, Rother, C., and Torr, P. H. S., “Higher order priors for joint intrinsic image, objects, and attributes estimation”, in Advances in Neural Information Processing Systems, 2013.
J. H. Kappes, Speth, M., Reinelt, G., and Schnörr, C., “Higher-order Segmentation via Multicuts”, ArXiv e-prints. 2013.PDF icon Technical Report (1.07 MB)
J. Hendrik Kappes, Speth, M., Reinelt, G., and Schnörr, C., “Higher-order Segmentation via Multicuts”, ArXiv e-prints. 2013.
J. Horn, “Hochaufgelöste optische Wellenhöhenmessung am Aeolotron mit Laser-induzierter Fluoreszenz”, Institut für Umwelphysik, Univeristät Heidelberg, Germany, 2013.
P. Fahle, “Hochauflösende Messung der raumzeitlichen Variation der Neigung winderzeugter Wasserwellen”, Institut für Umwelphysik, Univeristät Heidelberg, Germany, 2013.
A. Donath and Kondermann, D., “How Good is Crowdsourcing for Optical Flow Ground Truth Generation?”, submitted to CVPR, 2013.
M. Geese, “Image Sensor Nonuniformity Correction by a Scene-Based Maximum Likelihood Approach”. IWR, Fakultät für Physik und Astronomie, Univ.\ Heidelberg, 2013.
M. Geese, “Image Sensor Nonuniformity Correction by a Scene-Based Maximum Likelihood Approach”, vol. Dissertation. IWR, Univ. Heidelberg, 2013.
F. Friedl, “Investigating the Transfer of Oxygen at the Wavy Air-Water Interface under Wind-Induced Turbulence”, vol. Dissertation. Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg, 2013.
L. Fiaschi, Grosser, K. - H., Afonso, B., Zlatic, M., and Hamprecht, F. A., “Keeping Count: Leveraging Temporal Context to Count Heavily Overlapping Objects”, ISBI 2013.Proceedings, pp. 656-659, 2013.PDF icon Technical Report (711.68 KB)
C. N. Straehle, Peter, S., Köthe, U., and Hamprecht, F. A., “K-smallest Spanning Tree Segmentations”, in German Conference on Pattern Recognition (DAGM/GCPR). Proceedings, 2013, pp. 375-384.PDF icon Technical Report (1.18 MB)
K. E. Krall, “Laboratory Investigations of Air-Sea Gas Transfer under a Wide Range of Water Surface Conditions”, vol. Dissertation. Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg, 2013.
R. Walecki, “Large-Scale Automatic Reconstruction of Myelianated Axons and Detection of the Nodes of Ranvier”, University of Heidelberg, 2013.
L. Fiaschi, “Learning Based Biological Image Analysis”. University of Heidelberg, 2013.
J. Jancsary, Nowozin, S., and Rother, C., “Learning convex QP relaxations for structured prediction”, in 30th International Conference on Machine Learning, ICML 2013, 2013, pp. 1952–1960.
F. Diego and Hamprecht, F. A., “Learning Multi-Level Sparse Representation”, in NIPS. Proceedings, 2013.PDF icon Technical Report (2.79 MB)
F. Diego and Hamprecht, F. A., “Learning Multi-Level Sparse Representation for Identifying Neuronal Activity”, in Signal Processing with Adaptive Sparse Structured Representations Workshop (SPARS). Book of Abstracts., 2013.PDF icon Technical Report (1.05 MB)

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