HCI Logo
Heidelberg University Logo
IWR Logo

Publications

Export 1965 results:
Author Title [ Type(Desc)] Year
Filters: Filter is   [Clear All Filters]
Conference Paper
T. Hodaň, Michel, F., Brachmann, E., Kehl, W., Buch, A. Glent, Kraft, D., Drost, B., Vidal, J., Ihrke, S., Zabulis, X., Sahin, C., Manhardt, F., Tombari, F., Kim, T. Kyun, Matas, J., and Rother, C., “BOP: Benchmark for 6D object pose estimation”, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2018, vol. 11214 LNCS, pp. 19–35.
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.
S. Petra, Schnörr, C., Becker, F., and Lenzen, F., “B-SMART: Bregman-Based First-Order Algorithms for Non-Negative Compressed Sensing Problems”, in Proceedings of the 4th International Conference on Scale Space and Variational Methods in Computer Vision (SSVM) 2013, 2013, vol. 7893, pp. 110-124.PDF icon Technical Report (1.15 MB)
S. Petra, Schnörr, C., Becker, F., and Lenzen, F., “B-SMART: Bregman-Based First-Order Algorithms for Non-Negative Compressed Sensing Problems”, in Proceedings of the 4th International Conference on Scale Space and Variational Methods in Computer Vision SSVM, 2013, pp. 110-124.
J. Heikkonen, Koikkalainen, P., and Schnörr, C., “Building Trajectories via Selforganization from Spatiotemporal Features”, in 12th Int. Conf. on Pattern Recognition, Jerusalem, Israel, 1994.
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)
B. Jähne and Schultz, H., “Calibration and accuracy of optical slope measurements for short wind waves”, in Optics of the Air-Sea Interface: Theory and Measurements, 1992, vol. 1749, p. 222--233.
S. Karthik Mustikovela, Yang, M. Ying, and Rother, C., “Can ground truth label propagation from video help semantic segmentation?”, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2016, vol. 9915 LNCS, pp. 804–820.
C. M. Zechmann, Kelm, B. Michael, Zamecnik, P., Ikinger, U., Waldherr, R., Röll, S., Delorme, S., Hamprecht, F. A., and Bachert, P., “Can man still beat the machine? Automated vs. manual pattern recognition of 3D MRSI data of prostate cancer patients”, in Proceedings of the 16th ISMRM, 2006.PDF icon Technical Report (664.38 KB)
L. Maier-Hein, Mersmann, S., Kondermann, D., Bodenstedt, S., Sanchez, A., Stock, C., Kenngott, H., Eisenmann, M., and Speidel, S., “Can masses of non-experts train highly accurate image classifiers? A crowdsourcing approach to instrument segmentation in laparoscopic images”, in MICCAI, 2014.
C. N. Straehle, Köthe, U., Knott, G. W., and Hamprecht, F. A., “Carving: Scalable Interactive Segmentation of Neural Volume Electron Microscopy Images”, in MICCAI 2011, Proceedings., 2011, vol. 6891, pp. 653-660.PDF icon Technical Report (1.69 MB)
J. Welbl, “Casting Random Forests as Artificial Neural Networks (and Profiting from It)”, in GCPR. Proceedings, 2014, pp. 765-771.PDF icon Technical Report (376.24 KB)
C. Zhang, Yarkony, J., and Hamprecht, F. A., “Cell detection and segmentation using correlation clustering”, in MICCAI. Proceedings, 2014, pp. 9-16.PDF icon Technical Report (8.06 MB)
R. Mackowiak, Lenz, P., Ghori, O., Diego, F., Lange, O., and Rother, C., “CEREALS - Cost-Effective REgion-based Active Learning for Semantic Segmentation”, in British Machine Vision Conference 2018, BMVC 2018, 2019.
D. Schmund, Münsterer, T., Lauer, H., Jähne, B., and Jähne, B., “The circular wind wave facilities at the University of Heidelberg”, in Air-Water Gas Transfer - Selected papers from the Third International Symposium on Air-Water Gas Transfer, 1995, p. 505--516.
B. Michael Kelm, Menze, B. H., Neff, T., Zechmann, C. M., and Hamprecht, F. A., “CLARET: a tool for fully automated evaluation of MRSI with pattern recognition methods.”, in Bildverarbeitung für die Medizin 2006 - Algorithmen, Systeme, Anwendungen, 2006, pp. 51-55.PDF icon Technical Report (275.25 KB)
J. Heers, Schnörr, C., and Stiehl, H. S., “A class of parallel algorithms for nonlinear variational image segmentation”, in Proc. Noblesse Workshop on Non–Linear Model Based Image Analysis (NMBIA'98), Glasgow, Scotland, 1998.
B. H. Menze, Wormit, M., Bachert, P., Lichy, M. P., Schlemmer, H. - P., and Hamprecht, F. A., “Classification of in vivo magnetic resonance spectra”, in Classification in ubiquitous challenge: Proceedings of the GfKl 2004, 2004, pp. 362-369.PDF icon Technical Report (240.1 KB)
B. H. Menze and Ur, J. A., “Classification of multispectral ASTER imagery in the archaeological survey for settlement sites of the Near East”, in Proc 10th International Symposium on Physical Measurements and Signature in Remote Sensing (ISPMRS 07), Davos, Switzerland, 2007.PDF icon Technical Report (920.71 KB)
F. O. Kaster, Kelm, B. Michael, Zechmann, C. M., Weber, M. - A., Hamprecht, F. A., and Nix, O., “Classification of Spectroscopic Images in the DIROlab Environment”, in World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany, 2009, vol. 25/V, p. 252--255.PDF icon Technical Report (145.73 KB)
M. Bautista, Sanakoyeu, A., Sutter, E., and Ommer, B., “CliqueCNN: Deep Unsupervised Exemplar Learning”, in Proceedings of the Conference on Advances in Neural Information Processing Systems (NIPS), Barcelona, 2016.PDF icon Article (5.79 MB)
S. R. Long and Klinke, J., “A closer look at short waves generated by wave interactions with adverse currents”, in Gas Transfer at Water Surfaces, 2002, vol. 127, p. 121--128.
A. Kannan, Winn, J., and Rother, C., “Clustering appearance and shape by learning jigsaws”, in Advances in Neural Information Processing Systems, 2007, pp. 657–664.
A. Kannan, Winn, J., and Rother, C., “Clustering appearance and shape by learning jigsaws”, in Advances in Neural Information Processing Systems, 2007, pp. 657–664.
M. Geese, Ruhnau, P., and Jähne, B., “CNN based dark signal non-uniformity estimation”, in Cellular Nanoscale Networks and Their Applications (CNNA), 2012 13th International Workshop on, 2012, p. 1--6.
S. Waas and Jähne, B., “Combined height/slope/curvature measurements of short ocean wind waves”, in Proc.\ The Air-Sea Interface, Radio and Acoustic Sensing, Turbulence and Wave Dynamics, Marseille, 24--30. June 1993, 1996, p. 383--388.
S. Waas and Jähne, B., “Combined slope-height measurements of short wind waves: first results from field and laboratory measurements”, in Optics of the Air-Sea Interface: Theory and Measurements, 1992, vol. 1749, p. 295--306.
R. Rocholz, Wanner, S., Schimpf, U., and Jähne, B., “Combined visualization of wind waves and water surface temperature”, in Gas Transfer at Water Surfaces 2010, 2011, p. 496--506.
F. Hering, Wierzimok, D., Melville, W. K., and Jähne, B., “Combined wave and flow field visualization for investigation of short-wave/long-wave interaction”, in Proc.\ The Air-Sea Interface, Radio and Acoustic Sensing, Turbulence and Wave Dynamics, Marseille, 24--30. June 1993, 1996, p. 133--138.
B. Michael Kelm, Pal, C., and McCallum, A., “Combining Generative and Discriminative Methods for Pixel Classification with Multi-Conditional Learning.”, in ICPR 2006, 2006, vol. 2, pp. 828-832.PDF icon Technical Report (114.99 KB)
A. Bruhn, Weickert, J., and Schnörr, C., “Combining the Advantages of Local and Global Optic Flow Methods”, in Pattern Recognition, Proc. 24th DAGM Symposium, Zürich, Switzerland, 2002, vol. 2449, pp. 454–462.
B. Jähne, “A comparative analytical study of low-level motion estimators in space-time images”, in Proc. 16. DAGM-Symposium Mustererkennung, 1994.
C. Kräuter, Richter, K. E., Jähne, B., Mesarchaki, E., and Williams, J., “A comparative lab study of tansfer velocities of volatile tracers with widely varying solubilities”, in DPG Frühjahrstagung Dresden, Fachverband Umweltphysik, 2011.

Pages