HCI Logo
Heidelberg University Logo
IWR Logo

Publications

Export 796 results:
Author Title Type [ Year(Asc)]
Filters: First Letter Of Last Name is K  [Clear All Filters]
2014
M. Kandemir, Zhang, C., and Hamprecht, F. A., “Empowering multiple instance histopathology cancer diagnosis by cell graphs”, in MICCAI. Proceedings, 2014, vol. 8674, pp. 228-235.PDF icon Technical Report (1.76 MB)
M. Kandemir, Rubio, J. C., Schmidt, U., Wojek, C., Welbl, J., Ommer, B., and Hamprecht, F. A., “Event Detection by Feature Unpredictability in Phase-Contrast Videos of Cell Cultures”, in Medical Image Computing and Computer-Assisted Intervention, 2014, p. 154--161.PDF icon Technical Report (2 MB)
M. Kandemir, Rubio, J. C., Schmidt, U., Welbl, J., Ommer, B., and Hamprecht, F. A., “Event Detection by Feature Unpredictability in Phase-Contrast Videos of Cell Cultures”, in MICCAI. Proceedings, 2014, pp. 154-161.PDF icon Paper (2 MB)
M. Knorr, Esparza, J., Niehsen, W., and Stiller, C., “Extrinsic calibration of a fisheye multi-camera setup using overlapping fields of view”, in Intelligent Vehicles Symposium Proceedings, 2014, p. 1276--1281.
K. Ellen Krall and Jähne, B., “First laboratory study of air-sea gas exchange at hurricane wind speeds”, Ocean Sci., vol. 10, p. 257--265, 2014.
C. Kräuter, Trofimova, D., Kiefhaber, D., Krah, N., and Jähne, B., “High resolution 2-D fluorescence imaging of the mass boundary layer thickness at free water surfaces”, J. Europ. Opt. Soc. Rap. Public., vol. 9, p. 14016, 2014.
C. Kräuter, Trofimova, D., Kiefhaber, D., Krah, N., and Jähne, B., “High resolution 2-D fluorescence imaging of the mass boundary layer thickness at free water surfaces”, J. Europ. Opt. Soc. Rap. Public., vol. 9, p. 14016, 2014.
C. Kräuter, Trofimova, D., Kiefhaber, D., Krah, N., and Jähne, B., “High resolution 2-D fluorescence imaging of the mass boundary layer thickness at free water surfaces”, J. Europ. Opt. Soc. Rap. Public., vol. 9, p. 14016, 2014.
M. Hornáček, Besse, F., Kautz, J., Fitzgibbon, A., and Rother, C., “Highly overparameterized optical flow using PatchMatch belief propagation”, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2014, vol. 8691 LNCS, pp. 220–234.
C. Kräuter, Trofimova, D., Nagel, L., and Jähne, B., “High-resolution 2-D fluorescence imaging of gas transfer at a free water surface”, in Ocean Science Meeting, 23--28. 02. 2014, Honolulu Hawaii, 2014.
D. Kiefhaber, Reith, S., Rocholz, R., and Jähne, B., “High-speed imaging of short wind waves by shape from refraction”, J. Europ. Opt. Soc. Rap. Public., vol. 9, p. 14015, 2014.
J. Kleesiek, Biller, A., Urban, G., Köthe, U., Bendszus, M., and Hamprecht, F. A., “ilastik for Multi-modal Brain Tumor Segmentation”, in MICCAI BraTS (Brain Tumor Segmentation) Challenge. Proceedings, 3rdplace, 2014, pp. 12-17.PDF icon Technical Report (405.91 KB)
J. Kleesiek, Biller, A., Urban, G., Köthe, U., Bendszus, M., and Hamprecht, F. A., “ilastik for Multi-modal Brain Tumor Segmentation”, in MICCAI BraTS (Brain Tumor Segmentation) Challenge. Proceedings, 3rdplace, 2014, pp. 12-17.PDF icon Technical Report (405.91 KB)
M. Kandemir and Hamprecht, F. A., “Instance Label Prediction by Dirichlet Process Multiple Instance Learning”, in UAI. Proceedings, 2014.PDF icon Technical Report (4.26 MB)
T. Kröger, “Learning-based Segmentation for Connectomics”. University of Heidelberg, 2014.
J. H. Kappes, Beier, T., and Schnörr, C., “MAP-Inference on Large Scale Higher-Order Discrete Graphical Models by Fusion Moves”, in Computer Vision - {ECCV} 2014 Workshops - Zurich, Switzerland, September 6-7 and 12, 2014, Proceedings, Part {II}, 2014.PDF icon Technical Report (557.49 KB)
J. Hendrik Kappes, Beier, T., and Schnörr, C., “MAP-Inference on Large Scale Higher-Order Discrete Graphical Models by Fusion Moves”, in International Workshop on Graphical Models in Computer Vision, 2014.
D. Kiefhaber, Zappa, C. J., and Jähne, B., “Measurement of wind waves statistics from specular reflections”, in Ocean Science Meeting, 23--28. 02. 2014, Honolulu Hawaii, 2014.
E. Mesarchaki, Kräuter, C., Krall, K. Ellen, Bopp, M., Helleis, F., Williams, J., and Jähne, B., “Measuring air-sea gas exchange velocities in a large scale annular wind-wave tank”, Ocean Sci. Discuss., vol. 11, p. 1643--1689, 2014.
E. Mesarchaki, Kräuter, C., Krall, K. Ellen, Bopp, M., Helleis, F., Williams, J., and Jähne, B., “Measuring air-sea gas exchange velocities in a large scale annular wind-wave tank”, Ocean Sci. Discuss., vol. 11, p. 1643--1689, 2014.
G. Urban, Bendszus, M., Hamprecht, F. A., and Kleesiek, J., “Multi-modal Brain Tumor Segmentation using Deep Convolutional NeuralNetworks”, in MICCAI BraTS (Brain Tumor Segmentation) Challenge. Proceedings, winningcontribution, 2014, pp. 31-35.
C. N. Straehle, Kandemir, M., Köthe, U., and Hamprecht, F. A., “Multiple instance learning with response-optimized random forests”, in ICPR. Proceedings, 2014, pp. 3768 - 3773.PDF icon Technical Report (296.66 KB)
C. N. Straehle, Kandemir, M., Köthe, U., and Hamprecht, F. A., “Multiple instance learning with response-optimized random forests”, in ICPR. Proceedings, 2014, pp. 3768 - 3773.PDF icon Technical Report (296.66 KB)
M. Kandemir, Klami, A., Gonen, M., Vetek, A., and Kaski, S., “Multi-task and multi-view learning of user state”, Neurocomputing, vol. 139, pp. 97-106, 2014.
M. Kandemir, Klami, A., Gonen, M., Vetek, A., and Kaski, S., “Multi-task and multi-view learning of user state”, Neurocomputing, vol. 139, pp. 97-106, 2014.
M. Kandemir, Klami, A., Gonen, M., Vetek, A., and Kaski, S., “Multi-task and multi-view learning of user state”, Neurocomputing, vol. 139, pp. 97-106, 2014.
P. Márquez-Neila, Kohli, P., Rother, C., and Baumela, L., “Non-parametric higher-order random fields for image segmentation”, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2014, vol. 8694 LNCS, pp. 269–284.
D. Kiefhaber, “Optical Measurement of Short Wind Waves --- from the Laboratory to the Field”, vol. Dissertation. Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg, 2014.
F. Jug, Pietzsch, T., Kainmüller, D., Funke, J., Kaiser, M., van Nimwegen, E., Rother, C., and Myers, G., “Optimal joint segmentation and tracking of escherichia coli in the mother machine”, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 8677, pp. 25–36, 2014.
F. Jug, Pietzsch, T., Kainmüller, D., Funke, J., Kaiser, M., van Nimwegen, E., Rother, C., and Myers, G., “Optimal joint segmentation and tracking of escherichia coli in the mother machine”, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 8677, pp. 25–36, 2014.
P. Swoboda, Savchynskyy, B., Kappes, J. H., and Schnörr, C., “Partial Optimality by Pruning for MAP-inference with General Graphical Models”, in IEEE Conference on Computer Vision and Pattern Recognition 2014, 2014.
P. Swoboda, Savchynskyy, B., Kappes, J. H., and Schnörr, C., “Partial Optimality by Pruning for MAP-inference with General Graphical Models”, in IEEE Conference on Computer Vision and Pattern Recognition 2014, 2014.PDF icon Technical Report (703.34 KB)
P. Swoboda, Savchynskyy, B., Kappes, J. H., and Schnörr, C., “Partial Optimality by Pruning for MAP-inference with General GraphicalModels”, CVPR. Proceedings. pp. 1170-1177, 2014.
F. Besse, Rother, C., Fitzgibbon, A., and Kautz, J., “PMBP: PatchMatch Belief Propagation for correspondence field estimation”, International Journal of Computer Vision, vol. 110, pp. 2–13, 2014.
F. Besse, Rother, C., Fitzgibbon, A., and Kautz, J., “PMBP: PatchMatch Belief Propagation for correspondence field estimation”, International Journal of Computer Vision, vol. 110, pp. 2–13, 2014.

Pages