HCI Logo
Heidelberg University Logo
IWR Logo

Publications

Export 1965 results:
Author [ Title(Desc)] Type Year
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z 
M
J. H. Kappes, Schmidt, S., and Schnörr, C., “MRF Inference by k-Fan Decomposition and Tight Lagrangian Relaxation”, in European Conference on Computer Vision (ECCV), 2010, vol. 6313, p. 735--747.PDF icon Technical Report (1.49 MB)
J. H. Kappes, Schmidt, S., and Schnörr, C., “MRF Inference by k-Fan Decomposition and Tight Lagrangian Relaxation”, in European Conference on Computer Vision (ECCV), 2010, vol. 6313, pp. 735–747.
J. H. Kappes, Schmidt, S., and Schnörr, C., “MRF Inference by k-Fan Decomposition and Tight Lagrangian Relaxation”, in European Conference on Computer Vision (ECCV), 2010, vol. 6313, p. 735--747.
P. Geißler and Jähne, B., “A multi-camera system for in-shore measurements of bubble size distributions beneath breaking waves”, in Optical 3-D Measurement Techniques IV, Zurich, Sept. 29 - Oct. 2, 1997, 1997, p. 251--258.
G. Balschbach, Klinke, J., and Jähne, B., “Multichannel shape from shading techniques for moving specular surfaces”, in ECCV 1998, 1998, vol. 1407, p. 170--184.
G. Balschbach, Klinke, J., and Jähne, B., “Multichannel shape from shading techniques for reconstruction of specular surfaces”, in Tagungsband Herbsttagung des Graduiertenkollegs "3D Bildanalyse und -synthese", 1997.
T. Beier, “Multicut Algorithms for Neurite Segmentation”. Heidelberg University, 2018.
T. Beier, Pape, C., Rahaman, N., Prange, T., Berg, S., Bock, D., Cardona, A., Knott, G. W., Plaza, S. M., Scheffer, L. K., Köthe, U., Kreshuk, A., and Hamprecht, F. A., “Multicut brings automated neurite segmentation closer to human performance”, Nature Methods, vol. 14, no. 2, pp. 101-102, 2017.
J. Hendrik Kappes, Swoboda, P., Savchynskyy, B., Hazan, T., and Schnörr, C., “Multicuts and Perturb & MAP for Probabilistic Graph Clustering”, Journal of Mathematical Imaging and Vision, vol. 56, pp. 221–237, 2016.
J. H. Kappes, Swoboda, P., Savchynskyy, B., Hazan, T., and Schnörr, C., “Multicuts and Perturb & MAP for Probabilistic Graph Clustering”, J. Math. Imag. Vision, vol. 56, pp. 221–237, 2016.
B. Jähne, Brocke, M., Eisele, H., Hader, S., Hamprecht, F. A., Happold, W., Raisch, F., and Restle, J., “Multidimensionale Bildverarbeitung in der Produktion”, QZ, vol. 47, p. 1154--1159, 2002.
M. Gebhard, “Multidimensionale Segmentierung in Bildfolgen und Quantifizierung dynamischer Prozesse”. IWR, Fakultät für Physik und Astronomie, Univ.\ Heidelberg, 2004.
Z. Lin, Erz, M., and Jähne, B., “Multi-frequency multi-sampling fluorescence lifetime imaging using a high-speed line-scan camera”, in Optics, Photonics, and Digital Technologies for Multimedia Applications, 12--15 April 2010, Brussels, 2010, vol. 7723, p. 77231S.
A. Bruhn, Weickert, J., Kohlberger, T., and Schnörr, C., “A Multigrid Platform for Real-Time Motion Computation with Discontinuity-Preserving Variational Methods”, Int.~J.~Computer Vision, vol. 70, pp. 257-277, 2006.PDF icon Technical Report (447.65 KB)
A. Bruhn, Weickert, J., Kohlberger, T., and Schnörr, C., “A Multigrid Platform for Real-Time Motion Computation with Discontinuity-Preserving Variational Methods”, Int. J. Computer Vision, vol. 70, pp. 257-277, 2006.
B. Jähne, Herrmann, H., Jähne, B., and Haußecker, H., “Multimedia architectures”, Handbook of Computer Vision and Applications, vol. 3: Systems and Applications. Academic Press, p. 31--52, 1999.
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.
B. H. Menze and Hamprecht, F. A., “Multimodal Medical Image Analysis: from Visualization to Disease Modeling”, Zeitschrift für Med. Physik, vol. 1, pp. 1-2, 2010.PDF icon Technical Report (481.58 KB)
D. Cremers, Sochen, N., and Schnörr, C., “Multiphase Dynamic Labeling for Variational Recognition-Driven Image Segmentation”, in Computer Vision – ECCV 2004, 2004, vol. 3024, pp. 74-86.
D. Cremers, Sochen, N., and Schnörr, C., “Multiphase Dynamic Labeling for Variational Recognition-Driven Image Segmentation”, ijcv, vol. 66, pp. 67-81, 2006.
M. Wieler, “Multiple Instance Learning with Random Forests and Applications in Industrial Optical Inspection”. University of Heidelberg, 2014.
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)
A. M. Andrew, “Multiple View Geometry in Computer Vision”, Kybernetes, vol. 30. pp. 1333–1341, 2001.
B. Jähne, Jähne, B., Haußecker, H., and Geißler, P., “Multiresolutional signal representation”, Handbook of Computer Vision and Applications, vol. 2. Academic Press, p. 67--90, 1999.
B. Ommer and Malik, J., “Multi-scale Object Detection by Clustering Lines”, in Proceedings of the IEEE International Conference on Computer Vision, 2009, p. 484--491.PDF icon Technical Report (3.18 MB)
F. C. Walter, Damrich, S., and Hamprecht, F. A., “MultiStar: Instance Segmentation of Overlapping Objects with Star-Convex Polygons”, ISBI. pp. 295-298, 2021.PDF icon Technical Report (1.83 MB)
M. Schiegg, “Multi-Target Tracking with Probabilistic Graphical Models”. University of Heidelberg, 2015.
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.
B. H. Menze, Petrich, W., and Hamprecht, F. A., “Multivariate feature selection and hierarchical classification for infrared spectroscopy: serum-based detection of bovine spongiform encephalopathy”, Analytical and Bioanalytical Chemistry, vol. 387, pp. 1801-1807, 2007.PDF icon Technical Report (283.47 KB)
M. Hanselmann, Köthe, U., Renard, B. Y., Kirchner, M., Heeren, R. M. A., and Hamprecht, F. A., “Multivariate Watershed Segmentation of Compositional Data”, in Proceedings of the 15th International Conference on Discrete Geometry for Computer Imagery (DGCI), in press, 2009, vol. 5810, pp. 180-192.PDF icon Technical Report (1.25 MB)
C. Rother, “Multi-View Reconstruction and Camera Recovery using a Real or Virtual Reference Plane”. 2003.
B. Jähne, Geißler, P., and Haußecker, H., Mustererkennung 1996, 18. DAGM-Symposium Heidelberg, 11.–13. September 1996. Springer, 1996.
S. Wolf, Bailoni, A., Pape, C., Rahaman, N., Kreshuk, A., Köthe, U., and Hamprecht, F. A., “The Mutex Watershed and its Objective: Efficient, Parameter-Free Graph Partitioning”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 43, pp. 3724-3738, 2020.PDF icon Technical Report (2.58 MB)
S. Wolf, Pape, C., Bailoni, A., Rahaman, N., Kreshuk, A., Köthe, U., and Hamprecht, F. A., “The Mutex Watershed: Efficient, Parameter-Free Image Partitioning”, ECCV. Proceedings. Springer, pp. 571-587, 2018.
S. Wolf, Pape, C., Bailoni, A., Rahaman, N., Kreshuk, A., Köthe, U., and Hamprecht, F. A., “The Mutex Watershed: Efficient, Parameter-Free Image Partitioning”, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2018, vol. 11208 LNCS, pp. 571–587.

Pages