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S. Trittler and Hamprecht, F. A., “Near-Optimum Sampling Design and an Efficient Algorithm for Single Tone Frequency Estimation”, Digital Signal Processing, vol. 19, pp. 628-639, 2009.PDF icon Technical Report (511.23 KB)
B. Goldlücke, Strekalovskiy, E., and Cremers, D., “The Natural Vectorial Total Variation which Arises from Geometric Measure Theory”, SIAM Journal on Imaging Sciences, vol. 5, pp. 537-563, 2012.
B. Goldlücke, Strekalovskiy, E., and Cremers, D., “The natural vectorial total variation which arises from geometric measure theory”, SIAM Journal on Imaging Sciences, 2012.
M. Heiler and Schnörr, C., “Natural Statistics for Natural Image Segmentation”, in Proc. IEEE Int. Conf. Computer Vision (ICCV 2003), Nice, France, 2003, pp. 1259-1266.
M. Heiler and Schnörr, C., “Natural Image Statistics for Natural Image Segmentation”, Int. J. Comp. Vision, vol. 63, pp. 5–19, 2005.
M
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.
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)
B. Jähne, Geißler, P., and Haußecker, H., Mustererkennung 1996, 18. DAGM-Symposium Heidelberg, 11.–13. September 1996. Springer, 1996.
C. Rother, “Multi-View Reconstruction and Camera Recovery using a Real or Virtual Reference Plane”. 2003.
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)
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. 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. Schiegg, “Multi-Target Tracking with Probabilistic Graphical Models”. University of Heidelberg, 2015.
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)
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)
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.
A. M. Andrew, “Multiple View Geometry in Computer Vision”, Kybernetes, vol. 30. pp. 1333–1341, 2001.
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. Wieler, “Multiple Instance Learning with Random Forests and Applications in Industrial Optical Inspection”. University of Heidelberg, 2014.
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.
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)
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. 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.
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.
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.
M. Gebhard, “Multidimensionale Segmentierung in Bildfolgen und Quantifizierung dynamischer Prozesse”. IWR, Fakultät für Physik und Astronomie, Univ.\ Heidelberg, 2004.
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.
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.
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.
T. Beier, “Multicut Algorithms for Neurite Segmentation”. Heidelberg University, 2018.
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.

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