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Kappes, J H, Schmidt, S and Schnörr, C (2010). MRF Inference by k-Fan Decomposition and Tight Lagrangian Relaxation. European Conference on Computer Vision (ECCV). Springer Berlin / Heidelberg. 6313 735--747PDF icon Technical Report (1.49 MB)
Kappes, J H, Schmidt, S and Schnörr, C (2010). MRF Inference by k-Fan Decomposition and Tight Lagrangian Relaxation. European Conference on Computer Vision (ECCV). Springer Berlin / Heidelberg. 6313 735–747
Kappes, J H, Schmidt, S and Schnörr, C (2010). MRF Inference by k-Fan Decomposition and Tight Lagrangian Relaxation. European Conference on Computer Vision (ECCV). Springer. 6313 735--747
Geißler, P and Jähne, B (1997). A multi-camera system for in-shore measurements of bubble size distributions beneath breaking waves. Optical 3-D Measurement Techniques IV, Zurich, Sept. 29 - Oct. 2, 1997. Wichmann. 251--258
Balschbach, G, Klinke, J and Jähne, B (1998). Multichannel shape from shading techniques for moving specular surfaces. ECCV 1998. Springer, Berlin. 1407 170--184
Balschbach, G, Klinke, J and Jähne, B (1997). Multichannel shape from shading techniques for reconstruction of specular surfaces. Tagungsband Herbsttagung des Graduiertenkollegs "3D Bildanalyse und -synthese". H.-P. Seidel, B. Girod, H. Niemann (Hrsg.)
Beier, T (2018). Multicut Algorithms for Neurite Segmentation. Heidelberg University
Beier, T, 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 (2017). Multicut brings automated neurite segmentation closer to human performance. Nature Methods. 14 101-102. http://rdcu.be/oVDQ
Kappes, J Hendrik, Swoboda, P, Savchynskyy, B, Hazan, T and Schnörr, C (2016). Multicuts and Perturb & MAP for Probabilistic Graph Clustering. Journal of Mathematical Imaging and Vision. 56 221–237. http://arxiv.org/abs/1601.02088
Kappes, J H, Swoboda, P, Savchynskyy, B, Hazan, T and Schnörr, C (2016). Multicuts and Perturb & MAP for Probabilistic Graph Clustering. J. Math. Imag. Vision. 56 221–237
Jähne, B, Brocke, M, Eisele, H, Hader, S, Hamprecht, F A, Happold, W, Raisch, F and Restle, J (2002). Multidimensionale Bildverarbeitung in der Produktion. QZ. 47 1154--1159. http://www.qz-online.de/qz-zeitschrift/archiv/artikel/multidimensionale-bildverarbeitung-in-der-produktion-fuer-anspruchsvolle-338129.html
Gebhard, M (2004). Multidimensionale Segmentierung in Bildfolgen und Quantifizierung dynamischer Prozesse. IWR, Fakultät für Physik und Astronomie, Univ.\ Heidelberg. http://www.ub.uni-heidelberg.de/archiv/4392/
Lin, Z, Erz, M and Jähne, B (2010). Multi-frequency multi-sampling fluorescence lifetime imaging using a high-speed line-scan camera. Optics, Photonics, and Digital Technologies for Multimedia Applications, 12--15 April 2010, Brussels. 7723 77231S
Bruhn, A, Weickert, J, Kohlberger, T and Schnörr, C (2006). A Multigrid Platform for Real-Time Motion Computation with Discontinuity-Preserving Variational Methods. Int.~J.~Computer Vision. 70 257-277PDF icon Technical Report (447.65 KB)
Bruhn, A, Weickert, J, Kohlberger, T and Schnörr, C (2006). A Multigrid Platform for Real-Time Motion Computation with Discontinuity-Preserving Variational Methods. Int. J. Computer Vision. 70 257-277
Jähne, B, Herrmann, H, Jähne, B and Haußecker, H (1999). Multimedia architectures. Handbook of Computer Vision and Applications. Academic Press. 3: Systems and Applications 31--52
Urban, G, Bendszus, M, Hamprecht, F A and Kleesiek, J (2014). Multi-modal Brain Tumor Segmentation using Deep Convolutional NeuralNetworks. MICCAI BraTS (Brain Tumor Segmentation) Challenge. Proceedings, winningcontribution. 31-35
Menze, B H and Hamprecht, F A (2010). Multimodal Medical Image Analysis: from Visualization to Disease Modeling. Zeitschrift für Med. Physik. 1 1-2PDF icon Technical Report (481.58 KB)
Cremers, D, Sochen, N and Schnörr, C (2004). Multiphase Dynamic Labeling for Variational Recognition-Driven Image Segmentation. Computer Vision – ECCV 2004. Springer. 3024 74-86
Cremers, D, Sochen, N and Schnörr, C (2006). Multiphase Dynamic Labeling for Variational Recognition-Driven Image Segmentation. ijcv. 66 67-81
Wieler, M (2014). Multiple Instance Learning with Random Forests and Applications in Industrial Optical Inspection. University of Heidelberg
Straehle, C N, Kandemir, M, Köthe, U and Hamprecht, F A (2014). Multiple instance learning with response-optimized random forests. ICPR. Proceedings. 3768 - 3773PDF icon Technical Report (296.66 KB)
Andrew, A M (2001). Multiple View Geometry in Computer Vision. Kybernetes. 30 1333–1341
Jähne, B, Jähne, B, Haußecker, H and Geißler, P (1999). Multiresolutional signal representation. Handbook of Computer Vision and Applications. Academic Press. 2 67--90
Ommer, B and Malik, J (2009). Multi-scale Object Detection by Clustering Lines. Proceedings of the IEEE International Conference on Computer Vision. IEEE. 484--491PDF icon Technical Report (3.18 MB)
Walter, F C, Damrich, S and Hamprecht, F A (2021). MultiStar: Instance Segmentation of Overlapping Objects with Star-Convex Polygons. ISBI. 295-298PDF icon Technical Report (1.83 MB)
Schiegg, M (2015). Multi-Target Tracking with Probabilistic Graphical Models. University of Heidelberg
Kandemir, M, Klami, A, Gonen, M, Vetek, A and Kaski, S (2014). Multi-task and multi-view learning of user state. Neurocomputing. 139 97-106
Menze, B H, Petrich, W and Hamprecht, F A (2007). Multivariate feature selection and hierarchical classification for infrared spectroscopy: serum-based detection of bovine spongiform encephalopathy. Analytical and Bioanalytical Chemistry. 387 1801-1807PDF icon Technical Report (283.47 KB)
Hanselmann, M, Köthe, U, Renard, B Y, Kirchner, M, Heeren, R M A and Hamprecht, F A (2009). Multivariate Watershed Segmentation of Compositional Data. Proceedings of the 15th International Conference on Discrete Geometry for Computer Imagery (DGCI), in press. Springer. 5810 180-192PDF icon Technical Report (1.25 MB)
Rother, C (2003). Multi-View Reconstruction and Camera Recovery using a Real or Virtual Reference Plane. http://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=4&cad=rja&uact=8&ved=0CDUQFjAD&url=http%3A%2F%2Fwww.nada.kth.se%2Futbildning%2Fforsk.utb%2Favhandlingar%2Fdokt%2Frother.pdf&ei=AyX_VPKmIomeNqeOgpgL&usg=AFQjCNHCmc75P5EHYWLtBUaHtUAs4yOnJw&bvm=bv.
Jähne, B, Geißler, P and Haußecker, H (1996). Mustererkennung 1996, 18. Dagm-Symposium Heidelberg, 11.–13. September 1996. Springer
Wolf, S, Bailoni, A, Pape, C, Rahaman, N, Kreshuk, A, Köthe, U and Hamprecht, F A (2020). The Mutex Watershed and its Objective: Efficient, Parameter-Free Graph Partitioning. IEEE Transactions on Pattern Analysis and Machine Intelligence. 43 3724-3738PDF icon Technical Report (2.58 MB)
Wolf, S, Pape, C, Bailoni, A, Rahaman, N, Kreshuk, A, Köthe, U and Hamprecht, F A (2018). The Mutex Watershed: Efficient, Parameter-Free Image Partitioning. ECCV. Proceedings. Springer. 571-587
Wolf, S, Pape, C, Bailoni, A, Rahaman, N, Kreshuk, A, Köthe, U and Hamprecht, F A (2018). The Mutex Watershed: Efficient, Parameter-Free Image Partitioning. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 11208 LNCS 571–587. http://arxiv.org/abs/1904.12654

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