Publications

Cremers, D and Schnörr, C (2002). Motion Competition: Variational Integration of Motion Segmentation and Shape Regularization. Pattern Recognition, Proc. 24th DAGM Symposium. Springer, Zürich, Switzerland. 2449 472–480

Uttenweiler, D, Veigel, C, Steubing, R, Götz, C, Mann, S, Haußecker, H, Jähne, B and Fink, R H A (2000). Motion determination in actin filament fluorescence images with a spatio-temporal orientation analysis method.. Biophys J. Institut für Physiologie und Pathophysiologie, Ruprecht-Karls-Universität Heidelberg, 69120 Heidelberg, Germany. uttenweiler@urz.uni-heidelberg.de. 78 2709--2715

Jähne, (1990). Motion determination in space-time images. Proc. Computer Vision -- ECCV 90, Lecture Notes in Computer Science 427. 161--173

Jähne, (1989). Motion determination in space-time images. Image Processing III, SPIE Proceeding 1135, international congress on optical science and engineering, Paris, 24-28 April 1989. 147--152

Kondermann, D, Kondermann, C, Berthe, A, Kertzscher, U and Garbe, C S (2008). Motion Estimation Based on a Temporal Model of Fluid Flows. 13th International Symposium on Flow Visualization. 1-10

Schnörr, C and Peckar, W (1995). Motion-Based Identification of Deformable Templates. Proc. 6th Int. Conf. on Computer Analysis of Images and Patterns (CAIP '95). Springer Verlag, Prague, Czech Republic. 970 122-129

Becker, M, Baron, M, Kondermann, D, Bussler, M and Helzle, V (2013). Movie Dimensionalization Via Sparse User Annotations. submitted to 3DTV-Con

Kondermann, D and Becker, M (2013). Movie Dimensionalization Via Sparse User Annotations. submitted to ICCV

Tourani, S, Shekhovtsov, A, Rother, C and Savchynskyy, B (2018). MPLP++: Fast, Parallel Dual Block-Coordinate Ascent for Dense Graphical Models. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 11208 LNCS 264–281

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

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. 70 257-277

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 1-2

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 - 3773

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--491

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, in press

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