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Conference Paper

B. Andres, Kappes, J. H., Beier, T., Köthe, U., and Hamprecht, F. A., “Probabilistic Image Segmentation with Closedness Constraints”, in Proceedings of ICCV, 2011.

C. Schellewald and Schnörr, C., “Probabilistic Subgraph Matching Based on Convex Relaxation”, in Proc. Int. Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR'05), 2005, vol. 3757, pp. 171-186.

F. E Sanmartin, Damrich, S., and Hamprecht, F. A., “Probabilistic Watershed: Sampling all spanning forests for seeded segmentation and semi-supervised learning”, in Advances in Neural Information Processing Systems, 2019.

C. Rother, Carlsson, S., and Tell, D., “Projective factorization of planes and cameras in multiple views”, in Proceedings - International Conference on Pattern Recognition, 2002, vol. 16, pp. 737–740.

M. Schiegg, Heuer, B., Haubold, C., Wolf, S., Köthe, U., and Hamprecht, F. A., “Proof-reading Guidance in Cell Tracking by Sampling from Tracking-by-assignment Models”, in ISBI. Proceedings, 2015, pp. 394-398.

Technical Report (648.55 KB)

P. Pletscher, Nowozin, S., Kohli, P., and Rother, C., “Putting MAP back on the map”, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2011, vol. 6835 LNCS, pp. 111–121.

P. Pletscher, Nowozin, S., Kohli, P., and Rother, C., “Putting MAP back on the map”, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2011, vol. 6835 LNCS, pp. 111–121.

A. H. M. Blom, Brassel, J. - O., von Brocke, M., and Mittler, M., “Quality classification and process control of micro-spot laser welding”, in Proceedings of the Ninth International FAIM Conference - Flexible Automation and Intelligent Manufacturing, Tilburg, 1999, p. 929--941.

B. Andres, Köthe, U., Bonea, A., Nadler, B., and Hamprecht, F. A., “Quantitative Assessment of Image Segmentation Quality by Random Walk Relaxation Times”, in Pattern Recognition. 31st DAGM Symposium, Jena, Germany, September 9-11, 2009. Proceedings, 2009, vol. 5748, pp. 502-511.

Technical Report (3.08 MB)

D. Wierzimok and Hering, F., “Quantitative imaging of transport in fluids with digital particle tracking velocimetry”, in Imaging in Transport Processes, 1993, p. 297--308.

J. L. Barron and Spies, H., “Quantitative regularized range flow”, in Vision Interface, 2000, p. 203--210.

M. Erz and Jähne, B., “Radiometric and spectrometric calibrations, and distance noise measurement of TOF cameras”, in 3rd Workshop on Dynamic 3-D Imaging, 2009, vol. 5742, p. 28--41.

A. Eigenstetter, Takami, M., and Ommer, B., “Randomized Max-Margin Compositions for Visual Recognition”, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2014, p. 3590--3597.

Technical Report (8.01 MB)

M. Schmidt, Jehle, M., and Jähne, B., “Range flow estimation based on photonic mixing device data”, in Proc.\ Dyn3D Workshop, Heidelberg, Sept. 11, 2007, 2007.

J. Weickert and Schnörr, C., “Räumlich–zeitliche Berechnung des optischen Flusses mit nichtlinearen flussabhängigen Glattheitstermen”, in Mustererkennung 1999, 1999, pp. 317–324.

R. Strzodka and Garbe, C. S., “Real-time motion estimation and visualization on graphics cards”, in Proceedings IEEE Visualization 2004, 2004, p. 545--552.

A. Bruhn, Weickert, J., Feddern, C., Kohlberger, T., and Schnörr, C., “Real-Time Optic Flow Computation with Variational Methods”, in Proc. Computer Analysis of Images and Patterns (CAIP'03), 2003, vol. 2756, pp. 222-229.

O. Hosseini Jafari and Yang, M. Ying, “Real-time RGB-D based template matching pedestrian detection”, in Proceedings - IEEE International Conference on Robotics and Automation, 2016, vol. 2016-June, pp. 5520–5527.

K. Wiehler, Grigat, R. –R., Heers, J., Schnörr, C., and Stiehl, H. –S., “Real–Time Adaptive Smoothing with a 1D Nonlinear Relaxation Network in Analogue VLSI Technology”, in Mustererkennung 1998, Heidelberg, 1998.

P. Yarlagadda, Monroy, A., Carque, B., and Ommer, B., “Recognition and Analysis of Objects in Medieval Images”, in Proceedins of the Aian Conference on Computer Vision, Workshop on e-Heritage, 2010, p. 296--305.

Technical Report (2.76 MB)

A. Monroy, Carque, B., and Ommer, B., “Reconstructing the Drawing Process of Reproductions from Medieval Images”, in Proceedings of the International Conference on Image Processing, 2011, p. 2974--2977.

Technical Report (2.43 MB)

P. Vincent Gehler, Rother, C., Kiefel, M., Zhang, L., and Schölkopf, B., “Recovering intrinsic images with a global sparsity prior on reflectance”, in Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011, NIPS 2011, 2011.

R. Nair, Fitzgibbon, A., Kondermann, D., and Rother, C., “Reflection modeling for passive stereo”, in Proceedings of the IEEE International Conference on Computer Vision, 2015, vol. 2015 Inter, pp. 2291–2299.

J. Esparza, Vepa, L., Helmle, M., and Jähne, B., “Registration of a multi-camera system with a 3D laser range finder”, in 9th Workshop Driver Assistance Systems (FAS2014), 26.-28.03.2014, Walting, 2014, p. 37--46.

J. Jancsary, Nowozin, S., Sharp, T., and Rother, C., “Regression Tree Fields An efficient, non-parametric approach to image labeling problems”, in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2012, pp. 2376–2383.

J. Jancsary, Nowozin, S., Sharp, T., and Rother, C., “Regression Tree Fields An efficient, non-parametric approach to image labeling problems”, in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2012, pp. 2376–2383.

H. Spies, Jähne, B., and Barron, J. L., “Regularised range flow”, in European Conference on Computer Vision (ECCV), 2000, vol. 2, p. 785--799.

J. C. Rubio and Ommer, B., “Regularizing Max-Margin Exemplars by Reconstruction and Generative Models”, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015, p. 4213--4221.

Technical Report (2.8 MB)

A. Bhowmik, Gumhold, S., Rother, C., and Brachmann, E., “Reinforced Feature Points: Optimizing Feature Detection and Description for a High-Level Task”, in CVPR 2020 (oral), 2020.

PDF (2.74 MB)

H. Reinecke, Fantana, N. L., Haußecker, H., and Jähne, B., “Rekonstruktion von Schreiberkurven”, in Mustererkennung 1997, 1997, p. 527--536.

N. von Schmude, Lothe, P., and Jähne, B., “Relative Pose Estimation from Straight Lines using Parallel Line Clustering and its Application to Monocular Visual Odometry”, in Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, 2016.

C. S. Garbe and Jähne, B., “Reliable estimates of the sea surface heat flux from image sequences”, in Proceedings of the 23th DAGM Symposium on Pattern Recognition, München, 2001, p. 194--201.

H. Zhang, Hamprecht, F. A., and Amann, A., “Report about VOCs Dataset's Analysis based on Random Forests”, in Proceedings of the HPC-Asia05, 2005, pp. 603-607.

Technical Report (232.13 KB)

C. Schnörr, “Repräsentation von Bilddaten mit einem konvexen Variationsansatz”, in Mustererkennung 1996, Berlin, Heidelberg, 1996, pp. 21–28.

B. Jähne, Haußecker, H., Platt, U., Schurr, U., and Stitt, M., “The research unit (Forschergruppe) Image Sequence Processing to Study Dynamical Processes”, in Proc.\ 3D Image Analysis and Synthesis'97, Erlangen (Germany), November 17--18, 1997, 1997, p. 107--114.

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