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Conference Paper

B. Jähne, Haußecker, H., Hering, F., Balschbach, G., Klinke, J., Lell, M., Schmund, D., Schultz, M., Schurr, U., Stitt, M., and Platt, U., “The role of active vision in exploring growth, transport, and exchange processes”, in Aktives Sehen in technischen und biologischen Systemen, Workshop der GI-Fachgruppe 1.0.4. Bildverstehen Hamburg, 3--4. December 1996, 1996, vol. 4, p. 194--202.

F. Hering, Merle, M., Wierzimok, D., and Jähne, B., “A robust technique for tracking particles over long image sequences”, in Proc. ISPRS Intercommission Workshop `From Pixels to Sequences', Zurich, March 22 - 24, 1995, In Int'l Arch. of Photog. and Rem. Sens., 1995, vol. XXX-5W1, p. 74--79.

B. Antic and Ommer, B., “Robust Multiple-Instance Learning with Superbags”, in Proceedings of the Aian Conference on Computer Vision (ACCV) (Oral), 2012, p. 242--255.

Technical Report (319.58 KB)

A. Vianello, Ackermann, J., Diebold, M., and Jähne, B., “Robust Hough transform based 3D reconstruction from circular light fields”, in Conference on Computer Vision and Pattern Recognition (CVPR), 2018.

M. Heiler and Schnörr, C., “Reverse-Convex Programming for Sparse Image Codes”, in Proc. Int. Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR'05), 2005, vol. 3757, pp. 600-616.

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.

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

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. 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.

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.

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

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)

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)

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. 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.

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.

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.

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.

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. 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)

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.

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.

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.

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

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

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.

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. 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.

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

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.

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)

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.

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.

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