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2014
M. Bopp, “Luft- und wasserseitige Strömungsverhältnisse im ringförmigen Heidelberger Wind-Wellen-Kanal (Aeolotron)”, Institut für Umweltphysik, Universität Heidelberg, Germany, 2014.
2013
K. E. Krall, “Laboratory Investigations of Air-Sea Gas Transfer under a Wide Range of Water Surface Conditions”, vol. Dissertation. Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg, 2013.
R. Walecki, “Large-Scale Automatic Reconstruction of Myelianated Axons and Detection of the Nodes of Ranvier”, University of Heidelberg, 2013.
L. Fiaschi, “Learning Based Biological Image Analysis”. University of Heidelberg, 2013.
J. Jancsary, Nowozin, S., and Rother, C., “Learning convex QP relaxations for structured prediction”, in 30th International Conference on Machine Learning, ICML 2013, 2013, pp. 1952–1960.
F. Diego and Hamprecht, F. A., “Learning Multi-Level Sparse Representation”, in NIPS. Proceedings, 2013.PDF icon Technical Report (2.79 MB)
F. Diego and Hamprecht, F. A., “Learning Multi-Level Sparse Representation for Identifying Neuronal Activity”, in Signal Processing with Adaptive Sparse Structured Representations Workshop (SPARS). Book of Abstracts., 2013.PDF icon Technical Report (1.05 MB)
T. Kröger, Mikula, S., Denk, W., Köthe, U., and Hamprecht, F. A., “Learning to Segment Neurons with Non-local Quality Measures”, in MICCAI 2013. Proceedings, part II, 2013, vol. 8150, pp. 419-427.PDF icon Technical Report (2.87 MB)
B. Antic, Milbich, T., and Ommer, B., “Less is More: Video Trimming for Action Recognition”, in Proceedings of the IEEE International Conference on Computer Vision, Workshop on Understanding Human Activities: Context and Interaction, 2013, p. 515--521.PDF icon Technical Report (984.89 KB)
2012
B. Andres, Kappes, J. H., Beier, T., Köthe, U., and Hamprecht, F. A., “The Lazy Flipper: Efficient Depth-limited Exhaustive Search in Discrete Graphical Models”, in ECCV 2012, 2012.PDF icon Technical Report (532.64 KB)
B. Andres, Kappes, J. Hendrik, Beier, T., Köthe, U., and Hamprecht, F. A., “The Lazy Flipper: Efficient Depth-limited Exhaustive Search in Discrete Graphical Models”, in ECCV 2012, 2012.
B. Andres, Kappes, J. H., Beier, T., Köthe, U., and Hamprecht, F. A., “The Lazy Flipper: Efficient Depth-Limited Exhaustive Search in Discrete Graphical Models”, in Computer Vision - {ECCV} 2012 - 12th European Conference on Computer Vision, Florence, Italy, October 7-13, 2012, Proceedings, Part {VII}, 2012.PDF icon Technical Report (446.28 KB)
P. Yarlagadda, Eigenstetter, A., and Ommer, B., “Learning Discriminative Chamfer Regularization”, in BMVC, 2012, p. 1--11.
L. Fiaschi, Nair, R., Köthe, U., and Hamprecht, F. A., “Learning to Count with Regression Forest and Structured Labels”, ICPR 2012. Proceedings, pp. 2685-2688, 2012.PDF icon Technical Report (3.66 MB)
X. Lou and Hamprecht, F. A., “Learning to Segment Dense Cell Nuclei with Shape Prior”, CVPR 2012. Proceedings, pp. 1012-1018, 2012.PDF icon Technical Report (2.66 MB)
C. Sommer, Fiaschi, L., Hamprecht, F. A., and Gerlich, D., “Learning-based Mitotic Cell Detection in Histopathological Images”, ICPR 2012. Proceedings, pp. 2306-2309, 2012.PDF icon Technical Report (1.96 MB)
J. Jancsary, Nowozin, S., and Rother, C., “Loss-specific training of non-parametric image restoration models: A new state of the art”, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2012, vol. 7578 LNCS, pp. 112–125.
J. Jancsary, Nowozin, S., and Rother, C., “Loss-specific training of non-parametric image restoration models: A new state of the art”, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2012, vol. 7578 LNCS, pp. 112–125.
2011
K. E. Richter and Jähne, B., “A laboratory study of the Schmidt number dependency of air-water gas transfer”, in Gas Transfer at Water Surfaces 2010, 2011, p. 322--332.
U. Schimpf, Nagel, L., and Jähne, B., “Lock-in thermography at the ocean surface: a local and fast method to investigate heat and gas exchange between ocean and atmosphere”, in DPG Frühjahrstagung Dresden, Fachverband Umweltphysik, 2011.
2010
B. Andres, Kappes, J. H., Köthe, U., and Hamprecht, F. A., “The Lazy Flipper: MAP Inference in Higher-Order Graphical Models by Depth-limited Exhaustive Search”, ArXiv e-prints, 2010.PDF icon Technical Report (625.06 KB)
H. Nickisch, Rother, C., Kohli, P., and Rhemann, C., “Learning an Interactive Segmentation System - Supplemental Material”, 2010.
M. Jehle, Sommer, C., and Jähne, B., “Learning of Optimal Illumination for Material Classification”, in Proceedings of the 32nd DAGM Symposium on Pattern Recognition, Darmstadt, Germany, 2010, pp. 563-572.
M. Jehle, Sommer, C., and Jähne, B., “Learning of optimal illumination for material classification”, in Pattern Recognition, 2010, vol. 6376, p. 563--572.
B. Ommer and Buhmann, J. M., “Learning the Compositional Nature of Visual Object Categories for Recognition”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, p. 501--516, 2010.PDF icon Technical Report (2.78 MB)
J. Fehr, “Local Rotation Invariant Patch Descriptors for 3D Vector Fields”, Pattern Recognition, International Conference on, Istanbul, Turkey, August 23-26, 2010, pp. 1381-1384, 2010.
2009
J. Fehr and Burkhardt, H., “Local Rotation Invariant Patch Descriptors for 3D Vector Fields”, in to be submitted, 2009.
2008
A. Haja, Jähne, B., and Abraham, S., “Localization accuracy of region detectors”, in Proceedings CVPR'08, 2008.
2007
B. Ommer and Buhmann, J. M., “Learning the Compositional Nature of Visual Objects”, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2007, p. 1--8.PDF icon Technical Report (2.78 MB)
V. Lempitsky, Rother, C., and Blake, A., “LogCut - Efficient graph cut optimization for markov random fields”, in Proceedings of the IEEE International Conference on Computer Vision, 2007.
2006
D. Withopf and Jähne, B., “Learning algorithm for real-time vehicle tracking”, in Proc. IEEE Intelligent Transportation Systems Conference ITSC '06, 2006, p. 516--521.
B. Ommer and Buhmann, J. M., “Learning Compositional Categorization Models”, in Proceedings of the European Conference on Computer Vision, 2006, vol. 3953, p. 316--329.PDF icon Technical Report (1.35 MB)
M. Bergtholdt, Kappes, J. H., and Schnörr, C., “Learning of Graphical Models and Efficient Inference for Object Class Recognition”, in Proc. DAGM 2006, 2006, vol. 375-388, pp. 375-388.
M. Heiler and Schnörr, C., “Learning Sparse Representations by Non-Negative Matrix Factorization and Sequential Cone Programming”, J. Mach. Learning Res., vol. 7, pp. 1385–1407, 2006.
B. Ommer, Sauter, M., and M., B. J., “Learning Top-Down Grouping of Compositional Hierarchies for Recognition”, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Workshop on Perceptual Organization in Computer Vision, 2006, p. 194--194.PDF icon Technical Report (358.98 KB)

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