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A
H. Abu Alhaija, Sellent, A., Kondermann, D., and Rother, C., “Graphflow—6D large displacement scene flow via graph matching”, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2015, vol. 9358, pp. 285–296.
H. Abu Alhaija, Mustikovela, S. Karthik, Mescheder, L., Geiger, A., and Rother, C., “Augmented reality meets deep learning for car instance segmentation in urban scenes”, in British Machine Vision Conference 2017, BMVC 2017, 2017.
H. Abu Alhaija, Mustikovela, S. Karthik, Geiger, A., and Rother, C., “Geometric Image Synthesis”, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2019, vol. 11366 LNCS, pp. 85–100.
H. Abu Alhaija, Mustikovela, S. Karthik, Mescheder, L., Geiger, A., and Rother, C., “Augmented Reality Meets Computer Vision: Efficient Data Generation for Urban Driving Scenes”, International Journal of Computer Vision, vol. 126, pp. 961–972, 2018.
H. Abu Alhaija, Mustikovela, S. Karthik, Mescheder, L., Geiger, A., and Rother, C., “Augmented Reality Meets Computer Vision”, International Journal of Computer Vision, vol. In press, pp. 1–13, 2018.
H. Abu Alhaija, Mustikovela, S. K., Geiger, A., and Rother, C., “Geometric Image Synthesis”, ACCV. Proceedings, in press. 2018.PDF icon Technical Report (1.83 MB)
H. Abu Alhaija, Mustikovela, S. K., Mescheder, A., Geiger, C., and Rother, C., “Augmented Reality Meets Computer Vision Efficient Data Generation for Urban Driving Scenes”, IJCV, pp. 1-12, 2018.PDF icon Technical Report (3.83 MB)
J. F. Acker, Berkels, B., Bredies, K., Diallo, M. S., Droske, M., Garbe, C. S., Holschneider, M., Hron, J., Kondermann, C., Kulesh, M., Maass, P., Olischläger, N., and Peitgen, H. - O., “Inverse Problems and Parameter Identification in Image Processing”, Mathematical Methods in Time Series Analysis and Digital Image Processing. Springer, p. 111--151, 2008.
T. J. Adler, Ayala, L., Ardizzone, L., Kenngott, H. G., Vemuri, A., Müller-Stich, B. P., Rother, C., Köthe, U., and Maier-Hein, L., “Out of Distribution Detection for Intra-operative Functional Imaging”, in MICCAI UNSURE Workshop 2019, 2019, vol. 11840 LNCS, pp. 75–82.PDF icon PDF (3.1 MB)
M. Afifi, Derpanis, K. G., Ommer, B., and Brown, M. S., “Learning Multi-Scale Photo Exposure Correction”, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021.
A. Andersson, Diego, F., Hamprecht, F. A., and Wählby, C., “ISTDECO: In Situ Transcriptomics Decoding by Deconvolution”, bioRxiv, 2021.
B. Andres, Kappes, J. H., Beier, T., Köthe, U., and Hamprecht, F. A., “Probabilistic Image Segmentation with Closedness Constraints”, in Proceedings of ICCV, 2011.
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, Beier, T., and Kappes, J. H., “OpenGM: A C++ Library for Discrete Graphical Models”, ArXiv e-prints, 2012.
B. Andres, Kröger, T., Briggmann, K. L., Denk, W., Norogod, N., Knott, G. W., Köthe, U., and Hamprecht, F. A., “Globally Optimal Closed-Surface Segmentation for Connectomics”, in ECCV 2012. Proceedings, Part 3, 2012, pp. 778-791.PDF icon Technical Report (2.72 MB)
B. Andres, “Automated Segmentation of Large 3D Images of Nervous Systems Using a Higher-order Graphical Model”. University of Heidelberg, 2011.
B. Andres, “Model Selection in Optical Flow-Based Motion Estimation by Means of Residual Analysis”, University of Heidelberg, 2007.
B. Andres, Hamprecht, F. A., and Garbe, C. S., “Selection of Local Optical Flow Models by Means of Residual Analysis”, in Pattern Recognition, 2007, vol. 4713, pp. 72-81.PDF icon Technical Report (229.64 KB)
B. Andres, Kondermann, C., Kondermann, D., Köthe, U., Hamprecht, F. A., and Garbe, C. S., “On errors-in-variables regression with arbitrary covariance and its application to optical flow estimation”, Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on, pp. 1-6, 2008.PDF icon Technical Report (1.58 MB)
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)
B. Andres, Kappes, J. H., Köthe, U., Schnörr, C., and Hamprecht, F. A., “An Empirical Comparison of Inference Algorithms for Graphical Models with Higher Order Factors Using OpenGM”, in Pattern Recognition, Proc.~32th DAGM Symposium, 2010, pp. 353-362.
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)
B. Andres, Kappes, J. H., Beier, T., Köthe, U., and Hamprecht, F. A., “Probabilistic Image Segmentation with Closedness Constraints”, in ICCV, Proceedings, 2011, pp. 2611 - 2618.PDF icon Technical Report (8.18 MB)
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.PDF icon Technical Report (3.08 MB)
B. Andres, Kondermann, C., Kondermann, D., Hamprecht, F. A., and Garbe, C. S., “On errors-in-variables regression with arbitrary covariance and its application to optical flow estimation”, in IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2008, 2008, p. 1--6.
B. Andres, Garbe, C. S., Schnörr, C., and Jähne, B., “Selection of local optical flow models by means of residual analysis”, in Proceedings of the 29th DAGM Symposium on Pattern Recognition, 2007, p. 72--81.
B. Andres, “Model Selection in Optical Flow-Based Motion Estimation by Means of Residual Analysis”, University of Heidelberg, 2007.
B. Andres, Köthe, U., Helmstaedter, M., Denk, W., and Hamprecht, F. A., “Segmentation of SBFSEM Volume Data of Neural Tissue by Hierarchical Classification”, in Pattern Recognition. 30th DAGM Symposium Munich, Germany, June 10-13, 2008. Proceedings, 2008, vol. 5096, pp. 142-152.PDF icon Technical Report (1.21 MB)
B. Andres, Köthe, U., Kröger, T., and Hamprecht, F. A., “How to Extract the Geometry and Topology from Very Large 3D Segmentations”, ArXiv e-prints, 2010.PDF icon Technical Report (1.44 MB)
B. Andres, Köthe, U., Kröger, T., and Hamprecht, F. A., “Runtime-Flexible Multi-dimensional Views and Arrays for C++98 and C++0x”, ArXiv e-prints, 2010.PDF icon Technical Report (415.54 KB)
B. Andres, Köthe, U., Kröger, T., Helmstaedter, M., Briggmann, K. L., Denk, W., and Hamprecht, F. A., “3D Segmentation of SBFSEM Images of Neuropil by a Graphical Model over Supervoxel Boundaries”, Medical Image Analysis, vol. 16 (2012), pp. 796-805, 2012.PDF icon Technical Report (20.85 MB)
B. Andres, Kappes, J. H., Köthe, U., Schnörr, C., and Hamprecht, F. A., “An Empirical Comparison of Inference Algorithms for Graphical Models with Higher Order Factors Using OpenGM”, in Pattern Recognition, Proc.~32th DAGM Symposium, 2010.PDF icon Technical Report (218.43 KB)
B. Andres, Kappes, J. H., Beier, T., Köthe, U., and Hamprecht, F. A., “Probabilistic Image Segmentation with Closedness Constraints”, in Proceedings of ICCV, 2011.PDF icon Technical Report (2.95 MB)
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)
A. M. Andrew, “Multiple View Geometry in Computer Vision”, Kybernetes, vol. 30. pp. 1333–1341, 2001.

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