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Abu Alhaija, H, Sellent, A, Kondermann, D and Rother, C (2015). Graphflow—6D large displacement scene flow via graph matching. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 9358 285–296
Abu Alhaija, H, Mustikovela, S Karthik, Mescheder, L, Geiger, A and Rother, C (2017). Augmented reality meets deep learning for car instance segmentation in urban scenes. British Machine Vision Conference 2017, BMVC 2017
Abu Alhaija, H, Mustikovela, S Karthik, Geiger, A and Rother, C (2019). Geometric Image Synthesis. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 11366 LNCS 85–100. https://youtu.be/W2tFCz9xJoU
Abu Alhaija, H, Mustikovela, S Karthik, Mescheder, L, Geiger, A and Rother, C (2018). Augmented Reality Meets Computer Vision: Efficient Data Generation for Urban Driving Scenes. International Journal of Computer Vision. 126 961–972. http://arxiv.org/abs/1708.01566
Abu Alhaija, H, Mustikovela, S Karthik, Mescheder, L, Geiger, A and Rother, C (2018). Augmented Reality Meets Computer Vision. International Journal of Computer Vision. In press 1–13
Abu Alhaija, H, Mustikovela, S K, Mescheder, A, Geiger, C and Rother, C (2018). Augmented Reality Meets Computer Vision Efficient Data Generation for Urban Driving Scenes. IJCV. 1-12PDF icon Technical Report (3.83 MB)
Abu Alhaija, H, Mustikovela, S K, Geiger, A and Rother, C (2018). Geometric Image Synthesis. ACCV. Proceedings, in pressPDF icon Technical Report (1.83 MB)
Acker, J F, 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 (2008). Inverse Problems and Parameter Identification in Image Processing. Mathematical Methods in Time Series Analysis and Digital Image Processing. Springer. 111--151
Adler, T J, Ayala, L, Ardizzone, L, Kenngott, H G, Vemuri, A, Müller-Stich, B P, Rother, C, Köthe, U and Maier-Hein, L (2019). Out of Distribution Detection for Intra-operative Functional Imaging. MICCAI UNSURE Workshop 2019. 11840 LNCS 75–82PDF icon PDF (3.1 MB)
Afifi, M, Derpanis, K G, Ommer, B and Brown, M S (2020). Learning to Correct Overexposed and Underexposed Photos. https://arxiv.org/abs/2003.11596
Andres, B, Beier, T and Kappes, J H (2012). OpenGM: A C++ Library for Discrete Graphical Models. ArXiv e-prints
Andres, B, Kappes, J Hendrik, Beier, T, Köthe, U and Hamprecht, F A (2012). The Lazy Flipper: Efficient Depth-limited Exhaustive Search in Discrete Graphical Models. ECCV 2012
Andres, B, Kappes, J H, Beier, T, Köthe, U and Hamprecht, F A (2011). Probabilistic Image Segmentation with Closedness Constraints. Proceedings of ICCV
Andres, B (2011). Automated Segmentation of Large 3D Images of Nervous Systems Using a Higher-order Graphical Model. University of Heidelberg
Andres, B (2007). Model Selection In Optical Flow-Based Motion Estimation By Means Of Residual Analysis. University of Heidelberg
Andres, B, Hamprecht, F A and Garbe, C S (2007). Selection of Local Optical Flow Models by Means of Residual Analysis. Pattern Recognition. Springer. 4713 72-81PDF icon Technical Report (229.64 KB)
Andres, B, Köthe, U, Bonea, A, Nadler, B and Hamprecht, F A (2009). Quantitative Assessment of Image Segmentation Quality by Random Walk Relaxation Times. Pattern Recognition. 31st DAGM Symposium, Jena, Germany, September 9-11, 2009. Proceedings. Springer. 5748 502-511PDF icon Technical Report (3.08 MB)
Andres, B, Köthe, U, Helmstaedter, M, Denk, W and Hamprecht, F A (2008). Segmentation of SBFSEM Volume Data of Neural Tissue by Hierarchical Classification. Pattern Recognition. 30th DAGM Symposium Munich, Germany, June 10-13, 2008. Proceedings. Springer. 5096 142-152PDF icon Technical Report (1.21 MB)
Andres, B, Köthe, U, Kröger, T and Hamprecht, F A (2010). How to Extract the Geometry and Topology from Very Large 3D Segmentations. ArXiv e-prints. http://arxiv.org/abs/1009.6215PDF icon Technical Report (1.44 MB)
Andres, B, Kappes, J H, Beier, T, Köthe, U and Hamprecht, F A (2012). The Lazy Flipper: Efficient Depth-Limited Exhaustive Search in Discrete Graphical Models. Computer Vision - {ECCV} 2012 - 12th European Conference on Computer Vision, Florence, Italy, October 7-13, 2012, Proceedings, Part {VII}. http://dx.doi.org/10.1007/978-3-642-33786-4_12PDF icon Technical Report (446.28 KB)
Andres, B, Köthe, U, Kröger, T and Hamprecht, F A (2010). Runtime-Flexible Multi-dimensional Views and Arrays for C++98 and C++0x. ArXiv e-prints. http://arxiv.org/abs/1008.2909v1PDF icon Technical Report (415.54 KB)
Andres, B, Köthe, U, Kröger, T, Helmstaedter, M, Briggmann, K L, Denk, W and Hamprecht, F A (2012). 3D Segmentation of SBFSEM Images of Neuropil by a Graphical Model over Supervoxel Boundaries. Medical Image Analysis. 16 (2012) 796-805PDF icon Technical Report (20.85 MB)
Andres, B, Kappes, J H, Beier, T, Köthe, U and Hamprecht, F A (2011). Probabilistic Image Segmentation with Closedness Constraints. ICCV, Proceedings. 2611 - 2618PDF icon Technical Report (8.18 MB)
Andres, B, Kappes, J H, Köthe, U and Hamprecht, F A (2010). The Lazy Flipper: MAP Inference in Higher-Order Graphical Models by Depth-limited Exhaustive Search. ArXiv e-prints. http://arxiv.org/abs/1009.4102PDF icon Technical Report (625.06 KB)
Andres, B, Kappes, J H, Köthe, U, Schnörr, C and Hamprecht, F A (2010). An Empirical Comparison of Inference Algorithms for Graphical Models with Higher Order Factors Using OpenGM. Pattern Recognition, Proc.~32th DAGM Symposium. 353-362
Andres, B, Kondermann, C, Kondermann, D, Köthe, U, Hamprecht, F A and Garbe, C S (2008). 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. 1-6PDF icon Technical Report (1.58 MB)
Andres, B, Kondermann, C, Kondermann, D, Hamprecht, F A and Garbe, C S (2008). On errors-in-variables regression with arbitrary covariance and its application to optical flow estimation. IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2008. IEEE. 1--6
Andres, B, Garbe, C S, Schnörr, C and Jähne, B (2007). Selection of local optical flow models by means of residual analysis. Proceedings of the 29th DAGM Symposium on Pattern Recognition. Springer. 72--81
Andres, B (2007). Model Selection In Optical Flow-Based Motion Estimation By Means Of Residual Analysis. University of Heidelberg
Andres, B, Kröger, T, Briggmann, K L, Denk, W, Norogod, N, Knott, G W, Köthe, U and Hamprecht, F A (2012). Globally Optimal Closed-Surface Segmentation for Connectomics. ECCV 2012. Proceedings, Part 3. 778-791PDF icon Technical Report (2.72 MB)
Andres, B, Kappes, J H, Beier, T, Köthe, U and Hamprecht, F A (2012). The Lazy Flipper: Efficient Depth-limited Exhaustive Search in Discrete Graphical Models. ECCV 2012PDF icon Technical Report (532.64 KB)
Andres, B, Kappes, J H, Beier, T, Köthe, U and Hamprecht, F A (2011). Probabilistic Image Segmentation with Closedness Constraints. Proceedings of ICCVPDF icon Technical Report (2.95 MB)
Andres, B, Kappes, J H, Köthe, U, Schnörr, C and Hamprecht, F A (2010). An Empirical Comparison of Inference Algorithms for Graphical Models with Higher Order Factors Using OpenGM. Pattern Recognition, Proc.~32th DAGM SymposiumPDF icon Technical Report (218.43 KB)
Andrew, A M (2001). Multiple View Geometry in Computer Vision. Kybernetes. 30 1333–1341
Antic, B, Büchler, U, Wahl, A - S, Schwab, M E and Ommer, B (2015). Spatiotemporal Parsing of Motor Kinematics for Assessing Stroke Recovery. Medical Image Computing and Computer-Assisted Intervention. SpringerPDF icon Article (2.24 MB)

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