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Álvarez, J M, Gevers, T, Diego, F and López, A M (2012). Road Geometry Classification by Adaptive Shape Models. IEEE Transactions on Intelligent Transportation Systems (ITS). 99 1-10
Atif, M (2013). Optimal Depth Estimation and Extended Depth of Field from Single Images by Computational Imaging using Chromatic Aberrations. IWR, Fakultät für Physik und Astronomie, Univ.\ Heidelberg. http://www.ub.uni-heidelberg.de/archiv/15594
Atif, M, Zimmermann, K and Jähne, B (2010). A space-variant (3D) image simulation tool for computational cameras. International Conference on Computational Photography (ICCP) 2010
Atif, M and Jähne, B (2013). Optimal Depth Estimation from a Single Image by Computational Imaging Using Chromatic Aberrations. tm --- Technisches Messen. 80 343--348
Atif, M and Jähne, B (2012). Optimal Depth Estimation from a Single Image by Computational Imaging using Chromatic Aberrations. Forum Bildverarbeitung. KIT Scientific Publishing. 23--34. http://digbib.ubka.uni-karlsruhe.de/volltexte/1000030440
Aström, F and Schnörr, C (2016). Double-Opponent Vectorial Total Variation. Proc. ECCV
Aström, F, Petra, S, Schmitzer, B and Schnörr, C (2016). A Geometric Approach to Image Labeling. Proc. ECCV
Aström, F, Petra, S, Schmitzer, B and Schnörr, C (2016). Image Labeling by Assignment. http://arxiv.org/abs/1603.05285
Aström, F, Petra, S, Schmitzer, B and Schnörr, C (2017). Image Labeling by Assignment. J. Math. Imag. Vision. 58 211–238. Papers/Astroem2017.pdf
Aström, F, Petra, S, Schmitzer, B and Schnörr, C (2016). The Assignment Manifold: A Smooth Model for Image Labeling. Proc. 2nd Int. Workshop on Differential Geometry in Computer Vision and Machine Learning (DIFF-CVML'16; oral presentation; Grenander best paper award)
Aström, F, Hühnerbein, R, Savarino, F, Recknagel, J and Schnörr, C (2017). MAP Image Labeling Using Wasserstein Messages and Geometric Assignment. Proc. SSVM. Springer. 10302
Aström, F and Schnörr, C (2016). A Geometric Approach to Color Image Regularization. https://arxiv.org/abs/1605.05977
Aström, F and Schnörr, C (2017). A Geometric Approach for Color Image Regularization. Comp. Vision Image Understanding. 165 43–59. https://doi.org/10.1016/j.cviu.2017.10.013
Arnold, M, Bell, P and Ommer, B (2013). Automated Learning of Self-Similarity and Informative Structures in Architecture. Scientific Computing & Cultural Heritage
Arnab, A, Zheng, S, Jayasumana, S, Romera-paredes, B, Kirillov, A, Savchynskyy, B, Rother, C, Kahl, F and Torr, P (2018). Conditional Random Fields Meet Deep Neural Networks for Semantic Segmentation. Cvpr. XX 1–15. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.308.8889&rep=rep1&type=pdf%0Ahttp://dx.doi.org/10.1109/CVPR.2012.6248050
Ardizzone, L, Lüth, C, Kruse, J, Rother, C and Köthe, U (2019). Guided Image Generation with Conditional Invertible Neural Networks. http://arxiv.org/abs/1907.02392
Ardizzone, L, Lüth, C, Kruse, J, Rother, C and Köthe, U (2019). Guided Image Generation with Conditional Invertible Neural Networks. http://arxiv.org/abs/1907.02392
Ardizzone, L, Mackowiak, R, Rother, C and Köthe, U (2020). Exact Information Bottleneck with Invertible Neural Networks: Getting the Best of Discriminative and Generative Modeling. http://arxiv.org/abs/2001.06448PDF icon PDF (2.87 MB)
Antic, B, Milbich, T and Ommer, B (2013). Less is More: Video Trimming for Action Recognition. Proceedings of the IEEE International Conference on Computer Vision, Workshop on Understanding Human Activities: Context and Interaction. IEEE. 515--521PDF icon Technical Report (984.89 KB)
Antic, B and Ommer, B (2014). Learning Latent Constituents for Recognition of Group Activities in Video. Proceedings of the European Conference on Computer Vision (ECCV) (Oral). Springer. 33--47PDF icon Technical Report (4.54 MB)
Antic, B and Ommer, B (2011). Video Parsing for Abnormality Detection. Proceedings of the IEEE International Conference on Computer Vision. IEEE. 2415--2422PDF icon Technical Report (990.21 KB)
Antic, B and Ommer, B (2015). Spatio-temporal Video Parsing for Abnormality Detection. arXiv. abs/1502.06235. http://arxiv.org/abs/1502.06235PDF icon Technical Report (4.61 MB)
Antic, B and Ommer, B (2012). Robust Multiple-Instance Learning with Superbags. Proceedings of the Aian Conference on Computer Vision (ACCV) (Oral). Springer. 242--255PDF icon Technical Report (319.58 KB)
Antic, B and Ommer, B (2015). Per-Sample Kernel Adaptation for Visual Recognition and Grouping. Proceedings of the IEEE International Conference on Computer Vision. IEEEPDF icon Technical Report (1.58 MB)
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)
Andrew, A M (2001). Multiple View Geometry in Computer Vision. Kybernetes. 30 1333–1341
Andres, B (2011). Automated Segmentation of Large 3D Images of Nervous Systems Using a Higher-order Graphical Model. University of Heidelberg
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 (2007). Model Selection In Optical Flow-Based Motion Estimation By Means Of Residual Analysis. University of Heidelberg
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, Beier, T and Kappes, J H (2012). OpenGM: A C++ Library for Discrete Graphical Models. ArXiv e-prints
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, 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, 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, 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)

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