Publications

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Author Title Type [ Year(Desc)]
2016
Berger, J and Schnörr, C (2016). Joint Recursive Monocular Filtering of Camera Motion and Disparity Map. 38th German Conference on Pattern Recognition. Springer, Hannover. https://arxiv.org/abs/1606.02092PDF icon Technical Report (2.34 MB)
Berger, J and Schnörr, C (2016). Joint Recursive Monocular Filtering of Camera Motion and Disparity Map. 38th German Conference on Pattern Recognition
Stefanoiu, A, Weinmann, A, Storath, M, Navab, N and Baust, M (2016). Joint Segmentation and Shape Regularization with a Generalized Forward Backward Algorithm. IEEE Transactions on Image Processing. 25 3384 - 3394PDF icon Technical Report (3.55 MB)
Schiegg, M, Diego, F and Hamprecht, F A (2016). Learning Diverse Models: The Coulomb Structured Support Vector Machine. ECCV. Proceedings. Springer. LNCS 9907 585-599PDF icon Technical Report (2.54 MB)
von Borstel, M (2016). Learning To Count From Weak Supervision. University of Heidelberg
Diebold, M (2016). Light-Field Imaging and Heterogeneous Light Fields. IWR, Univ. Heidelberg. Dissertation
Pinggera, P, Ramos, S, Gehrig, S, Franke, U, Rother, C and Mester, R (2016). Lost and found: Detecting small road hazards for self-driving vehicles. IEEE International Conference on Intelligent Robots and Systems. 2016-Novem 1099–1106. http://www.6d-vision.com/lostandfounddataset
Richmond, D L, Kainmueller, D, Yang, M Y, Myers, E W and Rother, C (2016). Mapping auto-context decision forests to deep convnets for semantic segmentation. British Machine Vision Conference 2016, BMVC 2016. 2016-Septe 144.1–144.12. http://arxiv.org/abs/1507.07583
Richmond, D L, Kainmueller, D, Yang, M Y, Myers, E W and Rother, C (2016). Mapping auto-context decision forests to deep convnets for semantic segmentation. British Machine Vision Conference 2016, BMVC 2016. 2016-Septe 144.1–144.12. https://github.com/BVLC/caffe/wiki/Model-Zoo\#fcn
Richmond, D L, Kainmueller, D, Yang, M Y, Myers, E W and Rother, C (2016). Mapping auto-context decision forests to deep convnets for semantic segmentation. British Machine Vision Conference 2016, BMVC 2016. 2016-Septe 144.1–144.12
Strouse, T M D (2016). Marijuana's Public Health Pros and Cons | For Better | US News. U.S. News and World Report. http://health.usnews.com/health-news/patient-advice/articles/2016-10-12/marijuanas-public-health-pros-and-cons
Lenor, S (2016). Model-Based Estimation of Meteorological Visibility in the Context of Automotive Camera Systems. IWR, Univ. Heidelberg. Dissertation
Kappes, J Hendrik, Swoboda, P, Savchynskyy, B, Hazan, T and Schnörr, C (2016). Multicuts and Perturb & MAP for Probabilistic Graph Clustering. Journal of Mathematical Imaging and Vision. 56 221–237. http://arxiv.org/abs/1601.02088
Kappes, J H, Swoboda, P, Savchynskyy, B, Hazan, T and Schnörr, C (2016). Multicuts and Perturb & MAP for Probabilistic Graph Clustering. J. Math. Imag. Vision. 56 221–237
Jähne, B and Schwarzbauer, M (2016). Noise equalisation and quasi loss-less image data compression – or how many bits needs an image sensor?. tm – Technisches Messen. 83 16–24
Zisler, M, Kappes, J H, Schnörr, C, Petra, S and Schnörr, C (2016). Non-Binary Discrete Tomography by Continuous Non-Convex Optimization. IEEE Comp. Imaging. 2 335-347
Bodnariuc, E, Petra, S, Poelma, C and Schnörr, C (2016). Parametric Dictionary-Based Velocimetry for Echo PIV. Proc. CGPR
Swoboda, P, Shekhovtsov, A, Kappes, J Hendrik, Schnörr, C and Savchynskyy, B (2016). Partial Optimality by Pruning for MAP-Inference with General Graphical Models. IEEE Transactions on Pattern Analysis and Machine Intelligence. IEEE Computer Society. 38 1370–1382
Swoboda, P, Shekhovtsov, A, Kappes, J H, Schnörr, C and Savchynskyy, B (2016). Partial Optimality by Pruning for MAP-Inference with General Graphical Models. IEEE Trans. Patt. Anal. Mach. Intell. 38 1370–1382
Bodnariuc, E, Schiffner, M F, Petra, S and Schnörr, C (2016). Plane Wave Acoustic Superposition for Fast Ultrasound Imaging. International Ultrasonics Symposium
Hosseini Jafari, O and Yang, M Ying (2016). Real-time RGB-D based template matching pedestrian detection. Proceedings - IEEE International Conference on Robotics and Automation. 2016-June 5520–5527
von Schmude, N, Lothe, P and Jähne, B (2016). Relative Pose Estimation from Straight Lines using Parallel Line Clustering and its Application to Monocular Visual Odometry. Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications
Haubold, C, Schiegg, M, Kreshuk, A, Berg, S, Köthe, U and Hamprecht, F A (2016). Segmenting and Tracking Multiple Dividing Targets Using ilastik. Focus on Bio-Image Informatics. Springer. 219 199-229PDF icon Technical Report (4.46 MB)
Rathore, D (2016). Semantic Segmentation Using Deep Learning. University of Heidelberg
Schwarz, K (2016). Spatio-Temporal Measurements Of Water-Wave Height And Slope Using Laser-Induced Fluorescence And Splines. Institut für Umweltphysik, Universität Heidelberg, Germany
Sellent, A, Rother, C and Roth, S (2016). Stereo video deblurring. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 9906 LNCS 558–575
Sellent, A, Rother, C and Roth, S (2016). Stereo Video Deblurring-Supplemental Material
Kiem, A (2016). Structured Learning On Calcium Imaging Data. University of Heidelberg
Diego, F and Hamprecht, F A (2016). Structured Regression Gradient Boosting. CVPR. Proceedings. 1459-1467PDF icon Technical Report (3.97 MB)
Silvestri, F, Reinelt, G and Schnörr, C (2016). Symmetry-free SDP Relaxations for Affine Subspace Clustering. http://arxiv.org/abs/1607.07387
Brachmann, E, Michel, F, Krull, A, Yang, M Ying, Gumhold, S and Rother, C (2016). Uncertainty-Driven 6D Pose Estimation of Objects and Scenes from a Single RGB Image. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2016-Decem 3364–3372
Brachmann, E, Michel, F, Krull, A, Yang, M Ying, Gumhold, S and Rother, C (2016). Uncertainty-Driven 6D Pose Estimation of Objects and Scenes from a Single RGB Image. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2016-Decem 3364–3372
Kandemir, M, Haußmann, M, Diego, F, Rajamani, K, van der Laak, J and Hamprecht, F A (2016). Variational weakly-supervised Gaussian processes. BMVC. ProceedingsPDF icon Technical Report (3.28 MB)
Kleesiek, J, Petersen, J, Döring, M, Maier-Hein, K, Köthe, U, Wick, W, Hamprecht, F A, Bendszus, M and Biller, A (2016). Virtual Raters for Reproducible and Objective Assessments in Radiology. Nature Scientific Reports. 6PDF icon Technical Report (2.81 MB)
Haußmann, (2016). Weakly Supervised Detection With Gaussian Processes. University of Heidelberg

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