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D
Balles, L (2016). Deep Learning For Diabetic Retinopathy Diagnostics. University of Heidelberg
Schmidt, P (2016). Deep Learning For Bioimage Analysis. University of Heidelberg
Ruiz, A (2021). Deep K-Segments: A Generalization Of K-Means. Heidelberg University
Kandemir, M and Hamprecht, F A (2015). The Deep Feed-Forward Gaussian Process: An Effective Generalization to Covariance Priors. NIPS. Proceedings. 44 145-159PDF icon Supplementary Material (223.39 KB)PDF icon Technical Report (2.58 MB)
Cerrone, L (2018). Deep End-To-End Learning Of A Diffusion Process For Seeded Image Segmentation. Heidelberg University
Haußmann, M, Hamprecht, F A and Kandemir, M (2019). Deep Active Learning with Adaptive Acquisition. IJCAI. Proceedings. 2470-2476PDF icon Technical Report (137.6 KB)
Becker, F and Schnörr, C (2008). Decomposition of Quadratric Variational Problems. Pattern Recognition -- 30th DAGM Symposium. Springer Verlag. 5096 325--334PDF icon Technical Report (1.29 MB)
Becker, F and Schnörr, C (2008). Decomposition of Quadratric Variational Problems. Pattern Recognition -- 30th DAGM Symposium. 5096 325--334
Lu, G -hung, Tsai, W -ting and Jähne, B (2019). Decomposing infrared images of wind waves for quantitative separation into characteristic flow processes. IEEE Transactions on Geoscience and Remote Sensing. 57 8304–8316
Nowozin, S, Rother, C, Bagon, S, Sharp, T, Yao, B and Kohli, P (2011). Decision tree fields. Proceedings of the IEEE International Conference on Computer Vision. 1668–1675
Rennekamp, F (1998). Datenbank Gestützte Verwaltung Kalibrierter Bildsequenzen Zur Qualitätsbewertung Von Algorithmen. Fakultät für Physik und Astronomie Universität Heidelberg
Wanner, S, Meister, S and Goldlücke, B (2013). Datasets and Benchmarks for Densely Sampled 4D Light Fields. Vision, Modeling & Visualization. 225--226
Honauer, K, Johannsen, O, Kondermann, D and Goldlücke, B (2016). A Dataset and Evaluation Methodology for Depth Estimation on 4D Light Fields. Computer Vision - ACCV 2016 : 13th Asian Conference on Computer Vision, Taipei, Taiwan, November 20-24, 2016, Revised Selected Papers, Part III. Springer, Cham
Hader, S (2006). Data Mining auf multidimensionalen und komplexen Daten in der industriellen Bildverarbeitung. University of Heidelberg
Jähne, B, Klar, M and Jehle, M (2007). Data analysis. Handbook of Experimental Fluid Mechanics. Springer. 1437--1491
Jähne, (2007). Data acquisition by imaging detectors. Handbook of Experimental Fluid Mechanics. Springer. 1419--1436
Lang, S and Ommer, B (2020). Das Objekt jenseits der Digitalisierung. Das digitale Objekt. 7. http://www.deutsches-museum.de/fileadmin/Content/010_DM/060_Verlag/studies-7.pdfPDF icon lang_ommer_digitalhumanities_2020_.pdf (599.56 KB)
C
Beier, T, Kröger, T, Kappes, J H, Köthe, U and Hamprecht, F A (2014). Cut, Glue and Cut: A Fast, Approximate Solver for Multicut Partitioning. 2014 {IEEE} Conference on Computer Vision and Pattern Recognition, {CVPR} 2014, Columbus, OH, USA, June 23-28, 2014. http://dx.doi.org/10.1109/CVPR.2014.17PDF icon Technical Report (10.06 MB)
Shekhovtsov, A, Kohli, P and Rother, C (2012). Curvature prior for MRF-based segmentation and shape inpainting. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 7476 LNCS 41–51
Shekhovtsov, A, Kohli, P and Rother, C (2012). Curvature prior for MRF-based segmentation and shape inpainting. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 7476 LNCS 41–51. www.research.microsoft.com/vision/cambridge http://www.cs.ucl.ac.uk/staff/V.Kolmogorov/papers/StereoSegmentation_PAMI06.pdf%5Cnpapers3://publication/uuid/F008E9F4-510D-4478-A3C0-1BFB22F6AEA0
Shekhovtsov, A, Kohli, P and Rother, C (2012). Curvature prior for MRF-based segmentation and shape inpainting. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 7476 LNCS 41–51. http://arxiv.org/abs/1109.1480
Maier-Hein, L, Mersmann, S, Kondermann, D, Stock, C, Kenngott, H, Sanchez, A, Wagner, M, Preukschas, A, Wekerle, A - L, Helfert, S, Bodenstedt, S and Speidel, S (2014). Crowdsourcing for reference correspondence generation in endoscopic images. MICCAI
Schlesinger, D, Jug, F, Myers, G, Rother, C and Kainmueller, D (2017). Crowd sourcing image segmentation with iaSTAPLE. Proceedings - International Symposium on Biomedical Imaging. 401–405
Fehr, J, Reisert, M and Burkhardt, H (2009). Cross-Correlation and Rotation Estimation of Local 3D Vector FieldPatches. Proceedings of the ISVC 2009, Part I. Springer. 5875 287-296
Sayed, N, Brattoli, B and Ommer, B (2018). Cross and Learn: Cross-Modal Self-Supervision. German Conference on Pattern Recognition (GCPR) (Oral). Stuttgart, Germany. https://arxiv.org/abs/1811.03879v1PDF icon Article (891.47 KB)PDF icon Oral slides (9.17 MB)
Jähne, B, Waas, S and Klinke, J (1992). A critical theoretical review of optical techniques for short ocean wave measurements. Optics of the Air-Sea Interface: Theory and Measurements. 1749 204--215
Petra, S, Schnörr, C and Schröder, A (2013). Critical Parameter Values and Reconstruction Propertiesof Discrete Tomography: Application to Experimental FluidDynamics. Fundamenta Informaticae. 125 285--312PDF icon Technical Report (1.42 MB)
Petra, S, Schnörr, C and Schröder, A (2012). Critical Parameter Values and Reconstruction Properties of Discrete Tomography: Application to Experimental Fluid Dynamics. http://arxiv.org/abs/1209.4316
Meister, S Nicolas Ro (2014). On Creating Reference Data for Performance Analysis in Image Processing. IWR, Fakultät für Physik und Astronomie, Univ. Heidelberg. Dissertation
Meister, S (2013). On Creating Reference Data for Performance Analysis in Image Processing. University of Heidelberg
Meister, S (2014). On Creating Reference Data for Performance Analysis in Image Processing. IWR, Fakultät für Physik und Astronomie, Univ.\ Heidelberg. http://www.ub.uni-heidelberg.de/archiv/16193
Güssefeld, B, Honauer, K and Kondermann, D (2016). Creating Feasible Reflectance Data for Synthetic Optical Flow Datasets. Advances in Visual Computing - 12th International Symposium, {ISVC} 2016, Las Vegas, NV, USA, December 12-14, 2016, Proceedings, Part {I}
Peter, S, Diego, F, Hamprecht, F A and Nadler, B (2017). Cost-efficient Gradient Boosting. NIPS, poster
Vicente, S, Kolmogorov, V and Rother, C (2010). Cosegmentation revisited: Models and optimization. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 6312 LNCS 465–479
Rother, C, Kolmogorov, V, Minka, T and Blake, A (2006). Cosegmentation of image pairs by histogram matching - Incorporating a global constraint into MRFs. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 1 994–1000. http://research.microsoft.com/vision/cambridge/

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