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L. Balles, “Deep Learning for Diabetic Retinopathy Diagnostics”, University of Heidelberg, 2016.

P. Schmidt, “Deep Learning for Bioimage Analysis”, University of Heidelberg, 2016.

A. Ruiz, “Deep k-segments: a generalization of k-means”, Heidelberg University, 2021.

M. Kandemir and Hamprecht, F. A., “The Deep Feed-Forward Gaussian Process: An Effective Generalization to Covariance Priors”, NIPS. Proceedings, vol. 44. pp. 145-159, 2015.

Supplementary Material (223.39 KB)

Technical Report (2.58 MB)

L. Cerrone, “Deep End-to-End Learning of a Diffusion Process for Seeded Image Segmentation”, Heidelberg University, 2018.

M. Haußmann, Hamprecht, F. A., and Kandemir, M., “Deep Active Learning with Adaptive Acquisition”, IJCAI. Proceedings. pp. 2470-2476, 2019.

Technical Report (137.6 KB)

F. Becker and Schnörr, C., “Decomposition of Quadratric Variational Problems”, in Pattern Recognition -- 30th DAGM Symposium, 2008, vol. 5096, p. 325--334.

Technical Report (1.29 MB)

F. Becker and Schnörr, C., “Decomposition of Quadratric Variational Problems”, in Pattern Recognition -- 30th DAGM Symposium, 2008, vol. 5096, p. 325--334.

G. -hung Lu, Tsai, W. -ting, and Jähne, B., “Decomposing infrared images of wind waves for quantitative separation into characteristic flow processes”, IEEE Transactions on Geoscience and Remote Sensing, vol. 57, pp. 8304–8316, 2019.

S. Nowozin, Rother, C., Bagon, S., Sharp, T., Yao, B., and Kohli, P., “Decision tree fields”, in Proceedings of the IEEE International Conference on Computer Vision, 2011, pp. 1668–1675.

F. Rennekamp, “Datenbank gestützte Verwaltung kalibrierter Bildsequenzen zur Qualitätsbewertung von Algorithmen”, Fakultät für Physik und Astronomie Universität Heidelberg, 1998.

S. Wanner, Meister, S., and Goldlücke, B., “Datasets and Benchmarks for Densely Sampled 4D Light Fields”, in Vision, Modeling & Visualization, 2013, p. 225--226.

K. Honauer, Johannsen, O., Kondermann, D., and Goldlücke, B., “A Dataset and Evaluation Methodology for Depth Estimation on 4D Light Fields”, in Computer Vision - ACCV 2016 : 13th Asian Conference on Computer Vision, Taipei, Taiwan, November 20-24, 2016, Revised Selected Papers, Part III, Cham, 2016.

S. Hader, “Data Mining auf multidimensionalen und komplexen Daten in der industriellen Bildverarbeitung”. University of Heidelberg, 2006.

B. Jähne, Klar, M., and Jehle, M., “Data analysis”, Handbook of Experimental Fluid Mechanics. Springer, p. 1437--1491, 2007.

B. Jähne, “Data acquisition by imaging detectors”, Handbook of Experimental Fluid Mechanics. Springer, p. 1419--1436, 2007.

S. Lang and Ommer, B., “Das Objekt jenseits der Digitalisierung”, Das digitale Objekt, vol. 7. 2020.

lang_ommer_digitalhumanities_2020_.pdf (599.56 KB)

C

T. Beier, Kröger, T., Kappes, J. H., Köthe, U., and Hamprecht, F. A., “Cut, Glue and Cut: A Fast, Approximate Solver for Multicut Partitioning”, in 2014 {IEEE} Conference on Computer Vision and Pattern Recognition, {CVPR} 2014, Columbus, OH, USA, June 23-28, 2014, 2014.

Technical Report (10.06 MB)

A. Shekhovtsov, Kohli, P., and Rother, C., “Curvature prior for MRF-based segmentation and shape inpainting”, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2012, vol. 7476 LNCS, pp. 41–51.

A. Shekhovtsov, Kohli, P., and Rother, C., “Curvature prior for MRF-based segmentation and shape inpainting”, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2012, vol. 7476 LNCS, pp. 41–51.

A. Shekhovtsov, Kohli, P., and Rother, C., “Curvature prior for MRF-based segmentation and shape inpainting”, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2012, vol. 7476 LNCS, pp. 41–51.

L. Maier-Hein, Mersmann, S., Kondermann, D., Stock, C., Kenngott, H., Sanchez, A., Wagner, M., Preukschas, A., Wekerle, A. - L., Helfert, S., Bodenstedt, S., and Speidel, S., “Crowdsourcing for reference correspondence generation in endoscopic images”, in MICCAI, 2014.

D. Schlesinger, Jug, F., Myers, G., Rother, C., and Kainmueller, D., “Crowd sourcing image segmentation with iaSTAPLE”, in Proceedings - International Symposium on Biomedical Imaging, 2017, pp. 401–405.

J. Fehr, Reisert, M., and Burkhardt, H., “Cross-Correlation and Rotation Estimation of Local 3D Vector FieldPatches”, in Proceedings of the ISVC 2009, Part I, 2009, vol. 5875, pp. 287-296.

N. Sayed, Brattoli, B., and Ommer, B., “Cross and Learn: Cross-Modal Self-Supervision”, in German Conference on Pattern Recognition (GCPR) (Oral), Stuttgart, Germany, 2018.

Article (891.47 KB)

Oral slides (9.17 MB)

B. Jähne, Waas, S., and Klinke, J., “A critical theoretical review of optical techniques for short ocean wave measurements”, in Optics of the Air-Sea Interface: Theory and Measurements, 1992, vol. 1749, p. 204--215.

S. Petra, Schnörr, C., and Schröder, A., “Critical Parameter Values and Reconstruction Propertiesof Discrete Tomography: Application to Experimental FluidDynamics”, Fundamenta Informaticae, vol. 125, p. 285--312, 2013.

Technical Report (1.42 MB)

S. Petra, Schnörr, C., and Schröder, A., “Critical Parameter Values and Reconstruction Properties of Discrete Tomography: Application to Experimental Fluid Dynamics”. 2012.

S. Nicolas Ro Meister, “On Creating Reference Data for Performance Analysis in Image Processing”, vol. Dissertation. IWR, Fakultät für Physik und Astronomie, Univ. Heidelberg, 2014.

S. Meister, “On Creating Reference Data for Performance Analysis in Image Processing”. University of Heidelberg, 2013.

S. Meister, “On Creating Reference Data for Performance Analysis in Image Processing”. IWR, Fakultät für Physik und Astronomie, Univ.\ Heidelberg, 2014.

B. Güssefeld, Honauer, K., and Kondermann, D., “Creating Feasible Reflectance Data for Synthetic Optical Flow Datasets”, in Advances in Visual Computing - 12th International Symposium, {ISVC} 2016, Las Vegas, NV, USA, December 12-14, 2016, Proceedings, Part {I}, 2016.

S. Peter, Diego, F., Hamprecht, F. A., and Nadler, B., “Cost-efficient Gradient Boosting”, NIPS, poster. 2017.

S. Vicente, Kolmogorov, V., and Rother, C., “Cosegmentation revisited: Models and optimization”, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2010, vol. 6312 LNCS, pp. 465–479.

C. Rother, Kolmogorov, V., Minka, T., and Blake, A., “Cosegmentation of image pairs by histogram matching - Incorporating a global constraint into MRFs”, in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2006, vol. 1, pp. 994–1000.

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