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C. Schnörr, “Convex Variational Segmentation of Multi-Channel Images”, in Proc. 12th Int. Conf. on Analysis and Optimization of Systems: Images, Wavelets and PDE's, Paris, 1996, vol. 219.
L. A. Royer, Richmond, D. L., Rother, C., Andres, B., and Kainmueller, D., “Convexity shape constraints for image segmentation”, in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016, vol. 2016-Decem, pp. 402–410.
M. Hering, Körner, K., and Jähne, B., “Correlated speckle noise in white-light interferometry: theoretical analysis of measurement uncertainty”, Appl. Optics, vol. 48, p. 525--538, 2009.
G. Krause, “Correlation of Performance and Entropy in Active Learning with Convolutional Neural Networks”, Heidelberg University, 2017.
B. Maco, Holtmaat, A., Cantoni, M., Kreshuk, A., Straehle, C. N., Hamprecht, F. A., and Knott, G. W., “Correlative in vivo 2 photon and focused ion beam scanning electron microscopy of cortical neurons”, PloS one, vol. 8 (2), 2013.PDF icon Technical Report (2.13 MB)
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.
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.
S. Peter, Diego, F., Hamprecht, F. A., and Nadler, B., “Cost-efficient Gradient Boosting”, NIPS, poster. 2017.
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. 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”. 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. Petra, Schnörr, C., and Schröder, A., “Critical Parameter Values and Reconstruction Properties of Discrete Tomography: Application to Experimental Fluid Dynamics”. 2012.
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.PDF icon Technical Report (1.42 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.
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.PDF icon Article (891.47 KB)PDF icon Oral slides (9.17 MB)
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.
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.
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.
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.
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.PDF icon Technical Report (10.06 MB)
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S. Lang and Ommer, B., “Das Objekt jenseits der Digitalisierung”, Das digitale Objekt, vol. 7. 2020.PDF icon lang_ommer_digitalhumanities_2020_.pdf (599.56 KB)
B. Jähne, “Data acquisition by imaging detectors”, Handbook of Experimental Fluid Mechanics. Springer, p. 1419--1436, 2007.
B. Jähne, Klar, M., and Jehle, M., “Data analysis”, Handbook of Experimental Fluid Mechanics. Springer, p. 1437--1491, 2007.
S. Hader, “Data Mining auf multidimensionalen und komplexen Daten in der industriellen Bildverarbeitung”. University of Heidelberg, 2006.
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. Wanner, Meister, S., and Goldlücke, B., “Datasets and Benchmarks for Densely Sampled 4D Light Fields”, in Vision, Modeling & Visualization, 2013, p. 225--226.
F. Rennekamp, “Datenbank gestützte Verwaltung kalibrierter Bildsequenzen zur Qualitätsbewertung von Algorithmen”, Fakultät für Physik und Astronomie Universität Heidelberg, 1998.
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.
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.
F. Becker and Schnörr, C., “Decomposition of Quadratric Variational Problems”, in Pattern Recognition -- 30th DAGM Symposium, 2008, vol. 5096, p. 325--334.PDF icon 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.
M. Haußmann, Hamprecht, F. A., and Kandemir, M., “Deep Active Learning with Adaptive Acquisition”, IJCAI. Proceedings. pp. 2470-2476, 2019.PDF icon Technical Report (137.6 KB)

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