Publications

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C
Lellmann, J, Kappes, J H, Yuan, J, Becker, F, Schnörr, C, Mórken, K and Lysaker, M (2009). Convex Multi-Class Image Labeling by Simplex-Constrained Total Variation. Scale Space and Variational Methods in Computer Vision (SSVM 2009). Springer. 5567 150-162
Lellmann, J, Becker, F and Schnörr, C (2009). Convex Optimization for Multi-Class Image Labeling with a Novel Family of Total Variation Based Regularizers. Proceedings of the IEEE Conference on Computer Vision (ICCV 09) Kyoto, Japan. 646-653
Lellmann, J, Becker, F and Schnörr, C (2009). Convex Optimization for Multi-Class Image Labeling with a Novel Family of Total Variation Based Regularizers. IEEE International Conference on Computer Vision (ICCV). 646 -- 653PDF icon Technical Report (930.18 KB)
Silvestri, F, Reinelt, G and Schnörr, C (2015). A Convex Relaxation Approach to the Affine Subspace Clustering Problem. Proc.~GCPRPDF icon Technical Report (878.63 KB)
Keuchel, J, Schellewald, C, Cremers, D and Schnörr, C (2001). Convex Relaxations for Binary Image Partitioning and Perceptual Grouping. Mustererkennung 2001. Springer. 2191 353--360
Yuan, J, Schnörr, C, Kohlberger, T and Ruhnau, P (2004). Convex Set-Based Estimation of Image Flows. ICPR 2004 -- 17th Int.~Conf.~on Pattern Recognition. IEEE. 1 124-127
Swoboda, P and Schnörr, C (2013). Convex Variational Image Restoration with Histogram Priors. SIAM J.~Imag.~Sci. 6 1719-1735PDF icon Technical Report (553.54 KB)
Schnörr, (1996). Convex Variational Segmentation of Multi-Channel Images. Proc. 12th Int. Conf. on Analysis and Optimization of Systems: Images, Wavelets and PDE's. Springer-Verlag. 219
Hering, M, Körner, K and Jähne, B (2009). Correlated speckle noise in white-light interferometry: theoretical analysis of measurement uncertainty. Appl. Optics. 48 525--538
Krause, G (2017). Correlation Of Performance And Entropy In Active Learning With Convolutional Neural Networks. Heidelberg University
Maco, B, Holtmaat, A, Cantoni, M, Kreshuk, A, Straehle, C N, Hamprecht, F A and Knott, G W (2013). Correlative in vivo 2 photon and focused ion beam scanning electron microscopy of cortical neurons. PloS one. 8 (2)PDF icon Technical Report (2.13 MB)
Peter, S, Diego, F, Hamprecht, F A and Nadler, B (2017). Cost-efficient Gradient Boosting. NIPS, poster
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}. http://dx.doi.org/10.1007/978-3-319-50835-1_8
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
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
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)
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
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)
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
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
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)
D
Jähne, (2007). Data acquisition by imaging detectors. Handbook of Experimental Fluid Mechanics. Springer. 1419--1436
Jähne, B, Klar, M and Jehle, M (2007). Data analysis. Handbook of Experimental Fluid Mechanics. Springer. 1437--1491
Hader, S (2006). Data Mining auf multidimensionalen und komplexen Daten in der industriellen Bildverarbeitung. University of Heidelberg
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
Wanner, S, Meister, S and Goldlücke, B (2013). Datasets and Benchmarks for Densely Sampled 4D Light Fields. Vision, Modeling & Visualization. 225--226
Rennekamp, F (1998). Datenbank Gestützte Verwaltung Kalibrierter Bildsequenzen Zur Qualitätsbewertung Von Algorithmen. Fakultät für Physik und Astronomie Universität Heidelberg
Becker, F and Schnörr, C (2008). Decomposition of Quadratric Variational Problems. Pattern Recognition -- 30th DAGM Symposium. 5096 325--334
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)
Haußmann, M, Hamprecht, F A and Kandemir, M (2019). Deep Active Learning with Adaptive Acquisition. IJCAI. Proceedings, in press
Cerrone, L (2018). Deep End-To-End Learning Of A Diffusion Process For Seeded Image Segmentation. 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)
Schmidt, P (2016). Deep Learning For Bioimage Analysis. University of Heidelberg
Balles, L (2016). Deep Learning For Diabetic Retinopathy Diagnostics. University of Heidelberg

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