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Jähne, (1995). Digital Image Processing --- Concepts, Algorithms, And Scientific Applications, 3rd ed. Springer. http://d-nb.info/945151500
Jähne, (1993). Digital Image Processing --- Concepts, Algorithms, And Scientific Applications, 2nd ed. Springer. http://d-nb.info/931193567
Jähne, (1991). Digital Image Processing - Concepts, Algorithms, And Scientific Applications. Springer. http://d-nb.info/911090657
Kandemir, M, Feuchtinger, A, Walch, A and Hamprecht, F A (2014). Digital Pathology: Multiple instance learning can detect Barrett'scancer. ISBI. Proceedings. 1348-1351PDF icon Technical Report (2.86 MB)
Rother, C, Kumar, S, Kolmogorov, V and Blake, A (2005). Digital tapestry [automatic image synthesis]. Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on. 1 589–596. http://research.microsoft.com/ http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=1467321%5Cnhttp://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1467321
Jähne, (2005). Digitale Bildverarbeitung, 6th ed. Springer
Jähne, (2002). Digitale Bildverarbeitung, Fünfte. Springer Verlag. http://d-nb.info/963601830
Jähne, (1997). Digitale Bildverarbeitung, 4th ed. Springer. http://d-nb.info/949866776
Jähne, (1993). Digitale Bildverarbeitung, 3rd ed. Springer. http://d-nb.info/931125731
Jähne, (1991). Digitale Bildverarbeitung, 2nd ed. Springer. http://d-nb.info/910564256
Jähne, (1989). Digitale Bildverarbeitung. Springer. http://d-nb.info/890489467
Jähne, (2012). Digitale Bildverarbeitung Und Bildgewinnung. Springer Vieweg. http://www.springer.com/engineering/signals/book/978-3-642-04951-4
Scharr, H (1996). Digitale Bildverarbeitung Und Papier Texturanalyse Mittels Pyramiden Und Grauwertstatistiken Am Beispiel Der Papierformation. IWR, Fakultät für Physik und Astronomie, Univ.\ Heidelberg
Jehle, M, Jähne, B and Kertzscher, U (2006). Direct estimation of the wall shear rate using parametric motion models in 3D. Proceedings of the 28th DAGM Symposium on Pattern Recognition. 4174 434--443
Fita, E, Damrich, S and Hamprecht, F A (2021). Directed Probabilistic Watershed. NeurIPS. Proceedings. 34PDF icon Technical Report (957.78 KB)
Kirschbaum, E, Bailoni, A and Hamprecht, F A (2020). DISCo: Deep Learning, Instance Segmentation, and Correlations for Cell Segmentation in Calcium Imaging. MICCAI. Proceedings. 151-162
Lerch, K (2006). Discontinuity Preserving Filtering Of Spectral Images. University of Heidelberg
Bruhn, A, Weickert, J, Kohlberger, T and Schnörr, C (2005). Discontinuity-Preserving Computation of Variational Optic Flow in Real-Time. Scale-Space 2005. Springer. 3459 279–290
Damrich, S (2022). Discovering Structure without Labels. Heidelberg University
Lellmann, J, Lellmann, B, Widmann, F and Schnörr, C (2013). Discrete and Continuous Models for Partitioning Problems. Int.~J.~Comp.~Visionz. 104 241-269PDF icon Technical Report (4.74 MB)
Kausler, B X, Schiegg, M, Andres, B, Lindner, M, Köthe, U, Leitte, H, Wittbrodt, J, Hufnagel, L and Hamprecht, F A (2012). A Discrete Chain Graph Model for 3d+t Cell Tracking with High Misdetection Robustness. ECCV 2012. Proceedings. 7574 144-157PDF icon Technical Report (809.07 KB)
Savchynskyy, B (2019). Discrete Graphical Models — An Optimization Perspective. Foundations and Trends® in Computer Graphics and Vision. Now Publishers. 11 160–429
Yuan, J, Schnörr, C and Mémin, E (2007). Discrete Orthogonal Decomposition and Variational Fluid Flow Estimation. J.~Math.~Imag.~Vision. 28 67-80PDF icon Technical Report (752.44 KB)
Yuan, J, Ruhnau, P, Mémin, E and Schnörr, C (2005). Discrete Orthogonal Decomposition and Variational Fluid Flow Estimation. Scale-Space 2005. Springer. 3459 267–278
Zisler, M, Petra, S, Schnörr, C and Schnörr, C (2016). Discrete Tomography by Continuous Multilabeling Subject to Projection Constraints. Proc. GCPR
Schüle, T, Schnörr, C, Weber, S and Hornegger, J (2005). Discrete Tomography By Convex-Concave Regularization and D.C. Programming. Discr. Appl. Math. 151 229-243
Schüle, T, Schnörr, C, Weber, S and Hornegger, J (2003). Discrete Tomography By Convex-Concave Regularization And D.c. Programming. Dept. Math. and Comp. Science, University of Mannheim, Germany
Schmidt, U, Rother, C, Nowozin, S, Jancsary, J and Roth, S (2013). Discriminative Non-Blind Deblurring
Sorrenson, P, Rother, C and Köthe, U (2020). Disentanglement by Nonlinear ICA with General Incompressible-flow Networks (GIN). Intl. Conf. Learning Representations (ICLR). http://arxiv.org/abs/2001.04872PDF icon PDF (2.43 MB)
Esser, P, Rombach, R and Ommer, B (2020). A Disentangling Invertible Interpretation Network for Explaining Latent Representations. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). https://compvis.github.io/iin/PDF icon Article (13.07 MB)
Stahl, A, Ruhnau, P and Schnörr, C (2006). A Distributed Parameter Approach to Dynamic Image Motion. ECCV 2006, International Workshop on The Representation and Use of Prior Knowledge in Vision. LNCS, SpringerPDF icon Technical Report (1.24 MB)
Milbich, T, Roth, K, Bharadhwaj, H, Sinha, S, Bengio, Y, Ommer, B and Cohen, J Paul (2020). DiVA: Diverse Visual Feature Aggregation for Deep Metric Learning. IEEE European Conference on Computer Vision (ECCV). https://arxiv.org/abs/2004.13458
Haubold, C, Uhlmann, V, Unser, M and Hamprecht, F A (2017). Diverse M-best Solutions by Dynamic Programming. GCPR. Proceedings. Springer. LNCS 10496 255-267
Uhlmann, V, Haubold, C, Hamprecht, F A and Unser, M (2017). Diverse Shortest Paths for Bioimage Analysis. Bioinformatics. 1-3
Sanakoyeu, A, Tschernezki, V, Büchler, U and Ommer, B (2019). Divide and Conquer the Embedding Space for Metric Learning. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). https://github.com/CompVis/metric-learning-divide-and-conquer

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