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Publications

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Author Title Type [ Year(Desc)]
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2009
K. Mbock, “A Novel Algorithm for Motion Estimation with Explicit Consideration of Perturbations”, University of Heidelberg, 2009.
2010
D. Singaraju, Rother, C., and Rhemann, C., “New appearance models for natural image matting”, 2010, pp. 659–666.
B. Voss, Heinlein, A., Jähne, B., and Garbe, C. S., “A new approach for 3C3D measurements of aqueous boundary layer flows relative to the wind-wave undulated interface”, in 6th Int. Symp. Gas Transfer at Water Surfaces, Kyoto, May 17--21, 2010, 2010.
B. Voss and Garbe, C. S., “Novel strategy for water sided interfacial 3D3Cflow-visualization using a single camera”, in 14th International Symposium on Flow Visualization, 2010, pp. D1-018.
2012
B. Goldlücke, Strekalovskiy, E., and Cremers, D., “The Natural Vectorial Total Variation which Arises from Geometric Measure Theory”, SIAM Journal on Imaging Sciences, vol. 5, pp. 537-563, 2012.
B. Goldlücke, Strekalovskiy, E., and Cremers, D., “The natural vectorial total variation which arises from geometric measure theory”, SIAM Journal on Imaging Sciences, 2012.
B. Jähne, “Neuerungen zum EMVA Standard 1288, Der Release 3.1 des etablierten Standards zur Kameracharakterisierung”. 2012.
J. Jancsary, Nowozin, S., and Rother, C., “Non-parametric crfs for image labeling”, in NIPS Workshop Modern Nonparametric Methods in Machine Learning, 2012, pp. 1–5.
B. Voss, “Novel Single Camera Techniques for 3D3C Lagrangian Trajectory Measurements in Interfacial Flows”. IWR, Fakultät für Physik und Astronomie, Univ.\ Heidelberg, 2012.
2013
P. Bell, Schlecht, J., and Ommer, B., “Nonverbal Communication in Medieval Illustrations Revisited by Computer Vision and Art History”, Visual Resources Journal, Special Issue on Digital Art History, vol. 29, p. 26--37, 2013.
2014
G. Urban, “Neural Networks: Optimization and Applications”, University of Heidelberg, 2014.
P. Márquez-Neila, Kohli, P., Rother, C., and Baumela, L., “Non-parametric higher-order random fields for image segmentation”, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2014, vol. 8694 LNCS, pp. 269–284.
2015
J. Stapf, “Novel learning-based techniques for dense fluid motion measurements”. IWR, Fakultät für Physik und Astronomie, Univ.\ Heidelberg, 2015.
2016
B. Jähne and Schwarzbauer, M., “Noise equalisation and quasi loss-less image data compression – or how many bits needs an image sensor?”, tm – Technisches Messen, vol. 83, pp. 16–24, 2016.
M. Zisler, Kappes, J. H., Schnörr, C., Petra, S., and Schnörr, C., “Non-Binary Discrete Tomography by Continuous Non-Convex Optimization”, IEEE Comp. Imaging, vol. 2, pp. 335-347, 2016.
2017
N. Krasowki, Beier, T., Knott, G. W., Köthe, U., Hamprecht, F. A., and Kreshuk, A., “Neuron Segmentation with High-Level Biological Priors”, IEEE Transactions on Medical Imaging, vol. 37, no. 4, 2017.
F. Savarino, Hühnerbein, R., Aström, F., Recknagel, J., and Schnörr, C., “Numerical Integration of Riemannian Gradient Flows for Image Labeling”, in Proc. SSVM, 2017, vol. 10302.
2019
E. Brachmann and Rother, C., “Neural-guided RANSAC: Learning where to sample model hypotheses”, in Proceedings of the IEEE International Conference on Computer Vision, 2019, vol. 2019-Octob, pp. 4321–4330.PDF icon PDF (8.02 MB)
A. Ravindran, “Novel Deep Learning-based Instance Segmentation Using Mutex Watershed for Microscopy Cell Images”, Heidelberg University, 2019.
E. Kirschbaum, “Novel Machine Learning Approaches for Neurophysiological Data Analysis”. Heidelberg University, 2019.
2020
R. Rombach, Esser, P., and Ommer, B., “Network Fusion for Content Creation with Conditional INNs”, in CVPRW 2020 (AI for Content Creation), 2020.
R. Rombach, Esser, P., and Ommer, B., “Network-to-Network Translation with Conditional Invertible Neural Networks”, Neural Information Processing Systems (NeurIPS) (Oral). 2020.
P. Esser, Rombach, R., and Ommer, B., “A Note on Data Biases in Generative Models”, in NeurIPS 2020 Workshop on Machine Learning for Creativity and Design, 2020.

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