Biblio

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G
Grunwald, M., Müller J., Schall M., Laube P., Umlauf G., & Franz M. O. (2015).  Pixel-wise Hybrid Image Registration on Wood Decors. BW-CAR| SINCOM. 24.PDF icon Grunwald_2015_Pixel-wiseHybridImageRegistration.pdf (2.42 MB)
Grunwald, M., Hermann M., Freiberg F., & Franz M. O. (2018).  Optical Surface Detection: A novelty detection approach based on CNN-encoded features. SPIE Optics and Photonics. 10752 - 10752 - 13.PDF icon Spie2018.pdf (730 KB)
Grunwald, M., Gansloser J., & Franz M. O. (2016).  Radiometric calibration of digital cameras using sparse Gaussian processes. Workshop Farbbildverarbeitung.
Grunwald, M., Laube P., Schall M., Umlauf G., & Franz M. O. (2017).  Radiometric calibration of digital cameras using neural networks. Optics and Photonics for Information Processing XI.
H
Hake, F., Hermann M., Alkhatib H., Hesse C., Holste K., Umlauf G., et al. (2020).  Damage Detection for Port Infrastructure by Means of Machine-Learning-Algorithms. FIG Working Week 2020. PDF icon Fig2020.pdf (876.57 KB)
Hensler, J., Denker K., Franz M. O., & Umlauf G. (2011).  Hybrid face recognition based on real-time multi-camera stereo-matching. (G. al., B. et, Ed.).Advances in Visual Computing, Proc. ISVC 2011, LNCS. 158–167.PDF icon Hensler et al._2011_Hybrid face recognition based on real-time multi-camera stereo-matching.pdf (439.11 KB)
Hermann, M., Goldlücke B., & Franz M. O. (2022).  Image novelty detection based on mean-shift and typical set size. 21th International Conference on Image Analysis and Processing, ICIAP. PDF icon ICIAP-mean-shift-novelty-detection-preprint.pdf (2.96 MB)
Hermann, M., Madrid N., & Seepold R. (2017).  Detection of variations in holter ECG recordings based on dynamic cluster analysis. International Conference on Intelligent Decision Technologies.
Hermann, M., Griesser D., Gundel B., Dold D., Umlauf G., & Franz M. O. (2022).  Targetless Lidar-camera registration using patch-wise mutual information. International Conference on Information Fusion. PDF icon mir_reg_patch.pdf (9.58 MB)
Hermann, M., Umlauf G., Goldlücke B., & Franz M. O. (2023).  Incremental one-class learning using regularized null-space training for industrial defect detection. 16th International Conference on Machine Vision (ICMV).
Hermann, M., Umlauf G., Goldlücke B., & Franz M. O. (2022).  Fast and efficient image novelty detection based on mean-shifts. Sensors | Unusual Behavior Detection Based on Machine Learning .
Hermann, M., Umlauf G., & Franz M. O. (2022).  Large-scale independent component analysis by speeding up Lie group techniques. International Conference on Acoustics, Speech, and Signal Processing, ICASSP. PDF icon conference_101719.pdf (646.58 KB)
Hermann, M., Umlauf G., & Franz M. O. (2022).  Fast and memory-efficient independent component analysis using Lie group techniques. International Conference on Curves and Surfaces.
Hermann, M., Dold D., Umlauf G., & Dürr O. (2022).  DeepDoubt - Improving uncertainty measures in machine learning to improve explainability and transparency. 2022 All-Hands-Meeting of the BMBF-funded AI Research Projects at Munich Center for Machine Learning. PDF icon AHM2022_DeepDoubt.pdf (238.98 KB)
Herzog, L., Kook L., Götschi A., Petermann K., Hänsel M., Hamann J., et al. (2023).  Deep transformation models for functional outcome prediction after acute ischemic stroke. Biometrical Journal. 65, 2100379.
Herzog, L., Murina E., Dürr O., Wegener S., & Sick B. (2020).  Integrating uncertainty in deep neural networks for MRI based stroke analysis. Medical Image Analysis. 65, 101790.
Heyse, S., Brodte A., Bruttger O., Duerr O., Freeman T., Jung T., et al. (2005).  Quantifying bioactivity on a large scale: quality assurance and analysis of multiparametric ultra-HTS data. JALA: Journal of the Association for Laboratory Automation. 10, 207–212.
Hoher, P., Baur T., Reuter J., Griesser D., Govaers F., & Koch W. (2024).  3D-Extended Object Tracking and Shape Classification with a Lidar Sensor using Random Matrices and Virtual Measurement Models. 27th International Conference on Information Fusion (FUSION). 1-8.
Hoher, P., Reuter J., Dold D., Griesser D., Govaers F., & Koch W. (2023).  Extended Target Tracking With a Lidar Sensor Using Random Matrices and a Gaussian Processes Regression Model. International Conference on Information Fusion (FUSION). 1-8.
Homburger, H., Wirtensohn S., Hoher P., Baur T., Griesser D., Diehl M., et al. (2025).  Solgenia—A test vessel toward energy-efficient autonomous water taxi applications. Ocean Engineering.
Hörtling, S., Dold D., Dürr O., & Sick B. (2021).  Transformation models for flexible posteriors in variational bayes. arXiv preprint. 2106.00528.PDF icon 2106.00528.pdf (1.03 MB)
Huber, S., Franz M. O., & Bülthoff H. H. (1999).  On robots and flies: Modeling the visual orientation behavior of flies. Robotics and Autonomous Systems. 29, 227–242.PDF icon Huber, Franz, Bülthoff_1999_On robots and flies Modeling the visual orientation behavior of flies.pdf (473.13 KB)
I
Ilg, W., Bakır G. H., Franz M. O., & Giese M. A. (2003).  Hierarchical spatio-temporal morphable models for representation of complex movements for imitation learning. (Nunes, U., de Almeida A., Bejczy A., Kosuge K., & Machado J., Ed.).{Proc. of the 11th International Conference on Advanced Robotics}. 2, 453–458.PDF icon Ilg et al._2003_Hierarchical spatio-temporal morphable models for representation of complex movements for imitation learning.pdf (716.98 KB)
Ilg, W., Bakır G. H., Franz M. O., & Giese M. (2003).  A representation of complex movement sequences based on hierarchical spatio-temporal correspondence for imitation learning in robotics. (Bülthoff, H. H., Gegenfurtner K. R., Mallot H. A., Ulrich R., & Wichmann F. A., Ed.).{Proc. 6. Tübinger Wahrnehmungskonferenz (TWK 2003)}. 74.
K
Kibii, J. E., Dreher A., Wormser P. L., & Gimpel H. (2022).  Design and Calibration of Plane Mirror Setups for Mobile Robots with a 2D-Lidar. Sensors. 22, 7830.

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