Biblio

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Author Title Type [ Year(Desc)]
Filters: Author is Matthias Hermann  [Clear All Filters]
2018
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)
2020
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)
2021
Grunwald, M., Hermann M., Freiberg F., & Franz M. O. (2021).  Biologically-inspired vs. CNN texture representations in novelty detection. Applications of Machine Learning 2021. 118430I.PDF icon Spie2021.pdf (5.33 MB)
2022
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)
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).  Fast and memory-efficient independent component analysis using Lie group techniques. International Conference on Curves and Surfaces.
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., 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., 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)
2023
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).