@article{JohanningBraunEiteneueretal.2011, author = {Johanning, M. and Braun, Alexander and Eiteneuer, D. and Paape, C. and Balzer, Christoph and Neuhauser, W. and Wunderlich, C.}, title = {Resonance-enhanced isotope-selective photoionization of YbI for ion trap loading}, series = {Applied Physics B}, volume = {103}, journal = {Applied Physics B}, number = {2}, publisher = {Springer Nature}, issn = {1432-0649}, doi = {10.1007/s00340-011-4502-7}, pages = {327 -- 338}, year = {2011}, language = {en} } @unpublished{MuellerBraunKeuper2025, author = {M{\"u}ller, Patrick and Braun, Alexander and Keuper, Margret}, title = {Examining the Impact of Optical Aberrations to Image Classification and Object Detection Models}, edition = {v1}, publisher = {arXiv}, doi = {10.48550/arXiv.2504.18510}, url = {http://nbn-resolving.de/urn:nbn:de:hbz:due62-opus-55457}, pages = {32}, year = {2025}, abstract = {Deep neural networks (DNNs) have proven to be successful in various computer vision applications such that models even infer in safety-critical situations. Therefore, vision models have to behave in a robust way to disturbances such as noise or blur. While seminal benchmarks exist to evaluate model robustness to diverse corruptions, blur is often approximated in an overly simplistic way to model defocus, while ignoring the different blur kernel shapes that result from optical systems. To study model robustness against realistic optical blur effects, this paper proposes two datasets of blur corruptions, which we denote OpticsBench and LensCorruptions. OpticsBench examines primary aberrations such as coma, defocus, and astigmatism, i.e. aberrations that can be represented by varying a single parameter of Zernike polynomials. To go beyond the principled but synthetic setting of primary aberrations, LensCorruptions samples linear combinations in the vector space spanned by Zernike polynomials, corresponding to 100 real lenses. Evaluations for image classification and object detection on ImageNet and MSCOCO show that for a variety of different pre-trained models, the performance on OpticsBench and LensCorruptions varies significantly, indicating the need to consider realistic image corruptions to evaluate a model's robustness against blur.}, subject = {Maschinelles Lernen}, language = {de} } @unpublished{WolfBalajiBraunetal.2024, author = {Wolf, Dominik Werner and Balaji, Prasannavenkatesh and Braun, Alexander and Ulrich, Markus}, title = {Decoupling of neural network calibration measures}, series = {German Conference on Pattern Recognition (GCPR) 2024}, journal = {German Conference on Pattern Recognition (GCPR) 2024}, publisher = {arXiv}, doi = {10.48550/arXiv.2406.02411}, url = {http://nbn-resolving.de/urn:nbn:de:hbz:due62-opus-50104}, year = {2024}, abstract = {A lot of effort is currently invested in safeguarding autonomous driving systems, which heavily rely on deep neural networks for computer vision. We investigate the coupling of different neural network calibration measures with a special focus on the Area Under the Sparsification Error curve (AUSE) metric. We elaborate on the well-known inconsistency in determining optimal calibration using the Expected Calibration Error (ECE) and we demonstrate similar issues for the AUSE, the Uncertainty Calibration Score (UCS), as well as the Uncertainty Calibration Error (UCE). We conclude that the current methodologies leave a degree of freedom, which prevents a unique model calibration for the homologation of safety-critical functionalities. Furthermore, we propose the AUSE as an indirect measure for the residual uncertainty, which is irreducible for a fixed network architecture and is driven by the stochasticity in the underlying data generation process (aleatoric contribution) as well as the limitation in the hypothesis space (epistemic contribution).}, language = {en} } @article{JakabBarthelBraunetal.2025, author = {Jakab, Daniel and Barthel, Julian and Braun, Alexander and Mohandas, Reenu and Deegan, Brian Michael and Kumbham, Mahendar and Molloy, Dara and Collins, Fiachra and Scanlan, Anthony and Eising, Ciar{\´a}n}, title = {SOLAS: Superpositioning an Optical Lens in Automotive Simulation}, series = {Electronic Imaging}, volume = {37}, journal = {Electronic Imaging}, number = {15}, publisher = {Society for Imaging Science \& Technology}, issn = {2470-1173}, doi = {10.2352/EI.2025.37.15.AVM-101}, pages = {101 -- 1}, year = {2025}, language = {en} } @article{MuellerBraunKeuper2025, author = {M{\"u}ller, Patrick and Braun, Alexander and Keuper, Margret}, title = {Examining the Impact of Optical Aberrations to Image Classification and Object Detection Models}, series = {IEEE Transactions on Pattern Analysis and Machine Intelligence}, journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence}, publisher = {IEEE}, issn = {0162-8828}, doi = {10.1109/TPAMI.2025.3622234}, pages = {1 -- 15}, year = {2025}, language = {en} } @unpublished{WolfBraunUlrich2024, author = {Wolf, Dominik Werner and Braun, Alexander and Ulrich, Markus}, title = {Optical aberrations in autonomous driving: Physics-informed parameterized temperature scaling for neural network uncertainty calibration}, series = {International Journal of Computer Vision (IJCV)}, journal = {International Journal of Computer Vision (IJCV)}, publisher = {arXiv}, doi = {10.48550/arXiv.2412.13695}, url = {http://nbn-resolving.de/urn:nbn:de:hbz:due62-opus-50120}, year = {2024}, abstract = {'A trustworthy representation of uncertainty is desirable and should be considered as a key feature of any machine learning method' (Huellermeier and Waegeman, 2021). This conclusion of Huellermeier et al. underpins the importance of calibrated uncertainties. Since AI-based algorithms are heavily impacted by dataset shifts, the automotive industry needs to safeguard its system against all possible contingencies. One important but often neglected dataset shift is caused by optical aberrations induced by the windshield. For the verification of the perception system performance, requirements on the AI performance need to be translated into optical metrics by a bijective mapping (Braun, 2023). Given this bijective mapping it is evident that the optical system characteristics add additional information about the magnitude of the dataset shift. As a consequence, we propose to incorporate a physical inductive bias into the neural network calibration architecture to enhance the robustness and the trustworthiness of the AI target application, which we demonstrate by using a semantic segmentation task as an example. By utilizing the Zernike coefficient vector of the optical system as a physical prior we can significantly reduce the mean expected calibration error in case of optical aberrations. As a result, we pave the way for a trustworthy uncertainty representation and for a holistic verification strategy of the perception chain.}, language = {en} } @unpublished{GeerkensSieberichsBraunetal.2023, author = {Geerkens, Simon and Sieberichs, Christian and Braun, Alexander and Waschulzik, Thomas}, title = {QI2 -- an Interactive Tool for Data Quality Assurance}, publisher = {arXiv}, year = {2023}, abstract = {The importance of high data quality is increasing with the growing impact and distribution of ML systems and big data. Also the planned AI Act from the European commission defines challenging legal requirements for data quality especially for the market introduction of safety relevant ML systems. In this paper we introduce a novel approach that supports the data quality assurance process of multiple data quality aspects. This approach enables the verification of quantitative data quality requirements. The concept and benefits are introduced and explained on small example data sets. How the method is applied is demonstrated on the well known MNIST data set based an handwritten digits.}, language = {en} } @unpublished{SieberichsGeerkensBraunetal.2023, author = {Sieberichs, Christian and Geerkens, Simon and Braun, Alexander and Waschulzik, Thomas}, title = {ECS -- an Interactive Tool for Data Quality Assurance}, publisher = {arXiv}, doi = {10.48550/arXiv.2307.04368}, year = {2023}, abstract = {With the increasing capabilities of machine learning systems and their potential use in safety-critical systems, ensuring high-quality data is becoming increasingly important. In this paper we present a novel approach for the assurance of data quality. For this purpose, the mathematical basics are first discussed and the approach is presented using multiple examples. This results in the detection of data points with potentially harmful properties for the use in safety-critical systems.}, language = {en} } @article{GeerkensSieberichsBraunetal.2024, author = {Geerkens, Simon and Sieberichs, Christian and Braun, Alexander and Waschulzik, Thomas}, title = {QI²: an interactive tool for data quality assurance}, series = {AI and Ethics}, volume = {4}, journal = {AI and Ethics}, publisher = {Springer Nature}, issn = {2730-5961}, doi = {10.1007/s43681-023-00390-6}, url = {http://nbn-resolving.de/urn:nbn:de:hbz:due62-opus-42945}, pages = {141 -- 149}, year = {2024}, abstract = {The importance of high data quality is increasing with the growing impact and distribution of ML systems and big data. Also, the planned AI Act from the European commission defines challenging legal requirements for data quality especially for the market introduction of safety relevant ML systems. In this paper, we introduce a novel approach that supports the data quality assurance process of multiple data quality aspects. This approach enables the verification of quantitative data quality requirements. The concept and benefits are introduced and explained on small example data sets. How the method is applied is demonstrated on the well-known MNIST data set based an handwritten digits.}, language = {en} } @article{SieberichsGeerkensBraunetal.2024, author = {Sieberichs, Christian and Geerkens, Simon and Braun, Alexander and Waschulzik, Thomas}, title = {ECS: an interactive tool for data quality assurance}, series = {AI and Ethics}, volume = {4}, journal = {AI and Ethics}, publisher = {Springer Nature}, issn = {2730-5961}, doi = {10.1007/s43681-023-00393-3}, url = {http://nbn-resolving.de/urn:nbn:de:hbz:due62-opus-42908}, pages = {131 -- 139}, year = {2024}, abstract = {With the increasing capabilities of machine learning systems and their potential use in safety-critical systems, ensuring high-quality data is becoming increasingly important. In this paper, we present a novel approach for the assurance of data quality. For this purpose, the mathematical basics are first discussed and the approach is presented using multiple examples. This results in the detection of data points with potentially harmful properties for the use in safety-critical systems.}, language = {en} } @article{JakabBraunAgnewetal.2024, author = {Jakab, Daniel and Braun, Alexander and Agnew, Cathaoir and Mohandas, Reenu and Deegan, Brian Michael and Molloy, Dara and Ward, Enda and Scanlan, Anthony and Eising, Ciar{\´a}n}, title = {SS-SFR: synthetic scenes spatial frequency response on Virtual KITTI and degraded automotive simulations for object detection}, series = {IET Conference Proceedings}, volume = {2024}, journal = {IET Conference Proceedings}, number = {10}, publisher = {Institution of Engineering and Technology (IET)}, issn = {2732-4494}, doi = {10.1049/icp.2024.3292}, url = {http://nbn-resolving.de/urn:nbn:de:hbz:due62-opus-50080}, pages = {110 -- 117}, year = {2024}, language = {en} }