TY - JOUR A1 - Johanning, M. A1 - Braun, Alexander A1 - Timoney, N. A1 - Elman, V. A1 - Neuhauser, W. A1 - Wunderlich, Chr T1 - Individual addressing of trapped ions and coupling of motional and spin states using RF radiation JF - Physical review letters N2 - Individual electrodynamically trapped and laser cooled ions are addressed in frequency space using radio-frequency radiation in the presence of a static magnetic field gradient. In addition, an interaction between motional and spin states induced by an rf field is demonstrated employing rf optical double resonance spectroscopy. These are two essential experimental steps towards realizing a novel concept for implementing quantum simulations and quantum computing with trapped ions. Y1 - 2009 UR - http://www.ncbi.nlm.nih.gov/pubmed/19257664 U6 - https://doi.org/10.1103/PhysRevLett.102.073004 SN - 1079-7114 VL - 102 IS - 7 PB - American Physical Society ER - TY - JOUR A1 - Johanning, M. A1 - Braun, Alexander A1 - Eiteneuer, D. A1 - Paape, C. A1 - Balzer, C. A1 - Neuhauser, W. A1 - Wunderlich, C. T1 - Resonance-enhanced isotope-selective photoionization of YbI for ion trap loading JF - Applied Physics B Y1 - 2011 UR - https://arxiv.org/pdf/0712.0969.pdf U6 - https://doi.org/10.1007/s00340-011-4502-7 SN - 1432-0649 VL - 103 IS - 2 SP - 327 EP - 338 PB - Springer Nature ER - TY - JOUR A1 - Wittpahl, Christian A1 - Zakour, Hatem Ben A1 - Lehmann, Matthias A1 - Braun, Alexander T1 - Realistic Image Degradation with Measured PSF JF - Electronic Imaging, Autonomous Vehicles and Machines 2018 KW - SiL/HiL Y1 - 2018 U6 - https://doi.org/10.2352/ISSN.2470-1173.2018.17.AVM-149 SN - 2470-1173 IS - 17 PB - Society for Imaging Science and Technology ER - TY - JOUR A1 - Krebs, Christian A1 - Müller, Patrick A1 - Braun, Alexander T1 - Impact of Windshield Optical Aberrations on Visual Range Camera Based Classification Tasks Performed by CNNs JF - London Imaging Meeting Y1 - 2021 SN - 0-89208-346-6 U6 - https://doi.org/10.2352/issn.2694-118X.2021.LIM-83 SN - 2694-118X VL - 2021 IS - 1 SP - 83 EP - 87 PB - Ingenta connect ER - TY - JOUR A1 - Brummel, Mattis A1 - Müller, Patrick A1 - Braun, Alexander T1 - Spatial precision and recall indices to assess the performance of instance segmentation algorithms JF - Electronic Imaging KW - Space-Variance KW - Computer Vision KW - IoU KW - image perturbations Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:due62-opus-41552 SN - 2470-1173 VL - 34 IS - 16 SP - 1 EP - 6 PB - Society for Imaging Science and Technology ER - TY - JOUR A1 - Wohlers, Luis Constantin A1 - Müller, Patrick A1 - Braun, Alexander T1 - Original image noise reconstruction for spatially-varying filtered driving scenes JF - Electronic Imaging: Society for Imaging Science and Technology Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:due62-opus-41479 SN - 2470-1173 VL - 34 IS - 16 SP - 1 EP - 7 PB - Society for Imaging Sciences and Technology ER - TY - JOUR A1 - Wolf, Dominik Werner A1 - Ulrich, Markus A1 - Braun, Alexander T1 - Novel developments of refractive power measurement techniques in the automotive world JF - Metrologia N2 - Refractive power measurements serve as the primary quality standard in the automotive glazing industry. In the light of autonomous driving new optical metrics are becoming more and more popular for specifying optical quality requirements for the windshield. Nevertheless, the link between those quantities and the refractive power needs to be established in order to ensure a holistic requirement profile for the windshield. As a consequence, traceable high-resolution refractive power measurements are still required for the glass quality assessment. Standard measurement systems using Moiré patterns for refractive power monitoring in the automotive industry are highly resolution limited, wherefore they are insufficient for evaluating the camera window area. Consequently, there is a need for more sophisticated refractive power measurement systems that provide a higher spatial resolution. In addition, a calibration procedure has to be developed in order to guarantee for comparability of the measurement results. For increasing the resolution, a measurement setup based on an auto-correlation algorithm is tested in this paper. Furthermore, a calibration procedure is established by using a single reference lens with a nominal refractive power of 100 km-1. For the calibration of the entire measurement range of the system, the lens is tilted by an inclination angle orthogonal to the optical axis. The effective refractive power is then given by the Kerkhof model. By adopting the measurement and calibration procedure presented in this paper, glass suppliers in the automotive industry will be able to detect relevant manufacturing defects within the camera window area more accurately paving the way for a holistic quality assurance of the windshield for future advanced driver-assistance system (ADAS) functionalities. Concurrently, the traceability of the measurement results is ensured by establishing a calibration chain based on a single reference lens, which is traced back to international standards. Y1 - 2023 UR - http://iopscience.iop.org/article/10.1088/1681-7575/acf1a4 U6 - https://doi.org/10.1088/1681-7575/acf1a4 SN - 1681-7575 IS - ACCEPTED MANUSCRIPT PB - IOP Publishing ER - TY - JOUR A1 - Müller, Patrick A1 - Braun, Alexander T1 - MTF as a performance indicator for AI algorithms? JF - Electronic Imaging: Society for Imaging Science and Technology N2 - Abstract The modulation-transfer function (MTF) is a fundamental optical metric to measure the optical quality of an imaging system. In the automotive industry it is used to qualify camera systems for ADAS/AD. Each modern ADAS/AD system includes evaluation algorithms for environment perception and decision making that are based on AI/ML methods and neural networks. The performance of these AI algorithms is measured by established metrics like Average Precision (AP) or precision-recall-curves. In this article we research the robustness of the link between the optical quality metric and the AI performance metric. A series of numerical experiments were performed with object detection and instance segmentation algorithms (cars, pedestrians) evaluated on image databases with varying optical quality. We demonstrate with these that for strong optical aberrations a distinct performance loss is apparent, but that for subtle optical quality differences – as might arise during production tolerances – this link does not exhibit a satisfactory correlation. This calls into question how reliable the current industry practice is where a produced camera is tested end-of-line (EOL) with the MTF, and fixed MTF thresholds are used to qualify the performance of the camera-under-test. KW - AI performance metrics KW - Artificial Intelligence KW - Modulation Transfer Function (MTF) KW - Optical Quality KW - Perception Y1 - 2023 U6 - https://doi.org/10.2352/EI.2023.35.16.AVM-125 SN - 2470-1173 VL - 35 IS - 16 SP - 1 EP - 7 PB - Society for Imaging Sciences and Technology ER - TY - JOUR A1 - Müller, Patrick A1 - Brummel, Mattis A1 - Braun, Alexander T1 - Spatial recall index for machine learning algorithms JF - London Imaging Meeting N2 - We present a novel metric Spatial Recall Index to assess the performance of machine-learning (ML) algorithms for automotive applications, focusing on where in the image which performance occurs. Typical metrics like intersection-over-union (IoU), precisionrecallcurves or average precision (AP) quantify the performance over a whole database of images, neglecting spatial performance variations. But as the optics of camera systems are spatially variable over the field of view, the performance of ML-based algorithms is also a function of space, which we show in simulation: A realistic objective lens based on a Cooke-triplet that exhibits typical optical aberrations like astigmatism and chromatic aberration, all variable over field, is modeled. The model is then applied to a subset of the BDD100k dataset with spatially-varying kernels. We then quantify local changes in the performance of the pre-trained Mask R-CNN algorithm. Our examples demonstrate the spatial dependence of the performance of ML-based algorithms from the optical quality over field, highlighting the need to take the spatial dimension into account when training ML-based algorithms, especially when looking forward to autonomous driving applications. Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:due62-opus-35657 SN - 0-89208-346-6 VL - 2021 IS - 1 SP - 58 EP - 62 PB - Society for Imaging Science and Technology ER - TY - JOUR A1 - Wittpahl, Christian A1 - Deegan, Brian A1 - Black, Bob A1 - Braun, Alexander T1 - An analytic-numerical image flicker study to test novel flicker metrics JF - Electronic Imaging: Society for Imaging Science and Technology Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:due62-opus-35671 SN - 2470-1173 IS - 17 SP - 1 EP - 8 PB - Society for Imaging Science and Technology ER - TY - JOUR A1 - Sieberichs, Christian A1 - Geerkens, Simon A1 - Braun, Alexander A1 - Waschulzik, Thomas T1 - ECS: an interactive tool for data quality assurance JF - AI and Ethics N2 - 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. KW - DEAL KW - HSD Publikationsfonds KW - Data visulization KW - Distance based KW - Data quality assurance KW - Equivalence class sets KW - DFG Publikationskosten Y1 - 2024 U6 - https://doi.org/10.1007/s43681-023-00393-3 SN - 2730-5961 N1 - Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - 532148125 and supported by the central publication fund of Hochschule Düsseldorf University of Applied Sciences Data availability declaration: All data that support the findings of this scientific publication are available within this paper and/or its supplementary information files/materials. Otherwise: The data that support the findings of this scientific publication are available from the corresponding author upon reasonable request. PB - Springer Nature ER - TY - JOUR A1 - Molloy, Dara A1 - Müller, Patrick A1 - Deegan, Brian A1 - Mullins, Darragh A1 - Horgan, Jonathan A1 - Ward, Enda A1 - Jones, Edward A1 - Braun, Alexander A1 - Glavin, Martin T1 - Analysis of the Impact of Lens Blur on Safety-Critical Automotive Object Detection JF - IEEE Access N2 - Camera-based object detection is widely used in safety-critical applications such as advanced driver assistance systems (ADAS) and autonomous vehicle research. Road infrastructure has been designed for human vision, so computer vision, with RGB cameras, is a vital source of semantic information from the environment. Sensors, such as LIDAR and RADAR, are also often utilized for these applications; however, cameras provide a higher spatial resolution and color information. The spatial frequency response (SFR), or sharpness of a camera, utilized in object detection systems must be sufficient to allow a detection algorithm to localize objects in the environment over its lifetime reliably. This study explores the relationship between object detection performance and SFR. Six state-of-the-art object detection models are evaluated with varying levels of lens defocus. A novel raw image dataset is created and utilized, containing pedestrians and cars over a range of distances up to 100-m from the sensor. Object detection performance for each defocused dataset is analyzed over a range of distances to determine the minimum SFR necessary in each case. Results show that the relationship between object detection performance and lens blur is much more complex than previous studies have found due to lens field curvature, chromatic aberration, and astigmatisms. We have found that smaller objects are disproportionately impacted by lens blur, and different object detection models have differing levels of robustness to lens blur KW - DOAJ KW - Object detection KW - intelligent transportation system KW - autonomous vehicles KW - ADAS KW - lens blur Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:due62-opus-42999 SN - 2169-3536 VL - 12 SP - 3554 EP - 3569 PB - IEEE ER - TY - JOUR A1 - Geerkens, Simon A1 - Sieberichs, Christian A1 - Braun, Alexander A1 - Waschulzik, Thomas T1 - QI²: an interactive tool for data quality assurance JF - AI and Ethics N2 - 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. KW - Performance metrics KW - HSD Publikationsfonds KW - DEAL KW - DFG Publikationskosten KW - Machine learning KW - Quality assurance KW - Data integrity KW - Data quality Y1 - 2024 U6 - https://doi.org/10.1007/s43681-023-00390-6 SN - 2730-5961 N1 - Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - 532148125 and supported by the central publication fund of Hochschule Düsseldorf University of Applied Sciences Data availability declaration: The data that support the findings of this scientific publication are available from the corresponding author upon reasonable request. PB - Springer Nature ER -