TY - CHAP A1 - Kornmesser, Christoph A1 - Braun, Alexander A1 - Beushausen, Volker T1 - Applicability of different fluorescent dyes for 2D temperature measurements in dense sprays using planar laser induced fluorescence T2 - Proceedings of the 17th Annual Conference on Liquid Atomization and Spray Systems, 02.06.2001, Zürich KW - Deconvolution Y1 - 2001 UR - https://www.ilasseurope.org/ICLASS/ilass2001/ILASS2001.pdf SN - 3952224413 SP - 554 EP - 559 PB - ETH Zürich CY - Zürich 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 - CHAP A1 - Mueller, Patrick A1 - Lehmann, Matthias A1 - Braun, Alexander T1 - Simulating tests to test simulation T2 - IS&T International Symposium on Electronic Imaging 2020: Autonomous Vehicles and Machines, 26 January 2020 — 30 January 2020, Burlingame, CA, USA Y1 - 2020 U6 - https://doi.org/10.2352/ISSN.2470-1173.2020.16.AVM-149 VL - 2020 IS - 16 PB - Society for Imaging Science 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 - Tseng, Ethan A1 - Mosleh, Ali A1 - Mannan, Fahim A1 - St-Arnaud, Karl A1 - Sharma, Avinash A1 - Peng, Yifan A1 - Braun, Alexander A1 - Nowrouzezahrai, Derek A1 - Lalonde, Jean-François A1 - Heide, Felix T1 - Differentiable Compound Optics and Processing Pipeline Optimization for End-to-end Camera Design JF - ACM Transactions on Graphics N2 - Most modern commodity imaging systems we use directly for photography‐-or indirectly rely on for downstream applications‐-employ optical systems of multiple lenses that must balance deviations from perfect optics, manufacturing constraints, tolerances, cost, and footprint. Although optical designs often have complex interactions with downstream image processing or analysis tasks, today’s compound optics are designed in isolation from these interactions. Existing optical design tools aim to minimize optical aberrations, such as deviations from Gauss’ linear model of optics, instead of application-specific losses, precluding joint optimization with hardware image signal processing (ISP) and highly parameterized neural network processing. In this article, we propose an optimization method for compound optics that lifts these limitations. We optimize entire lens systems jointly with hardware and software image processing pipelines, downstream neural network processing, and application-specific end-to-end losses. To this end, we propose a learned, differentiable forward model for compound optics and an alternating proximal optimization method that handles function compositions with highly varying parameter dimensions for optics, hardware ISP, and neural nets. Our method integrates seamlessly atop existing optical design tools, such as Zemax. We can thus assess our method across many camera system designs and end-to-end applications. We validate our approach in an automotive camera optics setting‐-together with hardware ISP post processing and detection‐-outperforming classical optics designs for automotive object detection and traffic light state detection. For human viewing tasks, we optimize optics and processing pipelines for dynamic outdoor scenarios and dynamic low-light imaging. We outperform existing compartmentalized design or fine-tuning methods qualitatively and quantitatively, across all domain-specific applications tested. Y1 - 2021 U6 - https://doi.org/10.1145/3446791 SN - 1557-7368 VL - 40 IS - 2 SP - 1 EP - 19 PB - Association for Computing Machinery CY - New York, NY, USA 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 - 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 - THES A1 - Braun, Alexander T1 - Addressing Single Yb+ Ions: A new scheme for quantum computing in linear ion traps Y1 - 2007 SN - 978-3-86727-276-6 PB - Cuvillier Verlag CY - Göttingen ER - TY - CHAP A1 - Balzer, Christoph A1 - Hannemann, Th. A1 - Wunderlich, Chr. A1 - Braun, Alexander A1 - Ettler, M. A1 - Paape, Chr. A1 - Neuhauser, W. T1 - Efficient photoionisation, preparation and coherent manipulation of trapped /sup 171/Yb/sup +/-ions T2 - EQEC '05. European Quantum Electronics Conference, 12-17 June 2005, Munich Y1 - 2005 UR - http://ieeexplore.ieee.org/document/1567474/ SN - 0-7803-8973-5 U6 - https://doi.org/10.1109/EQEC.2005.1567474 SP - 308 PB - IEEE ER - TY - JOUR A1 - Heizmann, Michael A1 - Braun, Alexander A1 - Glitzner, Markus A1 - Günther, Matthias A1 - Hasna, Günther A1 - Klüver, Christina A1 - Krooß, Jakob A1 - Marquardt, Erik A1 - Overdick, Michael A1 - Ulrich, Markus T1 - Implementing machine learning: chances and challenges JF - at - Automatisierungstechnik Y1 - 2022 U6 - https://doi.org/10.1515/auto-2021-0149 SN - 2196-677X VL - 70 IS - 1 SP - 90 EP - 101 PB - De Gruyter CY - Berlin ER - TY - JOUR A1 - Balzer, Christoph A1 - Braun, Alexander A1 - Hannemann, T. A1 - Paape, Chr. A1 - Ettler, M. A1 - Neuhauser, W. A1 - Wunderlich, Chr. T1 - Electrodynamically trapped Yb+ ions for quantum information processing JF - Physical Review A Y1 - 2006 U6 - https://doi.org/10.1103/PhysRevA.73.041407 SN - 2469-9934 VL - 73 IS - 4 PB - American Physical Society ER - TY - GEN A1 - Braun, Alexander T1 - Laserspektroskopische Messverfahren zur Temperaturbestimmung in Sprays Y1 - 2001 PB - Georg-August-Universität Göttingen CY - Göttingen ER - TY - JOUR A1 - Braun, Alexander A1 - Kornmesser, Christoph A1 - Beushausen, Volker T1 - Simultaneous spatial and spectral imaging of lasing droplets JF - Journal of the Optical Society of America N2 - We present an experimental technique that allows the simultaneous spatial imaging and spectral analysis of falling droplets that exhibit lasing. Single droplet investigations serve as, among other purposes, a preliminary study for spray and combustion researchers. The described setup provides a valuable tool for the evaluation of microdroplet investigations with laser-spectroscopic techniques that rely on laser-induced fluorescence (LIF) or similar spectroscopical phenomena. The emphasis is that both spatial and spectral information are obtained from single-shot images of a falling droplet. Furthermore, combining spatial imaging and a spatially resolving optical multichannel analyzer makes a pointwise rastering of the droplets spectrum possible. This allows for the (almost) unambiguous determination of sources of influence on the spectrum of these droplets-such as geometrical distortion and lasing, nondissolved tracer lumps, and similar phenomena. Although the focus is on the experimental technique itself, we supplement detailed studies of lasing in falling microdroplets. These results were obtained with the aim of developing a system for measuring temperature distributions in droplets and sprays. In the light of these results the practice of calibrating a droplets spectrum by use of a bulk liquid sample needs to be critically reviewed. Y1 - 2005 U6 - https://doi.org/10.1364/josaa.22.001772 SN - 0030-3941 VL - 22 IS - 9 SP - 1772 EP - 1779 PB - Optical Society of America ER - TY - CHAP A1 - Johanning, M. A1 - Braun, Alexander A1 - Elman, V. A1 - Wunderlich, Chr. A1 - Neuhauser, W. T1 - Individual Addressing with Trapped Yb+ Ions T2 - 2007 European Conference on Lasers and Electro-Optics and the International Quantum Electronics Conference, 17.06.2007 - 22.06.2007, Munich, Germany Y1 - 2007 UR - http://ieeexplore.ieee.org/document/4386787/ SN - 978-1-4244-0930-3 U6 - https://doi.org/10.1109/CLEOE-IQEC.2007.4386787 PB - IEEE CY - München ER - TY - JOUR A1 - Heizmann, Michael A1 - Braun, Alexander A1 - Hüttel, Markus A1 - Klüver, Christina A1 - Marquardt, Erik A1 - Overdick, Michael A1 - Ulrich, Markus T1 - Artificial intelligence with neural networks in optical measurement and inspection systems JF - at - Automatisierungstechnik Y1 - 2020 U6 - https://doi.org/10.1515/auto-2020-0006 VL - 68 IS - 6 SP - 477 EP - 487 PB - De Gruyter ER - TY - CHAP A1 - Lehmann, Matthias A1 - Wittpahl, Christian A1 - Zakour, Hatem Ben A1 - Braun, Alexander ED - Haverkamp, Nils ED - Youngworth, Richard N. T1 - Resolution and accuracy of non-linear regression of PSF with artificial neural networks T2 - SPIE Optical Systems Design: Optical Instrument Science, Technology, and Applications, 2018, Frankfurt, Germany N2 - In a previous work we have demonstrated a novel numerical model for the point spread function (PSF) of an optical system that can efficiently model both experimental measurements and lens design simulations of the PSF. The novelty lies in the portability and the parameterization of this model, which allows for completely new ways to validate optical systems, which is especially interesting for mass production optics like in the automotive industry, but also for ophtalmology. The numerical basis for this model is a non-linear regression of the PSF with an artificial neural network (ANN). In this work we examine two important aspects of this model: the spatial resolution and the accuracy of the model. Measurement and simulation of a PSF can have a much higher resolution then the typical pixel size used in current camera sensors, especially those for the automotive industry. We discuss the influence this has on on the topology of the ANN and the final application where the modeled PSF is actually used. Another important influence on the accuracy of the trained ANN is the error metric which is used during training. The PSF is a distinctly non-linear function, which varies strongly over field and defocus, but nonetheless exhibits strong symmetries and spatial relations. Therefore we examine different distance and similarity measures and discuss its influence on the modeling performance of the ANN. Y1 - 2018 U6 - https://doi.org/10.1117/12.2313144 N1 - Preprint bei ArXiv verfügbar. VL - Proc. SPIE, Vol. 10695 IS - 106950C SP - 52 EP - 63 PB - International Society for Optics and Photonics CY - Frankfurt ER - TY - JOUR A1 - Lehmann, Matthias A1 - Wittpahl, Christian A1 - Zakour, Hatem Ben A1 - Braun, Alexander T1 - Modeling realistic optical aberrations to reuse existing drive scene recordings for autonomous driving validation JF - Journal of Electronic Imaging N2 - Training autonomous vehicles requires lots of driving sequences in all situations. Collecting and labeling these drive scenes is a very time-consuming and expensive process. Currently, it is not possible to reuse these drive scenes with different optical properties, because there exists no numerically efficient model for the transfer function of the optical system. We present a numerical model for the point spread function (PSF) of an optical system that can efficiently model both experimental measurements and lens design simulations of the PSF. The numerical basis for this model is a nonlinear regression of the PSF with an artificial neural network. The novelty lies in the portability and the parameterization of this model. We present a lens measurement series, yielding a numerical function for the PSF that depends only on the parameters defocus, field, and azimuth. By convolving existing images and videos with this PSF, we generate images as if seen through the measured lens. The methodology applies to any optical scenario, but we focus on the context of autonomous driving, where the quality of the detection algorithms depends directly on the optical quality of the used camera system. With this model, it is possible to reuse existing recordings, with the potential to avoid millions of test drive miles. The parameterization of the optical model allows for a method to validate the functional and safety limits of camera-based advanced driver assistance systems based on the real, measured lens actually used in the product. Y1 - 2019 U6 - https://doi.org/10.1117/1.JEI.28.1.013005 SN - 1560-229X VL - 28 IS - 1 SP - 013005 PB - SPIE ER - TY - JOUR A1 - Lehmann, Matthias A1 - Wittpahl, Christian A1 - Zakour, Hatem Ben A1 - Braun, Alexander T1 - Resolution and accuracy of nonlinear regression of point spread function with artificial neural networks JF - Optical Engineering N2 - We had already demonstrated a numerical model for the point spread function (PSF) of an optical system that can efficiently model both the experimental measurements and the lens design simulations of the PSF. The novelty lies in the portability and the parameterization of this model, which allow for completely new ways to validate optical systems, which is especially interesting not only for mass production optics such as in the automotive industry but also for ophthalmology. The numerical basis for this model is a nonlinear regression of the PSF with an artificial neural network (ANN). After briefly describing both the principle and the applications of the model, we then discuss two optically important aspects: the spatial resolution and the accuracy of the model. Using mean squared error (MSE) as a metric, we vary the topology of the neural network, both in the number of neurons and in the number of hidden layers. Measurement and simulation of a PSF can have a much higher spatial resolution than the typical pixel size used in current camera sensors. We discuss the influence this has on the topology of the ANN. The relative accuracy of the averaged pixel MSE is below 10  −  4, thus giving confidence that the regression does indeed model the measurement data with good accuracy. This article is only the starting point, and we propose several research avenues for future work. Y1 - 2019 U6 - https://doi.org/10.1117/1.oe.58.4.045101 SN - 0091-3286 VL - 58 IS - 4 SP - 045101 PB - SPIE ER - TY - CHAP A1 - Müller, Patrick A1 - Lehmann, Matthias A1 - Braun, Alexander ED - Kress, Bernard C. ED - Schelkens, Peter T1 - Optical quality metrics for image restoration T2 - Digital Optical Technologies 2019 N2 - Image restoration is a process used to remove blur (from different sources like object motion or aberrations) from images by either non-blind or blind-deconvolution. The metrics commonly used to quantify the restoration process are peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM). Often only a small sample of test images are used (like Lena or the camera guy). In optical design research PSNR and SSIM are not normally used, here image quality metrics based on linear system theory (e.g. modulation transfer function, MTF) are used to quantify optical errors like spherical or chromatic aberration. In this article we investigate how different image restoration algorithms can be quantified by applying image quality metrics. We start with synthetic image data that is used in camera test stands (e.g. Siemens star etc.), apply two different spatially variant degradation algorithms, and restore the original image by a direct method (Wiener filtering within sub-images), and by an iterative method (alternating direction method of multipliers, ADMM). Afterwards we compare the quality metrics (like MTF curves) for the original, the degraded and the restored image. As a first result we show that restoration algorithms sometimes fail in dealing with non-natural scenes, e.g. slanted-edge targets. Further, these first results indicate a correlation between degradation and restoration, i.e. the restoration algorithms are not capable of removing the optically relevant errors introduced by the degradation, a fact neither visible nor available from the PSNR values. We discuss the relevance in the context of the automotive industry, where image restoration may yield distinct advantages for camera-based applications, but testing methods rely on the used image quality metrics. Y1 - 2019 U6 - https://doi.org/10.1117/12.2528100 VL - Proceedings, Vol. 11062 CY - Munich ER - TY - CHAP A1 - Braun, Max A1 - Dellschaft, Klaas A1 - Franz, Thomas A1 - Hering, Dominik A1 - Jungen, Peter A1 - Metzler, Hagen A1 - Müller, Eugen A1 - Rostilov, Alexander A1 - Saathoff, Carsten ED - Huai, Jinpeng ED - Robin Chen, ED - Hsiao-Wuen Hon, ED - Yunhao Liu, T1 - Personalized search and exploration with mytag T2 - Proceedings of the 17th international conference on World Wide Web. WWW '08, Beijing China April 21 - 25, 2008 Y1 - 2008 UR - http://www.uni-koblenz.de/ klaasd/Downloads/papers/Braun2008PSA.pdf SN - 978-1-60558-085-2 U6 - https://doi.org/10.1145/1367497.1367641 PB - ACM CY - New York ER - TY - JOUR A1 - Braun, Alexander T1 - Automotive mass production of camera systems: Linking image quality to AI performance JF - tm - Technisches Messen N2 - Artificial intelligence methods based on machine learning or artificial neural networks have become indispensable in camera-based driver assistance systems, and also represent an essential building block for future autonomous driving. However, the great successes of these evaluation methods in environment perception and also driving planning are accompanied by equally great challenges in the validation and verification of these systems. One of the essential aspects for this is the required guaranteed safety of the functions under mass production conditions of the vehicles. This article explains this point of view using a detailed example from the field of camera-based driver assistance systems: the determination of inspection limits at the end of the production line. The camera is one of the most important sensor modalities for vehicle environment sensing and as such, the quality of the camera systems plays a key role in the safety argumentation of the overall system. Several illustrative application examples (role of simulations, calibration, influence of the windshield) will be presented. The basic ideas presented can be well transferred to the other sensor modalities (lidar, radar, ToF, etc.). The investigations/evidence show that doubts are allowed whether or how fast autonomous driving on level L4/5 will take hold as robotaxis or – even more challenging – in private ownership on a larger scale. KW - Electrical and Electronic Engineering KW - Instrumentation Y1 - 2022 U6 - https://doi.org/10.1515/teme-2022-0029 SN - 2196-7113 PB - Walter de Gruyter 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 Science 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 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:due62-opus-41484 UR - http://iopscience.iop.org/article/10.1088/1681-7575/acf1a4 SN - 1681-7575 VL - 60 IS - 064001 PB - IOP Publishing ER - TY - CHAP A1 - Müller, Patrick A1 - Braun, Alexander A1 - Keuper, Margret T1 - Impact of realistic properties of the point spread function on classification tasks to reveal a possible distribution shift T2 - NeurIPS 2022: Workshop on Distribution Shifts: Connecting Methods and Applications, December 3rd, 2022, New Orleans, USA KW - chromatic aberration KW - corruption KW - Deep Neural Networks KW - Distribution Shift KW - MTF Y1 - 2022 UR - https://openreview.net/forum?id=r7WJpE3oy0 PB - NeurIPS CY - New Orleans ER - TY - CHAP A1 - Müller, Patrick A1 - Braun, Alexander T1 - Simulating optical properties to access novel metrological parameter ranges and the impact of different model approximations T2 - 2022 IEEE International Workshop on Metrology for Automotive (MetroAutomotive), 4-6 July 2022 Y1 - 2022 SN - 978-1-6654-6689-9 U6 - https://doi.org/10.1109/MetroAutomotive54295.2022.9855079 SP - 133 EP - 138 PB - IEEE ER - TY - JOUR A1 - Müller, Patrick A1 - Braun, Alexander T1 - Local performance evaluation of AI-algorithms with the generalized spatial recall index JF - tm - Technisches Messen Y1 - 2023 U6 - https://doi.org/10.1515/teme-2023-0013 SN - 2196-7113 VL - 90 IS - 7-8 SP - 464 EP - 477 PB - De Gruyter 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 Science and Technology 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 - PAT A1 - Braun, Alexander A1 - Richwin, Matthias A1 - Weber, Thomas T1 - Optoelektronische Sensoreinrichtung für ein Kraftfahrzeug N2 - Beschrieben wird eine optoelektronische Sensoreinrichtung für ein Kraftfahrzeug zur unterscheidenden Detektion von Feuchtigkeit und Salzlösungen auf einer Fahrzeugscheibe, mit einer Beleuchtungseinheit zur Einstrahlung von Licht in eine Fahrzeugscheibe und mit einem Lichtempfänger, welcher von der Fahrzeugscheibe reflektiertes Licht erfasst, sowie mit einem Koppelelement zur Ein- und Auskopplung von Licht in bzw. aus der Fahrzeugscheibe, wobei die Beleuchtungseinrichtung Licht über einen Einfallswinkelbereich in die Fahrzeugscheibe einstrahlt, der sich über mehrere Winkelgrade erstreckt, wobei der Lichtempfänger eine Vielzahl von Empfangselementen aufweist, wobei ein optisches System das von der Fahrzeugscheibe in einen Ausfallswinkelbereich reflektierte Licht auf Empfangselemente des Lichtempfängers abbildet und wobei der Lichtempfänger ein Signal generiert, aus dem eine Auswertevorrichtung den Grenzwinkel der Totalreflexion an der Fahrzeugscheibe ermittelt. Y1 - 2009 UR - https://depatisnet.dpma.de/DepatisNet/depatisnet?action=bibdat&docid=DE102007052704A1 PB - Deutsches Patent- und Markenamt ER - TY - JOUR A1 - Wolf, Dominik Werner A1 - Thielbeer, Boris A1 - Ulrich, Markus A1 - Braun, Alexander T1 - Wavefront aberration measurements based on the Background Oriented Schlieren method JF - Measurement: Sensors N2 - Applications based on neural networks tend to be very sensitive to dataset shifts. Hence, the perception chain for autonomous driving is safeguarded against perturbations by imposing exaggerated optical quality requirements. Due to the non-linear coupling of optical elements, system requirements of camera-based Advanced Driver Assistance Systems (ADAS) can not be easily decomposed into individual part tolerances of the objective lens and the windscreen. This holds true for intensity-based part measurements, e.g. slanted edge measurements according to ISO12233, which can fundamentally not capture interference effects of the complex light field. Instead wavefront-based part measurements are required. Unfortunately, state-of-the-art wavefront measurement techniques are limited by the spanned sensitive area of the Shack-Hartmann lenslet array or the aperture stop of a corresponding interferometrical setup, respectively. Further, both measurements are limited by using collimated light only, whereas the target application has a large (angular) field of view, requiring many different measurements. We address those bottlenecks by proposing a novel wavefront aberration measurement procedure based on the Background Oriented Schlieren (BOS) method utilizing image auto-correlation. We analytically derive the governing equations for determining the Zernike coefficients of a wavefront aberration map in the knowledge of the local refractive power map obtained by a high-resolution BOS measurement. Furthermore, we experimentally demonstrate the feasibility of the measurement technique. Applying this novel method yields the promise of affordable wavefront aberration measurements only requiring a high-resolution camera and a sophisticated alignment strategy. KW - Wavefront aberration measurements KW - Deep optics KW - Dataset shifts KW - Data augmentation KW - Autonomous driving Y1 - 2024 U6 - https://doi.org/10.1016/j.measen.2024.101509 SN - 2665-9174 N1 - In Press, Corrected Proof PB - Elsevier ER - TY - CHAP A1 - Müller, Patrick A1 - Braun, Alexander A1 - Keuper, Margret T1 - Classification robustness to common optical aberrations T2 - 2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW) KW - Maschinelles Sehen KW - Abbildungsfehler KW - Neuronales Netz KW - Benchmarking Y1 - 2023 U6 - https://doi.org/10.1109/ICCVW60793.2023.00391 SP - 3634 EP - 3645 PB - IEEE 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 - 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, Christoph 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 - INPR A1 - Müller, Patrick A1 - Braun, Alexander A1 - Keuper, Margret T1 - Examining the Impact of Optical Aberrations to Image Classification and Object Detection Models N2 - 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. KW - Maschinelles Lernen KW - Maschinelles Sehen KW - Tiefes neuronales Netz KW - Objekterkennung KW - Abbildungsfehler Y1 - 2025 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:due62-opus-55457 PB - arXiv ET - v1 ER - TY - INPR A1 - Wolf, Dominik Werner A1 - Balaji, Prasannavenkatesh A1 - Braun, Alexander A1 - Ulrich, Markus T1 - Decoupling of neural network calibration measures T2 - German Conference on Pattern Recognition (GCPR) 2024 N2 - 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). Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:due62-opus-50104 PB - arXiv ER - TY - JOUR A1 - Jakab, Daniel A1 - Barthel, Julian A1 - Braun, Alexander A1 - Mohandas, Reenu A1 - Deegan, Brian Michael A1 - Kumbham, Mahendar A1 - Molloy, Dara A1 - Collins, Fiachra A1 - Scanlan, Anthony A1 - Eising, Ciarán T1 - SOLAS: Superpositioning an Optical Lens in Automotive Simulation JF - Electronic Imaging Y1 - 2025 U6 - https://doi.org/10.2352/EI.2025.37.15.AVM-101 SN - 2470-1173 VL - 37 IS - 15 SP - 101 EP - 1 PB - Society for Imaging Science & Technology ER - TY - JOUR A1 - Müller, Patrick A1 - Braun, Alexander A1 - Keuper, Margret T1 - Examining the Impact of Optical Aberrations to Image Classification and Object Detection Models JF - IEEE Transactions on Pattern Analysis and Machine Intelligence KW - Optical imaging KW - Lenses KW - Robustness KW - Kernel KW - Optical sensors Y1 - 2025 U6 - https://doi.org/10.1109/TPAMI.2025.3622234 SN - 0162-8828 SP - 1 EP - 15 PB - IEEE ER - TY - INPR A1 - Wolf, Dominik Werner A1 - Braun, Alexander A1 - Ulrich, Markus T1 - Optical aberrations in autonomous driving: Physics-informed parameterized temperature scaling for neural network uncertainty calibration T2 - International Journal of Computer Vision (IJCV) N2 - '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. Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:due62-opus-50120 PB - arXiv ER - TY - INPR A1 - Geerkens, Simon A1 - Sieberichs, Christian A1 - Braun, Alexander A1 - Waschulzik, Thomas T1 - QI2 -- an Interactive Tool for Data Quality Assurance 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 - Data Structures and Algorithms KW - Machine Learning KW - Computers and Society KW - Artificial Intelligence Y1 - 2023 PB - arXiv ER - TY - INPR A1 - Sieberichs, Christian A1 - Geerkens, Simon A1 - Braun, Alexander A1 - Waschulzik, Thomas T1 - ECS -- an Interactive Tool for Data Quality Assurance 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 - Artificial Intelligence KW - Machine Learning KW - PrePrint KW - Systems and Control Y1 - 2023 U6 - https://doi.org/10.48550/arXiv.2307.04368 PB - arXiv 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 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:due62-opus-42945 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 VL - 4 SP - 141 EP - 149 PB - Springer Nature 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 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:due62-opus-42908 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 VL - 4 SP - 131 EP - 139 PB - Springer Nature ER - TY - JOUR A1 - Jakab, Daniel A1 - Braun, Alexander A1 - Agnew, Cathaoir A1 - Mohandas, Reenu A1 - Deegan, Brian Michael A1 - Molloy, Dara A1 - Ward, Enda A1 - Scanlan, Anthony A1 - Eising, Ciarán T1 - SS-SFR: synthetic scenes spatial frequency response on Virtual KITTI and degraded automotive simulations for object detection JF - IET Conference Proceedings Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:due62-opus-50080 SN - 2732-4494 VL - 2024 IS - 10 SP - 110 EP - 117 PB - Institution of Engineering and Technology (IET) ER - TY - JOUR A1 - Jakab, Daniel A1 - Vázquez, Joel Herrera A1 - Barthel, Julian A1 - Honsbrok, Jan A1 - Deegan, Brian A1 - Mohandas, Reenu A1 - Brophy, Tim A1 - Scanlan, Anthony A1 - Ward, Enda A1 - Collins, Fiachra A1 - Eising, Ciarán A1 - Braun, Alexander T1 - SOLAS 1.1: Automotive Optical Simulation in Computer Vision (Early Access) JF - IEEE Open Journal of Vehicular Technology KW - Optical imaging KW - Optical distortion KW - Cameras KW - Lenses KW - Automotive engineering KW - Adaptive optics KW - Object detection KW - Computer vision KW - Optical sensors KW - Production Y1 - 2025 U6 - https://doi.org/10.1109/OJVT.2025.3640419 SN - 2644-1330 N1 - The supplemental material contains material to support the article. DOI:10.1109/OJVT.2025.3640419/mm1 SP - 1 EP - 16 PB - Institute of Electrical and Electronics Engineers ER -