@inproceedings{KornmesserBraunBeushausen2001, author = {Kornmesser, Christoph and Braun, Alexander and Beushausen, Volker}, title = {Applicability of different fluorescent dyes for 2D temperature measurements in dense sprays using planar laser induced fluorescence}, series = {Proceedings of the 17th Annual Conference on Liquid Atomization and Spray Systems, 02.06.2001, Z{\"u}rich}, booktitle = {Proceedings of the 17th Annual Conference on Liquid Atomization and Spray Systems, 02.06.2001, Z{\"u}rich}, publisher = {ETH Z{\"u}rich}, address = {Z{\"u}rich}, isbn = {3952224413}, pages = {554 -- 559}, year = {2001}, language = {en} } @article{KrebsMuellerBraun2021, author = {Krebs, Christian and M{\"u}ller, Patrick and Braun, Alexander}, title = {Impact of Windshield Optical Aberrations on Visual Range Camera Based Classification Tasks Performed by CNNs}, series = {London Imaging Meeting}, volume = {2021}, journal = {London Imaging Meeting}, number = {1}, publisher = {Ingenta connect}, isbn = {0-89208-346-6}, issn = {2694-118X}, doi = {10.2352/issn.2694-118X.2021.LIM-83}, pages = {83 -- 87}, year = {2021}, language = {de} } @inproceedings{MuellerLehmannBraun2020, author = {Mueller, Patrick and Lehmann, Matthias and Braun, Alexander}, title = {Simulating tests to test simulation}, series = {IS\&T International Symposium on Electronic Imaging 2020: Autonomous Vehicles and Machines, 26 January 2020 — 30 January 2020, Burlingame, CA, USA}, volume = {2020}, booktitle = {IS\&T International Symposium on Electronic Imaging 2020: Autonomous Vehicles and Machines, 26 January 2020 — 30 January 2020, Burlingame, CA, USA}, number = {16}, publisher = {Society for Imaging Science and Technology}, doi = {10.2352/ISSN.2470-1173.2020.16.AVM-149}, year = {2020}, language = {en} } @article{MuellerBrummelBraun2021, author = {M{\"u}ller, Patrick and Brummel, Mattis and Braun, Alexander}, title = {Spatial recall index for machine learning algorithms}, series = {London Imaging Meeting}, volume = {2021}, journal = {London Imaging Meeting}, number = {1}, publisher = {Society for Imaging Science and Technology}, isbn = {0-89208-346-6}, doi = {10.2352/issn.2694-118X.2021.LIM-58}, url = {http://nbn-resolving.de/urn:nbn:de:hbz:due62-opus-35657}, pages = {58 -- 62}, year = {2021}, abstract = {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.}, language = {en} } @article{TsengMoslehMannanetal.2021, author = {Tseng, Ethan and Mosleh, Ali and Mannan, Fahim and St-Arnaud, Karl and Sharma, Avinash and Peng, Yifan and Braun, Alexander and Nowrouzezahrai, Derek and Lalonde, Jean-Fran{\c{c}}ois and Heide, Felix}, title = {Differentiable Compound Optics and Processing Pipeline Optimization for End-to-end Camera Design}, series = {ACM Transactions on Graphics}, volume = {40}, journal = {ACM Transactions on Graphics}, number = {2}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, issn = {1557-7368}, doi = {10.1145/3446791}, pages = {1 -- 19}, year = {2021}, abstract = {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.}, language = {en} } @article{WittpahlDeeganBlacketal.2021, author = {Wittpahl, Christian and Deegan, Brian and Black, Bob and Braun, Alexander}, title = {An analytic-numerical image flicker study to test novel flicker metrics}, series = {Electronic Imaging: Society for Imaging Science and Technology}, journal = {Electronic Imaging: Society for Imaging Science and Technology}, number = {17}, publisher = {Society for Imaging Science and Technology}, issn = {2470-1173}, doi = {10.2352/ISSN.2470-1173.2021.17.AVM-183}, url = {http://nbn-resolving.de/urn:nbn:de:hbz:due62-opus-35671}, pages = {1 -- 8}, year = {2021}, language = {en} } @article{WittpahlZakourLehmannetal.2018, author = {Wittpahl, Christian and Zakour, Hatem Ben and Lehmann, Matthias and Braun, Alexander}, title = {Realistic Image Degradation with Measured PSF}, series = {Electronic Imaging, Autonomous Vehicles and Machines 2018}, journal = {Electronic Imaging, Autonomous Vehicles and Machines 2018}, number = {17}, publisher = {Society for Imaging Science and Technology}, issn = {2470-1173}, doi = {10.2352/ISSN.2470-1173.2018.17.AVM-149}, pages = {149}, year = {2018}, language = {en} } @phdthesis{Braun2007, author = {Braun, Alexander}, title = {Addressing Single Yb+ Ions: A new scheme for quantum computing in linear ion traps}, publisher = {Cuvillier Verlag}, address = {G{\"o}ttingen}, isbn = {978-3-86727-276-6}, pages = {169}, year = {2007}, language = {en} } @inproceedings{BalzerHannemannWunderlichetal.2005, author = {Balzer, Christoph and Hannemann, Th. and Wunderlich, Chr. and Braun, Alexander and Ettler, M. and Paape, Chr. and Neuhauser, W.}, title = {Efficient photoionisation, preparation and coherent manipulation of trapped /sup 171/Yb/sup +/-ions}, series = {EQEC '05. European Quantum Electronics Conference, 12-17 June 2005, Munich}, booktitle = {EQEC '05. European Quantum Electronics Conference, 12-17 June 2005, Munich}, publisher = {IEEE}, isbn = {0-7803-8973-5}, doi = {10.1109/EQEC.2005.1567474}, pages = {308}, year = {2005}, language = {en} } @article{HeizmannBraunGlitzneretal.2022, author = {Heizmann, Michael and Braun, Alexander and Glitzner, Markus and G{\"u}nther, Matthias and Hasna, G{\"u}nther and Kl{\"u}ver, Christina and Krooß, Jakob and Marquardt, Erik and Overdick, Michael and Ulrich, Markus}, title = {Implementing machine learning: chances and challenges}, series = {at - Automatisierungstechnik}, volume = {70}, journal = {at - Automatisierungstechnik}, number = {1}, publisher = {De Gruyter}, address = {Berlin}, issn = {2196-677X}, doi = {10.1515/auto-2021-0149}, pages = {90 -- 101}, year = {2022}, language = {en} } @article{BalzerBraunHannemannetal.2006, author = {Balzer, Christoph and Braun, Alexander and Hannemann, T. and Paape, Chr. and Ettler, M. and Neuhauser, W. and Wunderlich, Chr.}, title = {Electrodynamically trapped Yb+ ions for quantum information processing}, series = {Physical Review A}, volume = {73}, journal = {Physical Review A}, number = {4}, publisher = {American Physical Society}, issn = {2469-9934}, doi = {10.1103/PhysRevA.73.041407}, year = {2006}, language = {en} } @misc{Braun2001, author = {Braun, Alexander}, title = {Laserspektroskopische Messverfahren zur Temperaturbestimmung in Sprays}, publisher = {Georg-August-Universit{\"a}t G{\"o}ttingen}, address = {G{\"o}ttingen}, organization = {Georg-August-Universit{\"a}t G{\"o}ttingen}, year = {2001}, language = {de} } @article{BraunKornmesserBeushausen2005, author = {Braun, Alexander and Kornmesser, Christoph and Beushausen, Volker}, title = {Simultaneous spatial and spectral imaging of lasing droplets}, series = {Journal of the Optical Society of America}, volume = {22}, journal = {Journal of the Optical Society of America}, number = {9}, publisher = {Optical Society of America}, issn = {0030-3941}, doi = {10.1364/josaa.22.001772}, pages = {1772 -- 1779}, year = {2005}, abstract = {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.}, language = {en} } @inproceedings{JohanningBraunElmanetal.2007, author = {Johanning, M. and Braun, Alexander and Elman, V. and Wunderlich, Chr. and Neuhauser, W.}, title = {Individual Addressing with Trapped Yb+ Ions}, series = {2007 European Conference on Lasers and Electro-Optics and the International Quantum Electronics Conference, 17.06.2007 - 22.06.2007, Munich, Germany}, booktitle = {2007 European Conference on Lasers and Electro-Optics and the International Quantum Electronics Conference, 17.06.2007 - 22.06.2007, Munich, Germany}, publisher = {IEEE}, address = {M{\"u}nchen}, isbn = {978-1-4244-0930-3}, doi = {10.1109/CLEOE-IQEC.2007.4386787}, pages = {1}, year = {2007}, language = {en} } @article{HeizmannBraunHuetteletal.2020, author = {Heizmann, Michael and Braun, Alexander and H{\"u}ttel, Markus and Kl{\"u}ver, Christina and Marquardt, Erik and Overdick, Michael and Ulrich, Markus}, title = {Artificial intelligence with neural networks in optical measurement and inspection systems}, series = {at - Automatisierungstechnik}, volume = {68}, journal = {at - Automatisierungstechnik}, number = {6}, publisher = {De Gruyter}, doi = {10.1515/auto-2020-0006}, pages = {477 -- 487}, year = {2020}, language = {en} } @inproceedings{LehmannWittpahlZakouretal.2018, author = {Lehmann, Matthias and Wittpahl, Christian and Zakour, Hatem Ben and Braun, Alexander}, title = {Resolution and accuracy of non-linear regression of PSF with artificial neural networks}, series = {SPIE Optical Systems Design: Optical Instrument Science, Technology, and Applications, 2018, Frankfurt, Germany}, volume = {Proc. SPIE, Vol. 10695}, booktitle = {SPIE Optical Systems Design: Optical Instrument Science, Technology, and Applications, 2018, Frankfurt, Germany}, number = {106950C}, editor = {Haverkamp, Nils and Youngworth, Richard N.}, publisher = {International Society for Optics and Photonics}, address = {Frankfurt}, organization = {International Society for Optics and Photonics}, doi = {10.1117/12.2313144}, pages = {52 -- 63}, year = {2018}, abstract = {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.}, language = {en} } @article{LehmannWittpahlZakouretal.2019, author = {Lehmann, Matthias and Wittpahl, Christian and Zakour, Hatem Ben and Braun, Alexander}, title = {Modeling realistic optical aberrations to reuse existing drive scene recordings for autonomous driving validation}, series = {Journal of Electronic Imaging}, volume = {28}, journal = {Journal of Electronic Imaging}, number = {1}, publisher = {SPIE}, issn = {1560-229X}, doi = {10.1117/1.JEI.28.1.013005}, pages = {013005}, year = {2019}, abstract = {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.}, language = {en} } @article{LehmannWittpahlZakouretal.2019, author = {Lehmann, Matthias and Wittpahl, Christian and Zakour, Hatem Ben and Braun, Alexander}, title = {Resolution and accuracy of nonlinear regression of point spread function with artificial neural networks}, series = {Optical Engineering}, volume = {58}, journal = {Optical Engineering}, number = {4}, publisher = {SPIE}, issn = {0091-3286}, doi = {10.1117/1.oe.58.4.045101}, pages = {045101}, year = {2019}, abstract = {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.}, language = {en} } @inproceedings{MuellerLehmannBraun2019, author = {M{\"u}ller, Patrick and Lehmann, Matthias and Braun, Alexander}, title = {Optical quality metrics for image restoration}, series = {Digital Optical Technologies 2019}, volume = {Proceedings, Vol. 11062}, booktitle = {Digital Optical Technologies 2019}, editor = {Kress, Bernard C. and Schelkens, Peter}, address = {Munich}, organization = {International Society for Optics and Photonics}, doi = {10.1117/12.2528100}, pages = {1106214}, year = {2019}, abstract = {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.}, language = {en} } @inproceedings{BraunDellschaftFranzetal.2008, author = {Braun, Max and Dellschaft, Klaas and Franz, Thomas and Hering, Dominik and Jungen, Peter and Metzler, Hagen and M{\"u}ller, Eugen and Rostilov, Alexander and Saathoff, Carsten}, title = {Personalized search and exploration with mytag}, series = {Proceedings of the 17th international conference on World Wide Web. WWW '08, Beijing China April 21 - 25, 2008}, booktitle = {Proceedings of the 17th international conference on World Wide Web. WWW '08, Beijing China April 21 - 25, 2008}, editor = {Huai, Jinpeng and Robin Chen, and Hsiao-Wuen Hon, and Yunhao Liu,}, publisher = {ACM}, address = {New York}, isbn = {978-1-60558-085-2}, doi = {10.1145/1367497.1367641}, pages = {1031}, year = {2008}, language = {en} } @article{Braun2022, author = {Braun, Alexander}, title = {Automotive mass production of camera systems: Linking image quality to AI performance}, series = {tm - Technisches Messen}, journal = {tm - Technisches Messen}, publisher = {Walter de Gruyter}, issn = {2196-7113}, doi = {10.1515/teme-2022-0029}, year = {2022}, abstract = {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.}, language = {en} } @article{WohlersMuellerBraun2022, author = {Wohlers, Luis Constantin and M{\"u}ller, Patrick and Braun, Alexander}, title = {Original image noise reconstruction for spatially-varying filtered driving scenes}, series = {Electronic Imaging: Society for Imaging Science and Technology}, volume = {34}, journal = {Electronic Imaging: Society for Imaging Science and Technology}, number = {16}, publisher = {Society for Imaging Science and Technology}, issn = {2470-1173}, doi = {10.2352/EI.2022.34.16.AVM-214}, url = {http://nbn-resolving.de/urn:nbn:de:hbz:due62-opus-41479}, pages = {1 -- 7}, year = {2022}, language = {en} } @article{WolfUlrichBraun2023, author = {Wolf, Dominik Werner and Ulrich, Markus and Braun, Alexander}, title = {Novel developments of refractive power measurement techniques in the automotive world}, series = {Metrologia}, volume = {60}, journal = {Metrologia}, number = {064001}, publisher = {IOP Publishing}, issn = {1681-7575}, doi = {10.1088/1681-7575/acf1a4}, url = {http://nbn-resolving.de/urn:nbn:de:hbz:due62-opus-41484}, year = {2023}, abstract = {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 Moir{\´e} 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.}, language = {en} } @inproceedings{MuellerBraunKeuper2022, author = {M{\"u}ller, Patrick and Braun, Alexander and Keuper, Margret}, title = {Impact of realistic properties of the point spread function on classification tasks to reveal a possible distribution shift}, series = {NeurIPS 2022: Workshop on Distribution Shifts: Connecting Methods and Applications, December 3rd, 2022, New Orleans, USA}, booktitle = {NeurIPS 2022: Workshop on Distribution Shifts: Connecting Methods and Applications, December 3rd, 2022, New Orleans, USA}, publisher = {NeurIPS}, address = {New Orleans}, year = {2022}, language = {en} } @inproceedings{MuellerBraun2022, author = {M{\"u}ller, Patrick and Braun, Alexander}, title = {Simulating optical properties to access novel metrological parameter ranges and the impact of different model approximations}, series = {2022 IEEE International Workshop on Metrology for Automotive (MetroAutomotive), 4-6 July 2022}, booktitle = {2022 IEEE International Workshop on Metrology for Automotive (MetroAutomotive), 4-6 July 2022}, publisher = {IEEE}, isbn = {978-1-6654-6689-9}, doi = {10.1109/MetroAutomotive54295.2022.9855079}, pages = {133 -- 138}, year = {2022}, language = {en} } @article{MuellerBraun2023, author = {M{\"u}ller, Patrick and Braun, Alexander}, title = {Local performance evaluation of AI-algorithms with the generalized spatial recall index}, series = {tm - Technisches Messen}, volume = {90}, journal = {tm - Technisches Messen}, number = {7-8}, publisher = {De Gruyter}, issn = {2196-7113}, doi = {10.1515/teme-2023-0013}, pages = {464 -- 477}, year = {2023}, language = {en} } @article{MuellerBraun2023, author = {M{\"u}ller, Patrick and Braun, Alexander}, title = {MTF as a performance indicator for AI algorithms?}, series = {Electronic Imaging: Society for Imaging Science and Technology}, volume = {35}, journal = {Electronic Imaging: Society for Imaging Science and Technology}, number = {16}, publisher = {Society for Imaging Science and Technology}, issn = {2470-1173}, doi = {10.2352/EI.2023.35.16.AVM-125}, pages = {1 -- 7}, year = {2023}, abstract = {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.}, language = {en} } @article{BrummelMuellerBraun2022, author = {Brummel, Mattis and M{\"u}ller, Patrick and Braun, Alexander}, title = {Spatial precision and recall indices to assess the performance of instance segmentation algorithms}, series = {Electronic Imaging}, volume = {34}, journal = {Electronic Imaging}, number = {16}, publisher = {Society for Imaging Science and Technology}, issn = {2470-1173}, doi = {10.2352/EI.2022.34.16.AVM-101}, url = {http://nbn-resolving.de/urn:nbn:de:hbz:due62-opus-41552}, pages = {1 -- 6}, year = {2022}, language = {en} } @misc{BraunRichwinWeber2009, author = {Braun, Alexander and Richwin, Matthias and Weber, Thomas}, title = {Optoelektronische Sensoreinrichtung f{\"u}r ein Kraftfahrzeug}, publisher = {Deutsches Patent- und Markenamt}, pages = {10}, year = {2009}, abstract = {Beschrieben wird eine optoelektronische Sensoreinrichtung f{\"u}r ein Kraftfahrzeug zur unterscheidenden Detektion von Feuchtigkeit und Salzl{\"o}sungen auf einer Fahrzeugscheibe, mit einer Beleuchtungseinheit zur Einstrahlung von Licht in eine Fahrzeugscheibe und mit einem Lichtempf{\"a}nger, 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 {\"u}ber einen Einfallswinkelbereich in die Fahrzeugscheibe einstrahlt, der sich {\"u}ber mehrere Winkelgrade erstreckt, wobei der Lichtempf{\"a}nger eine Vielzahl von Empfangselementen aufweist, wobei ein optisches System das von der Fahrzeugscheibe in einen Ausfallswinkelbereich reflektierte Licht auf Empfangselemente des Lichtempf{\"a}ngers abbildet und wobei der Lichtempf{\"a}nger ein Signal generiert, aus dem eine Auswertevorrichtung den Grenzwinkel der Totalreflexion an der Fahrzeugscheibe ermittelt.}, language = {de} } @article{WolfThielbeerUlrichetal.2024, author = {Wolf, Dominik Werner and Thielbeer, Boris and Ulrich, Markus and Braun, Alexander}, title = {Wavefront aberration measurements based on the Background Oriented Schlieren method}, series = {Measurement: Sensors}, journal = {Measurement: Sensors}, publisher = {Elsevier}, issn = {2665-9174}, doi = {10.1016/j.measen.2024.101509}, year = {2024}, abstract = {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.}, language = {en} } @inproceedings{MuellerBraunKeuper2023, author = {M{\"u}ller, Patrick and Braun, Alexander and Keuper, Margret}, title = {Classification robustness to common optical aberrations}, series = {2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)}, booktitle = {2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)}, publisher = {IEEE}, doi = {10.1109/ICCVW60793.2023.00391}, pages = {3634 -- 3645}, year = {2023}, subject = {Maschinelles Sehen}, language = {en} } @article{MolloyMuellerDeeganetal.2024, author = {Molloy, Dara and M{\"u}ller, Patrick and Deegan, Brian and Mullins, Darragh and Horgan, Jonathan and Ward, Enda and Jones, Edward and Braun, Alexander and Glavin, Martin}, title = {Analysis of the Impact of Lens Blur on Safety-Critical Automotive Object Detection}, series = {IEEE Access}, volume = {12}, journal = {IEEE Access}, publisher = {IEEE}, issn = {2169-3536}, doi = {10.1109/ACCESS.2023.3348663}, url = {http://nbn-resolving.de/urn:nbn:de:hbz:due62-opus-42999}, pages = {3554 -- 3569}, year = {2024}, abstract = {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}, language = {en} } @article{JohanningBraunTimoneyetal.2009, author = {Johanning, M. and Braun, Alexander and Timoney, N. and Elman, V. and Neuhauser, W. and Wunderlich, Chr}, title = {Individual addressing of trapped ions and coupling of motional and spin states using RF radiation}, series = {Physical review letters}, volume = {102}, journal = {Physical review letters}, number = {7}, publisher = {American Physical Society}, issn = {1079-7114}, doi = {10.1103/PhysRevLett.102.073004}, year = {2009}, abstract = {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.}, language = {en} } @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} } @article{JakabVazquezBartheletal.2025, author = {Jakab, Daniel and V{\´a}zquez, Joel Herrera and Barthel, Julian and Honsbrok, Jan and Deegan, Brian and Mohandas, Reenu and Brophy, Tim and Scanlan, Anthony and Ward, Enda and Collins, Fiachra and Eising, Ciar{\´a}n and Braun, Alexander}, title = {SOLAS 1.1: Automotive Optical Simulation in Computer Vision (Early Access)}, series = {IEEE Open Journal of Vehicular Technology}, journal = {IEEE Open Journal of Vehicular Technology}, publisher = {Institute of Electrical and Electronics Engineers}, issn = {2644-1330}, doi = {10.1109/OJVT.2025.3640419}, pages = {1 -- 16}, year = {2025}, language = {en} }