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 - 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 - 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 - 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 - 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 -