@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} } @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} } @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{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{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} }