TY - JOUR A1 - Johanning, M. A1 - Braun, Alexander A1 - Eiteneuer, D. A1 - Paape, C. A1 - Balzer, C. A1 - Neuhauser, W. A1 - Wunderlich, C. T1 - Resonance-enhanced isotope-selective photoionization of YbI for ion trap loading JF - Applied Physics B Y1 - 2011 UR - https://arxiv.org/pdf/0712.0969.pdf U6 - https://doi.org/10.1007/s00340-011-4502-7 SN - 1432-0649 VL - 103 IS - 2 SP - 327 EP - 338 PB - Springer Nature ER - TY - JOUR A1 - Brummel, Mattis A1 - Müller, Patrick A1 - Braun, Alexander T1 - Spatial precision and recall indices to assess the performance of instance segmentation algorithms JF - Electronic Imaging KW - Space-Variance KW - Computer Vision KW - IoU KW - image perturbations Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:due62-opus-41552 SN - 2470-1173 VL - 34 IS - 16 SP - 1 EP - 6 PB - Society for Imaging Science and Technology ER - TY - JOUR A1 - 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 -