@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{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} } @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 Sciences 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} }