0571255 20240402213835.220230428235959.9 85160816520 10.1007/978-3-031-31438-4_8 10 s. P cav_un_epca*0571254978-3-031-31437-7108-117ChamSpringer2023GadeR. Image recognition Blur Augmentation of the training set Convolutional neural network cav_un_auth*0377447 Lébl Matěj UTIA-B Zpracování obrazové informace Department of Image Processing ZOI ZOI Department of Image Processing CZ Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0101209 Šroubek Filip UTIA-B Zpracování obrazové informace Department of Image Processing ZOI ZOI Department of Image Processing Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0101087 Flusser Jan UTIA-B Zpracování obrazové informace Department of Image Processing ZOI ZOI Department of Image Processing Ústav teorie informace a automatizace AV ČR, v. v. i. http://library.utia.cas.cz/separaty/2023/ZOI/lebl-0571255.pdf GA21-03921S GA ČR cav_un_auth*0412209 Blur is a common phenomenon in image acquisition that negatively influences the recognition rate of most classifiers. This paper studies the influence of image blurring of various types and sizes on the recognition rate achieved by a deep convolutional network. We confirm that the blur significantly decreases the performance if the network has been trained on clear images only. When the training set is augmented with blurred samples, the recognition rate becomes sufficiently high even if the blur in query images is of different size than the blur used for training. However, this is mostly not true if query images contain blur of a different type from the one used for training. cav_un_auth*0449343 Scandinavian Conference on Image Analysis 2023 /23./ 20230418 20230421 Levi FI JD 20000 20200 20206 2024 3 PO RVO:67985556 https://hdl.handle.net/11104/0342934 S 2023 85160816520 SCOPUS PUBMED WOS cav_un_epca*0571254 Image Analysis: 23rd Scandinavian Conference, SCIA 2023 978-3-031-31437-7 108 117 Cham Springer 2023 Lecture notes on computer science LNCS 13886 Gade R. 340