0533825 20250123090336.520201102235959.9 85098623422 000646178502029 10.1109/ICIP40778.2020.9191320 5 s. P cav_un_epca*0533760978-1-7281-6396-31522-48802036-2040PiscatawayIEEE2020 CNN SURF U-net automated object labeling image segmentation cav_un_auth*0283562 Novozámský Adam 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*0398453 Vít D. CZ 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*0044239 Franc J. CZ cav_un_auth*0038064 Krbálek M. CZ cav_un_auth*0332672 Bílková Zuzana 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*0101238 Zitová Barbara 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/2020/ZOI/novozamsky-0533825.pdf GA18-05360S GA ČR cav_un_auth*0361425 TN01000024 GA TA ČR CZ cav_un_auth*0376535 AP1701 AV ČR CZ cav_un_auth*0349658 Deep learning-based methods for classification and segmentation require large training sets. Generating training data is often a tedious and expensive task. In industrial applications, such as automated visual inspection of products in an assemble line, objects for classification are well defined yet labeled data are difficult to obtain. To alleviate the problem of manual labeling, we propose to train a convolutional neural network with an automatically generated training set using a naive classifier with handcrafted features. We show that when the naive classifier has high precision then the trained network has both high precision and recall despite the low recall of the naive classifier. We demonstrate the proposed methodology on real scenario of detecting a car coolant tank. However, the proposed methodology facilitates collection of train data for a wider type of CNN based methods such as near-duplicate image detection or segmenting tampered areas of images. cav_un_auth*0398419 2020 IEEE International Conference on Image Processing (ICIP) 20201025 20201028 Abu Dhabi AE JC 20000 20200 20206 2021 7 PR http://hdl.handle.net/11104/0312104 cav_un_auth*0377559 ČVUT v Praze, Fakulta jaderného a fyzikálního inženýrství ČVUT FJFI CZ S 0.366 Q3 2020 C 85098623422 SCOPUS PUBMED 000646178502029 WOS cav_un_epca*0533760 2020 IEEE International Conference on Image Processing (ICIP) IEEE 2020 Piscataway 2036 2040 978-1-7281-6396-3 1522-4880 2381-8549