0471594 20240103213656.720170224235959.9 85013427985 000418399200007 10.1007/978-3-319-52277-7_7 8 s. P cav_un_epca*0471591978-3-319-52276-052-59ChamSpringer International Publishing2017Beltran-CastanonC.NystromI.FamiliF. Tortoise recognition Testudo graeca cav_un_auth*0320130 Sedláček Matěj Rozpoznávání obrazu Department of Pattern Recognition RO RO UTIA-B Department of Pattern Recognition CZ Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0101093 Haindl Michal Rozpoznávání obrazu Department of Pattern Recognition RO RO UTIA-B Department of Pattern Recognition Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0345927 Formanová D. CZ http://library.utia.cas.cz/separaty/2017/RO/sedlacek-0471594.pdf cav_un_auth*0303439 GA14-10911S GA ČR CZ The spur-thighed tortoise ({\it Testudo graeca}) is listed among endangered species on the CITES list and the need to keep track of its specimens calls for a noninvasive, reliable and fast method that would recognize individual tortoises one from another. We present an automatic system for the recognition of tortoise specimen based on variable-quality digital photographs of their plastrons using an image classification approach and our proposed discriminative features. The plastron image database, on which the recognition system was tested, consists of 276 low-quality images with a variable scene set-up and of 982 moderate-quality images with a fixed scene set-up. The achieved overall success rates of automatically identifying a tortoise in the database were 43,0\% for the low-quality images and 60,7\% for the moderate-quality images. The results show that the automatic tortoise recognition based on the plastron images is feasible and suggests further improvements for a real application use. cav_un_auth*0343392 CIARP 2016 - 21st Iberoamerican Congress 2016 20161108 20161111 Lima PE BD 10000 10200 10201 2018 3 PR RVO:67985556 http://hdl.handle.net/11104/0271348 cav_un_auth*0343394 Czech Environmental Inspectorate S 3+4 Proceedings Paper Computer Science Artificial Intelligence|Computer Science Theory Methods|Imaging Science Photographic Technology 3+4 Proceedings Paper Computer Science Artificial Intelligence|Computer Science Theory Methods|Imaging Science Photographic Technology 3+4 Proceedings Paper Computer Science Artificial Intelligence|Computer Science Theory Methods|Imaging Science Photographic Technology 2017 85013427985 SCOPUS PUBMED 000418399200007 WOS cav_un_epca*0471591 Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications: 21st Iberoamerican Congress, CIARP 2016 Springer International Publishing 2017 Cham 52 59 978-3-319-52276-0 Lecture Notes in Computer Science 10125 340 Beltran-Castanon C. 340 Nystrom I. 340 Famili F.