An Automatic Tortoise Specimen Recognition

0471594 - ÚTIA 2018 RIV DE eng C - Konferenční příspěvek (zahraniční konf.)
Sedláček, Matěj - Haindl, Michal - Formanová, D.
An Automatic Tortoise Specimen Recognition.
Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications: 21st Iberoamerican Congress, CIARP 2016. Cham: Springer International Publishing, 2017 - (Beltran-Castanon, C.; Nystrom, I.; Famili, F.), s. 52-59. Lecture Notes in Computer Science, 10125. ISBN 978-3-319-52276-0.
[CIARP 2016 - 21st Iberoamerican Congress 2016. Lima (PE), 08.11.2016-11.11.2016]
Grant CEP: GA ČR(CZ) GA14-10911S
Institucionální podpora: RVO:67985556
Klíčová slova: Tortoise recognition * Testudo graeca
Obor OECD: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
http://library.utia.cas.cz/separaty/2017/RO/sedlacek-0471594.pdf

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.
Trvalý link: http://hdl.handle.net/11104/0271348