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An Automatic Tortoise Specimen Recognition
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SYSNO ASEP 0471594 Document Type C - Proceedings Paper (int. conf.) R&D Document Type Conference Paper Title An Automatic Tortoise Specimen Recognition Author(s) Sedláček, Matěj (UTIA-B)
Haindl, Michal (UTIA-B) RID, ORCID
Formanová, D. (CZ)Number of authors 3 Source Title 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. - ISBN 978-3-319-52276-0 Pages s. 52-59 Number of pages 8 s. Publication form Print - P Action CIARP 2016 - 21st Iberoamerican Congress 2016 Event date 08.11.2016 - 11.11.2016 VEvent location Lima Country PE - Peru Event type WRD Language eng - English Country DE - Germany Keywords Tortoise recognition ; Testudo graeca Subject RIV BD - Theory of Information OECD category Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8) R&D Projects GA14-10911S GA ČR - Czech Science Foundation (CSF) Institutional support UTIA-B - RVO:67985556 UT WOS 000418399200007 EID SCOPUS 85013427985 DOI 10.1007/978-3-319-52277-7_7 Annotation 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.Workplace Institute of Information Theory and Automation Contact Markéta Votavová, votavova@utia.cas.cz, Tel.: 266 052 201. Year of Publishing 2018
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