bibtype C - Conference Paper (international conference)
ARLID 0471594
utime 20240103213656.7
mtime 20170224235959.9
SCOPUS 85013427985
WOS 000418399200007
DOI 10.1007/978-3-319-52277-7_7
title (primary) (eng) An Automatic Tortoise Specimen Recognition
specification
page_count 8 s.
media_type P
serial
ARLID cav_un_epca*0471591
ISBN 978-3-319-52276-0
title Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications: 21st Iberoamerican Congress, CIARP 2016
page_num 52-59
publisher
place Cham
name Springer International Publishing
year 2017
editor
name1 Beltran-Castanon
name2 C.
editor
name1 Nystrom
name2 I.
editor
name1 Famili
name2 F.
keyword Tortoise recognition
keyword Testudo graeca
author (primary)
ARLID cav_un_auth*0320130
name1 Sedláček
name2 Matěj
full_dept (cz) Rozpoznávání obrazu
full_dept (eng) Department of Pattern Recognition
department (cz) RO
department (eng) RO
institution UTIA-B
full_dept Department of Pattern Recognition
country CZ
fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
author
ARLID cav_un_auth*0101093
name1 Haindl
name2 Michal
full_dept (cz) Rozpoznávání obrazu
full_dept Department of Pattern Recognition
department (cz) RO
department RO
institution UTIA-B
full_dept Department of Pattern Recognition
fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
author
ARLID cav_un_auth*0345927
name1 Formanová
name2 D.
country CZ
source
url http://library.utia.cas.cz/separaty/2017/RO/sedlacek-0471594.pdf
cas_special
project
ARLID cav_un_auth*0303439
project_id GA14-10911S
agency GA ČR
country CZ
abstract (eng) 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 \nachieved 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.
action
ARLID cav_un_auth*0343392
name CIARP 2016 - 21st Iberoamerican Congress 2016
dates 20161108
mrcbC20-s 20161111
place Lima
country PE
RIV BD
FORD0 10000
FORD1 10200
FORD2 10201
reportyear 2018
num_of_auth 3
presentation_type PR
inst_support RVO:67985556
permalink http://hdl.handle.net/11104/0271348
cooperation
ARLID cav_un_auth*0343394
name Czech Environmental Inspectorate
confidential S
mrcbC86 3+4 Proceedings Paper Computer Science Artificial Intelligence|Computer Science Theory Methods|Imaging Science Photographic Technology
mrcbC86 3+4 Proceedings Paper Computer Science Artificial Intelligence|Computer Science Theory Methods|Imaging Science Photographic Technology
mrcbC86 3+4 Proceedings Paper Computer Science Artificial Intelligence|Computer Science Theory Methods|Imaging Science Photographic Technology
arlyear 2017
mrcbU14 85013427985 SCOPUS
mrcbU24 PUBMED
mrcbU34 000418399200007 WOS
mrcbU63 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
mrcbU67 340 Beltran-Castanon C.
mrcbU67 340 Nystrom I.
mrcbU67 340 Famili F.