bibtype C - Conference Paper (international conference)
ARLID 0520496
utime 20241106135819.8
mtime 20200120235959.9
SCOPUS 85078695401
DOI 10.1109/IVCNZ48456.2019.8961023
title (primary) (eng) Coniferous Trees Needles-Based Taxonomy Classification
specification
page_count 6 s.
media_type P
serial
ARLID cav_un_epca*0520495
ISBN 978-1-7281-4188-6
ISSN 2151-2191
title International Conference on Image and Vision Computing New Zealand 2019 (IVCNZ 2019)
page_num 1-6
publisher
place Piscataway
name IEEE
year 2019
keyword Coniferous needles categorization
keyword Tree taxonomy recognition
keyword Spiral Markov random field model
author (primary)
ARLID cav_un_auth*0101093
name1 Haindl
name2 Michal
institution UTIA-B
full_dept (cz) Rozpoznávání obrazu
full_dept (eng) Department of Pattern Recognition
department (cz) RO
department (eng) RO
full_dept Department of Pattern Recognition
fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
author
ARLID cav_un_auth*0101239
name1 Žid
name2 Pavel
institution UTIA-B
full_dept (cz) Rozpoznávání obrazu
full_dept Department of Pattern Recognition
department (cz) RO
department RO
full_dept Department of Pattern Recognition
fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
source
url http://library.utia.cas.cz/separaty/2020/RO/haindl-0520496.pdf
cas_special
project
ARLID cav_un_auth*0376011
project_id GA19-12340S
agency GA ČR
country CZ
abstract (eng) This paper introduces multispectral rotationally invariant textural features of the Markovian type applied for the effective coniferous tree needles categorization. Presented texture features are inferred from the descriptive multispectral spiral wide-sense Markov model. Unlike the alternative texture recognition methods based on various gray-scale discriminative textural descriptions, we take advantage of the needles texture representation, which is fully descriptive multispectral and rotationally invariant. The presented method achieves high accuracy for needles recognition. Thus it can be used for reliable coniferous tree taxon classification. Our classifier is tested on the open source needles database Aff, which contains 716 high-resolution images from 11 diverse coniferous tree species.
action
ARLID cav_un_auth*0387868
name Image and Vision Computing New Zealand (IVCNZ 2019) /34./
dates 20191202
mrcbC20-s 20191204
place Dunedin
country NZ
RIV BD
FORD0 20000
FORD1 20200
FORD2 20205
reportyear 2020
num_of_auth 2
mrcbC52 4 A sml 4as 20241106135819.8
presentation_type PR
inst_support RVO:67985556
permalink http://hdl.handle.net/11104/0305414
mrcbC61 1
confidential S
contract
name Copyright receipt
date 20191013
mrcbT16-s 0.125
mrcbT16-E Q4
arlyear 2019
mrcbTft \nSoubory v repozitáři: haindl-0520496-CopyrightReceipt (1).pdf
mrcbU14 85078695401 SCOPUS
mrcbU24 PUBMED
mrcbU34 WOS
mrcbU63 cav_un_epca*0520495 International Conference on Image and Vision Computing New Zealand 2019 (IVCNZ 2019) 978-1-7281-4188-6 2151-2191 1 6 Piscataway IEEE 2019