bibtype C - Conference Paper (international conference)
ARLID 0506953
utime 20240103222330.6
mtime 20190726235959.9
SCOPUS 85061303226
WOS 000437494400032
DOI 10.1201/9780429505645
title (primary) (eng) Classification of digitized old maps
specification
page_count 6 s.
media_type P
serial
ARLID cav_un_epca*0506952
ISBN 978-0-429-50564-5
title Advances and Trends in Geodesy, Cartography and Geoinformatics
page_num 197-202
publisher
place Leiden
name Taylor & Francis Group, London, UK
year 2018
editor
name1 Molcikova
name2 S.
editor
name1 Hurcikova
name2 V.
editor
name1 Zeliznakova
name2 V.
editor
name1 Blistan
name2 P.
keyword old maps
keyword classification of digital images
keyword Bayesian classification
keyword web application
keyword web map services
author (primary)
ARLID cav_un_auth*0285297
share 50
name1 Talich
name2 M.
country CZ
author
ARLID cav_un_auth*0285298
share 25
name1 Böhm
name2 O.
country CZ
author
ARLID cav_un_auth*0101199
full_dept (cz) Zpracování obrazové informace
full_dept Department of Image Processing
department (cz) ZOI
department ZOI
full_dept Department of Image Processing
share 25
name1 Soukup
name2 Lubomír
institution UTIA-B
fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
source
url http://library.utia.cas.cz/separaty/2019/ZOI/soukup-0506953.pdf
cas_special
abstract (eng) Because of their importance as historical sources, old maps are steadily becoming more interesting to researchers and public users. However, the users are no longer satisfied only by simple digitization and on-line publication. Users primarily require advanced web tools for more sophisticated work with old maps. This paper is concerned with classification of digitized old maps in form of raster images. An automatic classification of digital maps is useful tools. This process allows to automatically de-tect areas with common characteristic, i.e. forests, water surfaces, buildings etc. Technically it is a problem of assigning the image's pixels to one of several classes defined in advance. If the map is georeferenced the classified image can be used to determine the surface areas of the clas-sified regions, or otherwise evaluate their position. Unfortunately quite substantial difficulties can be expected when attempting to apply these tools. The main cause of these difficulties is varied quality of digitized maps resulting from damage caused to the original maps by time or storage conditions and from varying scanning procedures. Even individual maps from the same map series can differ quite a lot. The review of the main classification methods with special emphasis on the Bayesian meth-ods of classification is given. An example of this classification and its use is also given. Web application of raster image classification is introduced as well. The web application can classify both individual images and raster data provided via Web Map Services (WMS) with respect to OGC standards (Open Geospatial Consortium). After gathering the data, classification is applied to distinguish separate regions in the image. User can choose between several classification methods and adjust pertinent parameters. Furthermore, several subsequent basic analytical tools are offered. The classification results and registration parameters can be saved for further use.
action
ARLID cav_un_auth*0377662
name 10th International Scientific and Professional Conference on Geodesy, Cartography and Geoinformatics, 2017
dates 20171010
place Demanovska Dolina
country SK
mrcbC20-s 20171013
RIV DE
FORD0 10000
FORD1 10500
FORD2 10508
reportyear 2020
presentation_type PR
inst_support RVO:67985556
permalink http://hdl.handle.net/11104/0298079
cooperation
ARLID cav_un_auth*0340296
name Výzkumný ústav geodetický, topografický a kartografický, v. v. i.
institution VÚGTK
country CZ
confidential S
mrcbC86 3+4 Proceedings Paper Geosciences Multidisciplinary
arlyear 2018
mrcbU14 85061303226 SCOPUS
mrcbU24 PUBMED
mrcbU34 000437494400032 WOS
mrcbU63 cav_un_epca*0506952 Advances and Trends in Geodesy, Cartography and Geoinformatics Taylor & Francis Group, London, UK 2018 Leiden 197 202 978-0-429-50564-5
mrcbU67 340 Molcikova S.
mrcbU67 340 Hurcikova V.
mrcbU67 340 Zeliznakova V.
mrcbU67 340 Blistan P.