bibtype C - Conference Paper (international conference)
ARLID 0313521
utime 20240111140708.1
mtime 20081029235959.9
title (primary) (eng) Texture Oriented Image Inpainting based on Local Statistical Model
specification
page_count 6 s.
media_type CD-ROM
serial
ARLID cav_un_epca*0313520
title Proc. 10th IASTED Conf. on Signal & Image Processing, SIP 2008
page_num 15-20
publisher
place Calgary
name ACTA Press
year 2008
editor
name1 Cristea
name2 P.
title (cze) Texturně orientovaná metoda obrazového vkreslování založená na lokálním statistickém modelu
keyword Image Restoration
keyword Image Inpainting
keyword Color Texture Prediction
keyword Local Texture Model
keyword Gaussian Mixtures
keyword EM Algorithm
author (primary)
ARLID cav_un_auth*0101091
name1 Grim
name2 Jiří
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*0101197
name1 Somol
name2 Petr
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*0101182
name1 Pudil
name2 Pavel
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*0243189
name1 Míková
name2 I.
country CZ
author
ARLID cav_un_auth*0231129
name1 Malec
name2 M.
country CZ
source
source_type pdf
url http://library.utia.cas.cz/separaty/2008/RO/grim-texture oriented image inpainting based on local statistical model.pdf
cas_special
project
project_id 1M0572
agency GA MŠk
ARLID cav_un_auth*0001814
project
project_id 2C06019
agency GA MŠk
country CZ
ARLID cav_un_auth*0216518
project
project_id GA102/08/0593
agency GA ČR
ARLID cav_un_auth*0239567
project
project_id GA102/07/1594
agency GA ČR
ARLID cav_un_auth*0228611
research CEZ:AV0Z10750506
abstract (eng) Image inpainting as a means of substituting missing image parts can become difficult when the image is textured. In this paper we apply a local statistical model of the source color image with the aim to predict missing texture regions. We have shown in a series of papers that textures can be modeled locally by estimating the joint probability density of spectral pixel values in a suitably chosen observation window. For the sake of image inpainting we estimate the joint multivariate density in the form of a Gaussian mixture of product components. The missing image region is inpainted iteratively by step-wise prediction of the unknown spectral values.
abstract (cze) Obrazové vkreslování (automatické nahrazování chybějících částí obrazu) je obzvláště obtížné v případě texturních obrázků. Aplikujeme lokální statistický model využívající posuvného okna, který umožňuje rekonstruovat texturní vlastnosti. Pro účely vkreslování odhadujeme vícerozměrnou hustotu ve formě gausovské směsi součinových komponent. Chybějící části obrazu jsou vkreslovány iterativně.
action
ARLID cav_un_auth*0243073
name 10th IASTED Conf. on Signal & Image Processing, SIP 2008
place Kailua-Kona, HI
dates 18.08.2008-20.08.2008
country US
reportyear 2009
RIV BD
permalink http://hdl.handle.net/11104/0164319
arlyear 2008
mrcbU56 pdf
mrcbU63 cav_un_epca*0313520 Proc. 10th IASTED Conf. on Signal & Image Processing, SIP 2008 15 20 Calgary ACTA Press 2008
mrcbU67 Cristea P. 340