About: Sparse image extrapolation using different inpainting algorithms

Facets (new session)
Description
Metadata
Settings
- owl:sameAs
- Inference Rule:

About: Sparse image extrapolation using different inpainting algorithms Goto Sponge NotDistinct Permalink

An Entity of Type : http://linked.opendata.cz/ontology/domain/vavai/Vysledek, within Data Space : linked.opendata.cz associated with source document(s)

Attributes	Values
rdf:type	skos:Concept http://linked.opendata.cz/ontology/domain/vavai/Vysledek
Description	Image inpainting via approximately solving underdetermined systems of linear equations can take different forms. State of the art methods use sparse solutions of such systems to inpaint (i.e. fill-in) the missing part of an image. Some of these approaches are applicable for image extrapolation as well, but this cannot be seen just as a special case of standard inpainting problem. For example, usual methods assume filling the holes from different directions, which is not tractable in the case of extrapolation. In this paper some of the algorithms that are tailored to inpainting are introduced and modified for use in image extrapolation. We use K-SVD algorithm that trains a dictionary for optimal sparse representation, MCA (Morphological Component Analysis) that expects two incoherent dictionaries for representing separately cartoon and texture. The last algorithm present is the statistics-based EM (Expectation Maximization). The performance of these algorithms for image extrapolation is compared on rea Image inpainting via approximately solving underdetermined systems of linear equations can take different forms. State of the art methods use sparse solutions of such systems to inpaint (i.e. fill-in) the missing part of an image. Some of these approaches are applicable for image extrapolation as well, but this cannot be seen just as a special case of standard inpainting problem. For example, usual methods assume filling the holes from different directions, which is not tractable in the case of extrapolation. In this paper some of the algorithms that are tailored to inpainting are introduced and modified for use in image extrapolation. We use K-SVD algorithm that trains a dictionary for optimal sparse representation, MCA (Morphological Component Analysis) that expects two incoherent dictionaries for representing separately cartoon and texture. The last algorithm present is the statistics-based EM (Expectation Maximization). The performance of these algorithms for image extrapolation is compared on rea (en)
Title	Sparse image extrapolation using different inpainting algorithms Sparse image extrapolation using different inpainting algorithms (en)
skos:prefLabel	Sparse image extrapolation using different inpainting algorithms Sparse image extrapolation using different inpainting algorithms (en)
skos:notation	RIV/00216305:26220/12:PU100049!RIV14-MSM-26220___
http://linked.open...avai/riv/aktivita	S
http://linked.open...avai/riv/aktivity	S
http://linked.open...vai/riv/dodaniDat	2014
http://linked.open...aciTvurceVysledku	Špiřík, Jan Zátyik, Ján
http://linked.open.../riv/druhVysledku	D - Článek ve sborníku
http://linked.open...iv/duvernostUdaju	S - Úplné a pravdivé údaje nepodléhající ochraně podle zvláštních právních předpisů
http://linked.open...titaPredkladatele	Vysoké učení technické v Brně / Fakulta elektrotechniky a komunikačních technologií
http://linked.open...dnocenehoVysledku	170160
http://linked.open...ai/riv/idVysledku	RIV/00216305:26220/12:PU100049
http://linked.open...riv/jazykVysledku	eng - angličtina
http://linked.open.../riv/klicovaSlova	image extrapolation, sparse, K-SVD, MCA, EM (en)
http://linked.open.../riv/klicoveSlovo	EM MCA sparse K-SVD image extrapolation
http://linked.open...ontrolniKodProRIV	[FA8598282ADD]
http://linked.open...v/mistoKonaniAkce	Vrátna, SK
http://linked.open...i/riv/mistoVydani	Neuveden
http://linked.open...i/riv/nazevZdroje	Proceedings of the 14th International Conference on Research in Telecommunication Technologies
http://linked.open...in/vavai/riv/obor	JA
http://linked.open...ichTvurcuVysledku	2 (xsd:int)
http://linked.open...cetTvurcuVysledku	2 (xsd:int)
http://linked.open...UplatneniVysledku	2012
http://linked.open...iv/tvurceVysledku	Špiřík, Jan Zátyik, Ján
http://linked.open...vavai/riv/typAkce	EUR - Evropská
http://linked.open.../riv/zahajeniAkce	2012-09-01 (xsd:date)
number of pages	5 (xsd:int)
http://purl.org/ne...btex#hasPublisher	Neuveden
https://schema.org/isbn	978-80-554-0569-8
http://localhost/t...ganizacniJednotka	26220

Faceted Search & Find service v1.16.118 as of Jun 21 2024

Alternative Linked Data Documents: ODE Content Formats:

RDF

ODATA

Microdata

About

OpenLink Virtuoso version 07.20.3240 as of Jun 21 2024, on Linux (x86_64-pc-linux-gnu), Single-Server Edition (126 GB total memory, 58 GB memory in use)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2024 OpenLink Software