About: Bayesian supervised segmentation of objects in natural images using low-level information

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

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rdf:type	skos:Concept http://linked.opendata.cz/ontology/domain/vavai/Vysledek
Description	Detection of particular meaningful objects in images is of great importance in many fields, including image retrieval or image quality analysis. In this contribution, eleven frequently observed objects (areas) in natural images are learned and detected. The presented algorithm is based on region merging and Bayesian decision theory. The main goal is not perfect recognition, since for that purpose it is necessary to use higher-level knowledge about the image content. Merging of segments proceeds only up to a reliable point, not to merge different categories. Unique merging criteria combine the values of probabilities attached to the segments for all the most likely categories, color and texture features and information about edges. Results are demonstrated on a few images and discussed. Detection of particular meaningful objects in images is of great importance in many fields, including image retrieval or image quality analysis. In this contribution, eleven frequently observed objects (areas) in natural images are learned and detected. The presented algorithm is based on region merging and Bayesian decision theory. The main goal is not perfect recognition, since for that purpose it is necessary to use higher-level knowledge about the image content. Merging of segments proceeds only up to a reliable point, not to merge different categories. Unique merging criteria combine the values of probabilities attached to the segments for all the most likely categories, color and texture features and information about edges. Results are demonstrated on a few images and discussed. (en)
Title	Bayesian supervised segmentation of objects in natural images using low-level information Bayesian supervised segmentation of objects in natural images using low-level information (en)
skos:prefLabel	Bayesian supervised segmentation of objects in natural images using low-level information Bayesian supervised segmentation of objects in natural images using low-level information (en)
skos:notation	RIV/67985556:_____/03:00330336!RIV10-AV0-67985556
http://linked.open...avai/riv/aktivita	Z
http://linked.open...avai/riv/aktivity	Z(AV0Z1075907)
http://linked.open...vai/riv/dodaniDat	2010
http://linked.open...aciTvurceVysledku	Boldyš, Jiří
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	Ústav teorie informace a automatizace AV ČR, v. v. i.
http://linked.open...dnocenehoVysledku	599563
http://linked.open...ai/riv/idVysledku	RIV/67985556:_____/03:00330336
http://linked.open...riv/jazykVysledku	eng - angličtina
http://linked.open.../riv/klicovaSlova	belief networks; decision theory; image segmentation (en)
http://linked.open.../riv/klicoveSlovo	image segmentation belief networks decision theory
http://linked.open...ontrolniKodProRIV	[DD8A775C5D99]
http://linked.open...v/mistoKonaniAkce	Rome
http://linked.open...i/riv/mistoVydani	Roma
http://linked.open...i/riv/nazevZdroje	Proceedings of the 3rd International Symposium on Image and Signal Processing and Analysis, 2003
http://linked.open...in/vavai/riv/obor	BD
http://linked.open...ichTvurcuVysledku	1 (xsd:int)
http://linked.open...cetTvurcuVysledku	1 (xsd:int)
http://linked.open...UplatneniVysledku	2003
http://linked.open...iv/tvurceVysledku	Boldyš, Jiří
http://linked.open...vavai/riv/typAkce	WRD - Světová
http://linked.open.../riv/zahajeniAkce	2003-09-18 (xsd:date)
http://linked.open...n/vavai/riv/zamer	Uncertainty methods in theoretical cybernetics: system identification, information processing, decision-making, and control
number of pages	6 (xsd:int)
http://purl.org/ne...btex#hasPublisher	IEEE
https://schema.org/isbn	953-184-061-X

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