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dc.contributor.authorKopenkov, V. N.
dc.contributor.editorSkala, Václav
dc.date.accessioned2018-05-18T08:30:51Z-
dc.date.available2018-05-18T08:30:51Z-
dc.date.issued2016
dc.identifier.citationWSCG '2016: short communications proceedings: The 24th International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision 2016 in co-operation with EUROGRAPHICS: University of West Bohemia, Plzen, Czech RepublicMay 30 - June 3 2016, p. 129-135.en
dc.identifier.isbn978-80-86943-58-9
dc.identifier.issn2464-4617
dc.identifier.uriwscg.zcu.cz/WSCG2016/!!_CSRN-2602.pdf
dc.identifier.urihttp://hdl.handle.net/11025/29696
dc.description.abstractThe article deal with technology of digital images processing on the base of non-linear algorithms. This approach allows to construct the efficient procedure of local image processing. The aim of this research is to workout an algorithm of processing images with predetermined computational complexity and the best quality of processing on the existing data set avoiding a problem of retraining or lesstraining. To achieve this aim we use local discrete wavelet transformation and hierarchical regression to construct local image processing procedure on the base of a training dataset. Moreover, we workout method to estimate the necessity of finishing or continuing the training process. This method is based on of the functional of full cross-validation control which allow to construct processing procedure with predetermined computational complexity and veracity, and with the best quality.en
dc.format7 s.cs
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherVáclav Skala - UNION Agencyen
dc.relation.ispartofseriesWSCG '2016: short communications proceedingsen
dc.rights© Václav Skala - UNION Agencycs
dc.subjectmístní zpracovánícs
dc.subjecthierarchická regresecs
dc.subjectintervalový odhadcs
dc.subjectfunkce kompletní kluzné kontroly kvalitycs
dc.titleDevelopment of computational procedure of local image processing, based on the usage of hierarchical regressionen
dc.typekonferenční příspěvekcs
dc.typeconferenceObjecten
dc.rights.accessopenAccessen
dc.type.versionpublishedVersionen
dc.subject.translatedlocal processingen
dc.subject.translatedhierarchical regressionen
dc.subject.translatedinterval estimateen
dc.subject.translatedfunction of complete sliding quality controlen
dc.type.statusPeer-revieweden
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