Generalization and localization based style imitation for grayscale images

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dc.citation.epage473en_US
dc.citation.spage465en_US
dc.citation.volumeNumber2869en_US
dc.contributor.authorNar, F.en_US
dc.contributor.authorÇetin, Atılımen_US
dc.coverage.spatialAntalya, Turkeyen_US
dc.date.accessioned2019-01-31T13:22:34Z
dc.date.available2019-01-31T13:22:34Zen_US
dc.date.issued2003en_US
dc.departmentDepartment of Computer Engineeringen_US
dc.descriptionDate of Conference: November 3-5, 2003en_US
dc.descriptionConference name: International Symposium on Computer and Information Sciences 18th International Symposiumen_US
dc.description.abstractAn example based rendering (EBR) method based on generalization and localization that uses artificial neural networks (ANN) and k-Nearest Neighbor (k-NN) is proposed. The method involves learning phase and application phase, which means that once a transformation filter is learned, it can be applied to any other image. In learning phase, error back-propagation learning algorithm is used to learn general transformation filter using unfiltered source image and filtered output image. ANNs are usually unable to learn filter-generated textures and brush strokes hence these localized features are stored in a feature instance table for using with k-NN during application phase. In application phase, for any given grayscale image, first ANN is applied then k-NN search is used to retrieve local features from feature instances considering texture continuity to produce desired image. Proposed method is applied up to 40 image filters that are collection of computer-generated and human-generated effects/styles. Good results are obtained when image is composed of localized texture/style features that are only dependent to intensity values of pixel itself and its neighbors.en_US
dc.identifier.doi10.1007/978-3-540-39737-3_58en_US
dc.identifier.doi10.1007/b14229en_US
dc.identifier.issn0302-9743
dc.identifier.urihttp://hdl.handle.net/11693/48629en_US
dc.language.isoEnglishen_US
dc.publisherSpringer, Berlin, Heidelbergen_US
dc.relation.isversionofhttps://doi.org/10.1007/978-3-540-39737-3_58en_US
dc.relation.isversionofhttps://doi.org/10.1007/b14229en_US
dc.source.titleComputer and Information Sciences - ISCIS 2003en_US
dc.subjectArtificial Neural Networken_US
dc.subjectGrayscale Imageen_US
dc.subjectTexture Synthesisen_US
dc.subjectBrush Strokeen_US
dc.subjectApplication Phaseen_US
dc.titleGeneralization and localization based style imitation for grayscale imagesen_US
dc.typeConference Paperen_US

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