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dc.contributor.authorKitanovski, Vlado
dc.contributor.authorPedersen, Marius
dc.date.accessioned2018-04-10T07:38:53Z
dc.date.available2018-04-10T07:38:53Z
dc.date.created2018-04-09T11:58:37Z
dc.date.issued2018
dc.identifier.issn2313-433X
dc.identifier.urihttp://hdl.handle.net/11250/2493320
dc.description.abstractThis article addresses methods for detection of orientation-modulation data embedded in color dispersed-dot-halftone images. Several state-of-the-art methods for detection of orientation-embedded data in printed halftone images have been proposed, however they have only been evaluated independently without comparing with each other. We propose an improved detection method, which is using Principal Component Analysis (PCA) components as oriented-feature extractors, and a probabilistic model for the print-and-scan channel for maximum likelihood detection. The proposed detector and four state-of-the-art detectors are compared with each other in terms of correct detection rate, using a comprehensive testing set of printed natural images captured with three different devices. The proposed detector achieves highest correct detection rate using fewer feature extractors than the other methods, and it is significantly more robust to non-calibrated devices used for capturing the printed images. This is mostly due to the improved PCA-based oriented-feature extractors that are responsive to the embedded orientations and robust and insensitive to the other visual content.nb_NO
dc.language.isoengnb_NO
dc.publisherMDPInb_NO
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.titleDetection of Orientation-Modulation Embedded Data in Color Printed Natural Imagesnb_NO
dc.typeJournal articlenb_NO
dc.typePeer reviewednb_NO
dc.description.versionpublishedVersionnb_NO
dc.source.volume4nb_NO
dc.source.journalJournal of Imagingnb_NO
dc.source.issue4nb_NO
dc.identifier.doi10.3390/jimaging4040056
dc.identifier.cristin1578319
dc.description.localcode© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).nb_NO
cristin.unitcode194,63,10,0
cristin.unitnameInstitutt for datateknologi og informatikk
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1


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Navngivelse 4.0 Internasjonal
Except where otherwise noted, this item's license is described as Navngivelse 4.0 Internasjonal