Probabilistic Modelling of Printed Dots ...
Type de document :
Compte-rendu et recension critique d'ouvrage
Titre :
Probabilistic Modelling of Printed Dots at the Microscopic Scale
Auteur(s) :
Nguyen, Quoc Thong [Auteur correspondant]
Laboratoire de Mathématiques de Bretagne Atlantique [LMBA]
Delignon, Yves [Auteur]
Ecole nationale supérieure Mines-Télécom Lille Douai [IMT Lille Douai]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Septier, Francois [Auteur]
Ecole nationale supérieure Mines-Télécom Lille Douai [IMT Lille Douai]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Ho, Anh Thu Phan [Auteur]
Laboratoire Informatique, Image et Interaction - EA 2118 [L3I]
Laboratoire de Mathématiques de Bretagne Atlantique [LMBA]
Delignon, Yves [Auteur]
Ecole nationale supérieure Mines-Télécom Lille Douai [IMT Lille Douai]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Septier, Francois [Auteur]
Ecole nationale supérieure Mines-Télécom Lille Douai [IMT Lille Douai]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Ho, Anh Thu Phan [Auteur]
Laboratoire Informatique, Image et Interaction - EA 2118 [L3I]
Titre de la revue :
Signal Processing: Image Communication
Pagination :
129-138
Éditeur :
Elsevier
Date de publication :
2018-03
ISSN :
0923-5965
Mot(s)-clé(s) en anglais :
Metropolis Hastings within Gibbs
Bernoulli process
Probabilistic model
Microscopic printing
Markov chain
Bernoulli process
Probabilistic model
Microscopic printing
Markov chain
Discipline(s) HAL :
Sciences de l'ingénieur [physics]/Traitement du signal et de l'image [eess.SP]
Informatique [cs]/Modélisation et simulation
Sciences de l'ingénieur [physics]
Informatique [cs]
Informatique [cs]/Modélisation et simulation
Sciences de l'ingénieur [physics]
Informatique [cs]
Résumé en anglais : [en]
Microscopic analysis of paper printing shows regularly spaced dots whose random shape depends on the printing technology, the configuration of the printer as well as the paper properties. The modelling and identification ...
Lire la suite >Microscopic analysis of paper printing shows regularly spaced dots whose random shape depends on the printing technology, the configuration of the printer as well as the paper properties. The modelling and identification of paper and ink interactions are required for qualifying the printing quality, for controlling the printing process and for application in authentication as well. This paper proposes an approach to identify the authentic printer source using micro-tags consisting of microscopic printed dots embedded in the documents. These random shape features are modelled and extracted as a signature for a particular printer. In the paper, we propose a probabilistic model consisting of vector parameters using a spatial interaction binary model with inhomogeneous Markov chain. These parameters determine the location and describe the diverse micro random structures of microscopic printed dots. A Markov chain Monte Carlo (MCMC) algorithm is thus developed to approximate the Minimum Mean Squared Error estimator. The performance is assessed through numerical simulations. The real printed dots from the common printing technologies (conventional offset, waterless offset, inkjet, laser) are used to assess the effectiveness of the model.Lire moins >
Lire la suite >Microscopic analysis of paper printing shows regularly spaced dots whose random shape depends on the printing technology, the configuration of the printer as well as the paper properties. The modelling and identification of paper and ink interactions are required for qualifying the printing quality, for controlling the printing process and for application in authentication as well. This paper proposes an approach to identify the authentic printer source using micro-tags consisting of microscopic printed dots embedded in the documents. These random shape features are modelled and extracted as a signature for a particular printer. In the paper, we propose a probabilistic model consisting of vector parameters using a spatial interaction binary model with inhomogeneous Markov chain. These parameters determine the location and describe the diverse micro random structures of microscopic printed dots. A Markov chain Monte Carlo (MCMC) algorithm is thus developed to approximate the Minimum Mean Squared Error estimator. The performance is assessed through numerical simulations. The real printed dots from the common printing technologies (conventional offset, waterless offset, inkjet, laser) are used to assess the effectiveness of the model.Lire moins >
Langue :
Anglais
Vulgarisation :
Non
Projet ANR :
Collections :
Source :
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