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dc.contributor.authorMalekmohamadi, Hosseinen
dc.contributor.authorEmrith, Khemrajen
dc.contributor.authorPollard, Stephenen
dc.contributor.authorAdams, Guyen
dc.contributor.authorSmith, Melvynen
dc.contributor.authorSimske, Steveen
dc.date.accessioned2017-10-13T10:21:58Z
dc.date.available2017-10-13T10:21:58Z
dc.date.issued2014-01
dc.identifier.citationMalekmohamadi, H. et al (2014) Paper substrate classification based on 3D surface micro-geometry. Computer Vision Theory and Applications (VISAPP), 2014 International Conference on;en
dc.identifier.isbn9789897581335
dc.identifier.urihttp://hdl.handle.net/2086/14617
dc.description.abstractThis paper presents an approach to derive a novel 3D signature based on the micro-geometry of paper surfaces so as to uniquely characterise and classify different paper substrates. This procedure is extremely important to confront different conducts of tampering valuable documents. We use a 4-light source photometric stereo (PS) method to recover dense 3D geometry of paper surfaces captured using an ultra-high resolution sensing device. We derived a unique signature for each paper type based on the shape index (SI) map generated from the surface normals of the 3D data. We show that the proposed signature can robustly and accurately classify paper substrates with different physical properties and different surface textures. Additionally, we present results demonstrating that our classification model using the 3D signature performs significantly better as compared to the use of conventional 2D image based descriptors extracted from both printed and non-printed paper surfaces. Accuracy of the proposed method is validated over a dataset comprising of 21 printed and 22 non-printed paper types and a measure of classification success of over 92%is achieved in both cases (92.5% for printed surfaces and 96% for the non-printed ones).en
dc.publisherIEEEen
dc.subjectPaper Classificationen
dc.subjectPhotometric Stereoen
dc.subjectShape indexen
dc.subjectCo-occurrence Matricesen
dc.titlePaper substrate classification based on 3D surface micro-geometryen
dc.typeConferenceen
dc.peerreviewedYesen
dc.funderN/Aen
dc.projectidN/Aen
dc.cclicenceN/Aen
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en


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