Paper type classification based on a new 3D surface texture measure
A novel three-dimensional (3D) surface texture measure (3DSTM) is presented based on the micro-geometry of paper surfaces to classify different paper substrates. This is useful to automatically determine whether a document is printed on the correct paper substrate to help identify fraud. We use a 4-light source photometric stereo (PS) method to recover the dense 3D geometry of paper surfaces captured using a high-resolution sensing device. We derive a unique 3DSTM 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 3DSTM can robustly and accurately classify paper substrates with different physical properties and different surface textures. The accuracy of the proposed method is validated over a dataset comprising of 21 printed and 22 non-printed paper types and a measure of success over 92% is achieved.
Citation : Malekmohamadi, H. et al. (2014) Paper type classification based on a new 3D surface texture measure. Electronic Letters, 50 (8), pp. 596-598
ISSN : 0013-5194
Research Institute : Institute of Artificial Intelligence (IAI)
Peer Reviewed : Yes