Digital imaging and signal processing technologies offer new methods for inkjet and photographic media engineers and manufacturers, and those responsible for product quality control, to classify and characterize printing materials surface textures using new and more quantitative methods.
This paper presents a collaborative project to systematically and semi-automatically characterize the surface texture of inkjet media. These methods have applications in product design and specification, and in manufacturing quality control.Surface texture is a critical feature in the
manufacture, marketing and use of inkjet papers, especially those used for fine art printing. Raking light reveals texture through a stark rendering of highlights and shadows. Though raking light photomicrographs effectively document surface features of inkjet paper, the sheer number and diversity
of textures prohibits efficient visual classification. This work provides evidence that automatic, computer-based classification of texture documented with raking light photomicrographs is feasible by demonstrating an encouraging degree of success sorting a set of 120 photomicrographs made
from diverse samples of inkjet paper and canvas available in the market from 2000 through 2011.The samples used for this study were drawn from the
Paul Messier, Richard Johnson, Henry Wilhelm, William A. Sethares, Andrew G. Klein, Patrice Abry, Stéphane Jaffard, Herwig Wendt, Stephane Roux, Nelly Pustelnik, Nanne van Noord, Laurens van der Maaten, Eric Postma, "Automated Surface Texture Classification of Inkjet and Photographic Media" in Proc. IS&T Int'l Conf. on Digital Printing Technologies and Digital Fabrication (NIP29), 2013, pp 85 - 91, https://doi.org/10.2352/ISSN.2169-4451.2013.29.1.art00025_1