• DocumentCode
    254055
  • Title

    A New Perspective on Material Classification and Ink Identification

  • Author

    Shiradkar, Rakesh ; Li Shen ; Landon, George ; Ong, Sim Heng ; Ping Tan

  • Author_Institution
    Nat. Univ. of Singapore, Singapore, Singapore
  • fYear
    2014
  • fDate
    23-28 June 2014
  • Firstpage
    2275
  • Lastpage
    2282
  • Abstract
    The surface bi-directional reflectance distribution function (BRDF) can be used to distinguish different materials. The BRDFs of many real materials are near isotropic and can be approximated well by a 2D function. When the camera principal axis is coincident with the surface normal of the material sample, the captured BRDF slice is nearly 1D, which suffers from significant information loss. Thus, improvement in classification performance can be achieved by simply setting the camera at a slanted view to capture a larger portion of the BRDF domain. We further use a handheld flashlight camera to capture a 1D BRDF slice for material classification. This 1D slice captures important reflectance properties such as specular reflection and retro-reflectance. We apply these results on ink classification, which can be used in forensics and analyzing historical manuscripts. For the first time, we show that most of the inks on the market can be well distinguished by their reflectance properties.
  • Keywords
    cameras; identification; image classification; 1D BRDF slice; 2D function; camera principal axis; captured BRDF slice; forensics; handheld flashlight camera; historical manuscript analysis; information loss; ink classification; ink identification; material classification performance; near isotropic; reflectance properties; retro-reflectance; specular reflection; surface bi-directional reflectance distribution function; Accuracy; Cameras; Ink; Lighting; Surface reconstruction; Three-dimensional displays; BRDF; ink identification; material classification;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer Vision and Pattern Recognition (CVPR), 2014 IEEE Conference on
  • Conference_Location
    Columbus, OH
  • Type

    conf

  • DOI
    10.1109/CVPR.2014.291
  • Filename
    6909688