Back to articles
Volume: 28 | Article ID: art00006
Image
Learning Optimal Incident Illumination using Spectral Bidirectional Reflectance Distribution Function Images for Material Classification
  DOI :  10.2352/ISSN.2470-1173.2016.9.MMRMA-358  Published OnlineFebruary 2016
Abstract

This article introduces a novel algorithm to learn optimal incident illumination for material classification using spectral bidirectional reflectance distribution function (BRDF) images. The method performs a joint selection of incident angle and spectral band in two steps: (1) clustering and selecting incident angles using statistics on the spectral BRDF images for a specific material, and (2) searching for the optimal angles and spectral bands that maximize material discriminability, which we measure in classification performance. The benefits of reducing the number of incident illumination angles include improving material classification, reducing computational time and storage, and allowing for a less cumbersome and potentially mobile imaging system. The authors show that their approach provides comparable material classification performance when using a reduced number of incident illuminations as compared with when using a larger number. They also compare their approach with prior work. © 2015 Society for Imaging Science and Technology.

Subject Areas :
Views 5
Downloads 0
 articleview.views 5
 articleview.downloads 0
  Cite this article 

Sandra Skaff, Siu-Kei Tin, Manuel Martinello, "Learning Optimal Incident Illumination using Spectral Bidirectional Reflectance Distribution Function Images for Material Classificationin Proc. IS&T Int’l. Symp. on Electronic Imaging: Measuring, Modeling, and Reproducing Material Appearance,  2016,  https://doi.org/10.2352/ISSN.2470-1173.2016.9.MMRMA-358

 Copy citation
  Copyright statement 
Copyright © Society for Imaging Science and Technology 2016
72010604
Electronic Imaging
2470-1173
Society for Imaging Science and Technology