Pigment identification based on spectral reflectance reconstructed from RGB images for cultural heritage investigations

Jay Arre Toque; Yusuke Murayama; Ari Ide-Ektessabi

doi:10.1117/12.840001

16 February 2010 Pigment identification based on spectral reflectance reconstructed from RGB images for cultural heritage investigations

Jay Arre Toque, Yusuke Murayama, Ari Ide-Ektessabi

Author Affiliations +

Proceedings Volume 7531, Computer Vision and Image Analysis of Art; 75310K (2010) https://doi.org/10.1117/12.840001
Event: IS&T/SPIE Electronic Imaging, 2010, San Jose, California, United States

Abstract

Common analysis techniques for artworks, such as X-ray based techniques, usually employ high-energy radiation sources. It also oftentimes requires the removal of material from the sample making the analysis relatively destructive. This is unacceptable for samples with high cultural value. Therefore, there is a need to develop alternative nondestructive and noninvasive analysis methods. This paper presents an approach for pigment estimation of Japanese paintings. Reflectance spectra were reconstructed from the RGB values of digital images with the help of multiple linear regression analysis. A reference database with the measured reflectance spectra of the most common pigments used in Japanese artworks was developed and used for identification by comparison and matching. Results have shown that estimation can be successfully performed with only 2% error. The estimation results show some promise that the system could become a powerful tool for the analysis of cultural heritage.

Citation Download Citation

Jay Arre Toque, Yusuke Murayama, and Ari Ide-Ektessabi "Pigment identification based on spectral reflectance reconstructed from RGB images for cultural heritage investigations", Proc. SPIE 7531, Computer Vision and Image Analysis of Art, 75310K (16 February 2010); https://doi.org/10.1117/12.840001

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available