Illuminant color estimation by hue categorization based on gray world assumption

Harumi Kawamura; Shunichi Yonemura; Jun Ohya; Norihiko Matsuura

doi:10.1117/12.872398

7 February 2011 Illuminant color estimation by hue categorization based on gray world assumption

Harumi Kawamura, Shunichi Yonemura, Jun Ohya, Norihiko Matsuura

Proceedings Volume 7873, Computational Imaging IX; 787312 (2011) https://doi.org/10.1117/12.872398
Event: IS&T/SPIE Electronic Imaging, 2011, San Francisco Airport, California, United States

Abstract

This paper proposes a gray world assumption based method for estimating an illuminant color from an image by hue categorization. The gray world assumption hypothesizes that the average color of all the objects in a scene is gray. However, it is difficult to estimate an illuminant color correctly if the colors of the objects in a scene are dominated by certain colors. To solve this problem, our method uses the opponent color properties that the average of a pair of opponent colors is gray. Thus our method roughly categorizes the colors derived from the image based on hue and selects them one by one from the hue categories until selected colors satisfy the gray world assumption. In our experiments, we used three kinds of illuminants (i.e., CIE standard illuminants A and D₆₅, and a fluorescent light) and two kinds of data sets. One data set satisfies the gray world assumption, and the other does not. Experiment results show that estimated illuminants are closer to the correct ones than those obtained with the conventional method and the estimation error for both using CIE standard illuminants A and D65 by our method are within the barely noticeable difference in human color perception.

Citation Download Citation

Harumi Kawamura, Shunichi Yonemura, Jun Ohya, and Norihiko Matsuura "Illuminant color estimation by hue categorization based on gray world assumption", Proc. SPIE 7873, Computational Imaging IX, 787312 (7 February 2011); https://doi.org/10.1117/12.872398

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