This paper proposes a method for estimating illuminant colors that have two different light sources, i.e. fluorescent light
and daylight. The conventional methods assume that one light source exists in a scene or in a small region and that
it spans a scene uniformly. Therefore the methods cannot estimate illuminant colors when two different light sources
are illuminating the scene or the region. Our method formulates the relationships among the colors of two regions that
have the same surface reflectance but are in different locations and have different illumination rates (which vary from
location to location). In order to clarify the unknown surface reflectance common to each color region, the method uses
the property that the colors derived in the regions comprise the plane through the origin in a three-dimensional color space.
By determining the normal of the plane, which is unique to the surface reflectance, the coefficients of the basis function of
surface reflectance are derived. In this way, we can estimate illumination rates, that is, the colors of the scene illuminants.
The results of numerical simulations using the reflectance dataset from the ISO-TR 16066 database and two illuminants (a
typical fluorescent lamp and sunlight) show that the estimated illumination rates are similar to the ground truth.
A new approach is proposed for estimating illuminant colors from color images under an unknown scene illuminant.
The approach is based on a combination of a gray-world-assumption-based illuminant color estimation method and a
method using color gamuts. The former method, which is one we had previously proposed, improved on the original
method that hypothesizes that the average of all the object colors in a scene is achromatic. Since the original method
estimates scene illuminant colors by calculating the average of all the image pixel values, its estimations are incorrect
when certain image colors are dominant. Our previous method improves on it by choosing several colors on the basis
of an opponent-color property, which is that the average color of opponent colors is achromatic, instead of using all
colors. However, it cannot estimate illuminant colors when there are only a few image colors or when the image colors
are unevenly distributed in local areas in the color space. The approach we propose in this paper combines our previous
method and one using high chroma and low chroma gamuts, which makes it possible to find colors that satisfy the gray
world assumption. High chroma gamuts are used for adding appropriate colors to the original image and low chroma
gamuts are used for narrowing down illuminant color possibilities. Experimental results obtained using actual images
show that even if the image colors are localized in a certain area in the color space, the illuminant colors are accurately
estimated, with smaller estimation error average than that generated in the conventional method.
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.
We propose a personal image processing system for face recognition. It locates a subject's face in the image and recognizes the person using only simple commercial devices such as an NTSC video camera and a personal computer. This system can locate a subject's face and recognize the person in real-time without imposing strict conditions on the background, by using mosaic pattern matching. We describe the system configuration and its algorithm, and show experimental results for various faces and environments.
We propose the region-based dichromatic (RBD) estimation method for illumination color estimation. The method realizes illumination color estimation and, at the same time, illumination independent color object segmentation, even if the highlights in the image are generated by several objects of different color. The dichromatic reflection theory, which was proposed by S. A. Shafer and T. Kanade, yields a method that sometimes outputs incorrect illumination color when the color of high luminance matte region is similar to the illumination color. The proposed method solves this problem by extracting highlights as bicomponent regions. The RBD estimation method proceeds as follows: (1) extraction of high luminance regions, (2) grouping of similar color regions based on color difference in the L*u*v* uniform color space, (3) extraction of highlight regions which contain two reflection components, (4) illumination color estimation by finding the point that minimizes the summation of the distance to all highlight lines in each highlight region. The experimental results for real images show that the RBD estimation method estimates the illumination color more correctly than the conventional method.
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