The realization of color constancy on computer vision is important to recognize objects in varying light sources. This paper proposes a method to estimate the illuminant under the “Minimum Brightness Variance Assumption” which states that the variation of the brightness of the objects is as small as possible. In this method, the illuminant is estimated to be red when the red part of the object in the scene is bright. In detail, we define an evaluation function to calculate the variance of the brightness in the scene and we minimize the evaluation function to estimate the color of the illuminant and the color of the object. We conducted experiments with synthetic images and confirmed that the proposed method works well to reduce the influence of the illuminant for the objects in the scene.
The evaluation of the noise present in the image acquisition system and the influence of the noise is essential to image
acquisition. However the mean square errors (MSE) is not divided into two terms, i.e., the noise independent MSE
(MSEfree) and noise dependent MSE (MSEnoise) were not discussed separately before. The MSEfree depends on the
spectral characteristics of a set of sensors, illuminations and reflectances of imaged objects and the MSEfree arises in the
noise free case, however MSEnoise originates from the noise present image acquisition system.
One of the authors (N.S.) already proposed a model to separate the MSE into the two factors and also proposed a model
to estimate noise variance present in image acquisition systems. By the use of this model, we succeeded in the expression
of the MSEnoise as a function of the noise variance and showed that the experimental results agreed fairly well with the
expression when the Wiener estimation was used for the recovery. The present paper shows the extended expression for
the influence of the system noise on the MSEnoise and the experimental results to show the trustworthiness of the
expression for the regression model, Imai-Berns model and finite dimensional linear model.
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