Two methods for color constancy are proposed in this paper. Both are based on the correlation matrix on the three-dimensional space of colors, red, green and blue. In the first method, the eigenvector corresponding to the largest eigenvalue is assumed to be a good estimate of the illumination color, and the influence of the illumination color is eliminated from the input image. In the second method, it is assumed that the eigenvector corresponding to the largest eigenvalue presents the color gray when the illumination is white The image under white illumination is obtained by a iteration method so as to satisfy the condition that the eigenvector corresponding to the largest eigenvalue presents the color gray. These two methods were compared to a widely used typical method for color constancy, the Gray-world method, by simulation experiments using synthesized images and real images, and the effectiveness of the proposed methods was validated by these experiments.

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 gray-world assumption is one of the most famous methods to estimate and eliminate a biased illumination or a colored illumination. However, the gray-world assumption is not always true when the colors of the objects in the scene are biased. To deal only with the biased illumination but not with the biased colors of the objects, we propose a novel method to estimate the biased illumination. Because the objects in the scene do not illuminate or contribute to the luminance, we can estimate the biased illumination by the correlation between the luminance and the colors in the scene. In this paper, proposed method and the experimental results are presented and the proposed method estimated the biased illumination even though the colors of the objects in the scene are biased. © 2013 ISSN 1881-803X.

The acquisition of accurate spectral reflectances of objects through the use of image data is very important to reproduce realistic color images under a variety of viewing illuminants. The accuracy of recovered spectral reflectances is usually evaluated by the mean square errors (MSE) between the measured and the recovered reflectances. In the evaluation of the influence of the noise on the image acquisition, dividing the MSE into two terms, i.e., the noise independent MSE (MSE free) and noise dependent MSE (MSE noise) is essential to analyze the influence of the noise. A model separating the MSE into the two factors and estimating the noise variance was already proposed by one of the authors, and later it was modified to a comprehensive model and was also applied to the noise estimates. The present paper shows the experimental results on the extended application of the comprehensive model.

It is well known that the noise present in an image acquisition system plays important roles in solving inverse problems, such as the reconstruction of spectral reflectances of imaged objects from the sensor responses. Usually, a recovered spectral reflectance vector (r) over cap by a matrix W is expressed by (r) over cap Wp, where p is a sensor response vector. In this paper, the mean square errors (MSEs) between the recovered spectral reflectances with various reconstruction matrices W and actual spectral reflectances are divided into the noise independent MSE (MSEFREE) and the noise dependent MSE (MSENOISE). By dividing the MSE into two terms, the MSENOISE is defined as the estimated noise variance multiplied by the sum of the squared singular values of the matrix W. It is shown that the relation between the increase in the MSE and the MSENOISE agrees quite well with the experimental results by the multispectral camera, and that the estimated noise variances are of the same order of magnitude for various matrices W, but the increase in the MSE by the noise mainly results from the increase in the sum of the squared singular values for the unregularized reconstruction matrix W. (C) 2010 Optical Society of America

Recovery of spectral reflectances of objects being imaged through the use of sensor responses is important to reproduce color images under various illuminations. Although the Wiener estimation is usually used for the recovery, the recovery performance of the estimation depends on the autocorrelation matrix of the spectral reflectances and the noise present in an image acquisition system. The purpose of the present paper is to show that the Wiener estimation with the noise variance estimated by the previous proposal [IEEE Trans. Image Process. 16, 1848 ( 2006)] and with the autocorrelation matrix that uses the features of the spectral reflectances recovered by the previous method is very effective in greatly improving the performance. (C) 2010 Optical Society of America

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.

Colorimetric evaluation of an image acquisition device is important for evaluating and optimizing a set of sensors. We have already proposed a colorimetric evaluation model [J. Imaging Sci. Technol. 49, 588-593 (2005)] based on the Wiener estimation. The mean square errors (MSE) between the estimated and the actual fundamental vectors by the Wiener filter and the proposed colorimetric quality (Qc) agree quite well with the proposed model and we have shown that the estimation of the system noise variance of the image acquisition system is essential for the evaluation model. In this paper, it is confirmed that the proposed model can be applied to two different reflectance recovery models, and these models provide us an easy method for estimating the proposed colorimetric quality (Qc). The influence of the system noise originates from the sampling intervals of the spectral characteristics of the sensors, the illuminations and the reflectance and the quantization error on the evaluation model are studied and it is confirmed from the experimental results that the proposed model holds even in a noisy condition. (C) 2009 Optical Society of America

Accurate recovery of spectral reflectances is important for color reproduction under a variety of illuminations, and several models have been proposed to recover them. To evaluate the quality (Q(r)) of an image acquisition system aimed at recovery of spectral reflectances, we proposed an evaluation model based on Wiener estimation and showed that mean square errors between the recovered and measured spectral reflectances as a function of Or agree quite well with the prediction from the model, and that estimation of the noise variance of the image acquisition system is essential to the evaluation model. In this paper, the evaluation model was applied to two different reflectance recovety methods, and it is confirmed that the proposed model can be applied to different methods. (c) 2008 Society for Imaging Science and Technology.

A colorimetric evaluation of an image acquisition device is important for evaluating and optimizing a set of sensors. We have already proposee a colorimetric evaluation model [J. Imaging Sci. Technol. 49(6). 588-593 (2005)] based on the Wiener estimation. The mean square errors (MSE) between the estimated and the actual fondamental vectors by the Wiener filter agrees quite well with the proposed model and we have shown that the estimation of the noise variance of the image acquisition System is essential to the evaluation model. We also have confirmed that the proposed model can be applied to two different reflectance recovery models and that the proposed colorimetric quality (Qc) can be estimated by applying these models without a prior knowledge of the spectral characteristics of the sensors, the recording illuminant and the noise present in the image acquisition device, which are required for the estimation by the Wiener model. In this paper, the effects of the sampling intervais of the spectral reflectance and the quantization error to the evaluation model are studied and il is confirmed from the experimental results that at least 8-bit depth of the image data and 20-nm sampling intervais are required to apply the evaluation model accurately. © 2008 Society for Imaging Science and Technology.

Recovery of spectral reflectances of imaged objects is important to reproduce color images under arbitrary illuminations. In this paper, a new method is proposed to recover spectral reflectances accurately through the use of image data without prior knowledge of the imaged objects. It is shown that the Wiener estimation which uses the noise variance estimated by training samples and the features of the recovered spectral reflectances is effective to improve the recovery performance.

Acquisition of spectral information of objects being imaged through the use of sensor responses is important to reproduce color images under various illuminations. In the past several models have been proposed to recover the spectral reflectances from sensor responses. The accuracy of the spectral reflectances recovered by five different models is compared by using multispectral cameras. It is shown that the Wiener estimation that uses the noise variance estimated as proposed in IEEE Trans. Image Process. 15, 1848 (2006) recovers the spectral reflectances more accurately than the others when the test samples are different from learning samples. (c) 2007 Optical Society of America.

Acquisition of spectral information of objects being imaged through the use of sensor responses is important to reproduce color images under various illuminations. In the past several models have been proposed to recover the spectral reflectances from sensor responses. The accuracy of the spectral reflectances recovered by five different models is compared by using multispectral cameras. It is shown that the Wiener estimation that uses the noise variance estimated as proposed in IEEE Trans. Image Process. 15, 1848 (2006) recovers the spectral reflectances more accurately than the others when the test samples are different from learning samples. (c) 2007 Optical Society of America.

Acquisition of the spectral reflectances of imaged objects through the use of sensor responses is important for color reproduction of images under various illuminations. The accuracy of the recovered spectral reflectances depends not only on the spectral sensitivities of sensors but also on the noise present in a device. For the first time, the mean square errors (MSE) between the measured and recovered spectral reflectances were separated into noise dependent and noise independent MSE based on the proposed model. Experiments were performed to study the influence of the bit-depth of the analog to digital conversion and of the sampling intervals over the visible wavelength on the MSE. It is shown that the sum of the theoretically calculated noise dependent and independent MSE agrees fairly well with the MSE of the spectral reflectances recovered by multispectral cameras. It is also confirmed that the noise dependent MSE increases with a decrease in the bit-depth and that the noise independent MSE increases with an increase in the sampling intervals. The separation of the MSE into noise dependent and independent MSE gives a powerful tool for the accurate image acquisition.

On our studies, we have focused on the traditional dance of Japan recorded in image database. In this study, we aim to reproduce the traditional dance motion recorded in image database. In this paper, we proposed an estimate method of dance motion with the multivariate autoregressive model. The multi-articular motion is a multivariate feedback system which gives the effect to each other. We analyzed the motion of three-variables (shoulder, elbow and wrist). As the result, it was able to be confirmed that the wrist motion was controlled for the shoulder motion in the low frequency region. The elbow motion has been separated without affecting from other joint. Using the multivariate autoregressive model, we estimated the motion with the effect between joints and get good result about an estimate of dance motion.

This paper describes a method of solid modelling using the three-dimensional polyharmonic function of volume distribution. This method requires the arbitary points or lines on the free-form surface of solid model. The free-form surface is defined using algebra sum. The free-form surfaces of solid model are searched, after solving the simultaneous linear algebric equation. In this paper, the polyharmonic functions of volume distribution and their related functions are deduced. These functions effectively smooth the obtained surface, even if functions of low order are used. In order to investigate the efficiency of this method, several examples are given.

This paper describes a method for generating a free-form surface by a boundary integral equation and mapping. This method requires knowing the boundary geometry of the free-form surface and setting arbitrary points on the free-form surface. First, a standard surface is developed onto a plain surface by mapping. The height of the points on the free-form surface is calculated, after solving the discretized boundary integral equation. These points are inverse-mapped onto the original space. In order to investigate the efficiency of this method, several examples are performed.

Noise reduction is one of significant problems for Moire image processing. The detection of true con-tour lines in a noisy Moire image often requires to rejoint contours split by noises. We developed a recursive search method and a loop controlled method which identify the direction of short segment of lines in a small area to connect and repair contour lines. Unfortunately, these two mehtods can not connect contour lines without errors. When they accept human judgements, however, they are efficient and useful enough to find contour lines because the human effort to indicate whether to connect two lines or not is very slight. With these methods we can obtain contour lines more easily, faster, more accurately than before.

デジタルミュージアムは貴重な絵画を画像データベースとして蓄積し、画像をディスプレイ上にリアルに再現することを試みるものである。作品の印象は昼光や燭光など照明光の分光分布やその強度などの照明環境で大幅に変化する。このように多様な照明環境で人間が見た時の色彩を視覚系の色順応を考慮し画像再現する研究が極めて活発に行われている。 本報告書では日本画、油彩画および水彩画の３種類の絵画の分光反射率を絵画を描くのに用いた色材の先験情報を用いずに画像データから正確に復元し、多様な照明環境の下で観察したときの美術作品の色彩を忠実にディスプレイ上に再現するための研究成果について述べる。

画像データから分光反射率を復元することは多様な照明の下でカラー画像を再現するために重要である。この復元にウィーナ推定が一般的に用いられているが、本推定性能は分光反射率の自己相関行列と画像入力系に存在するノイズに依存する。本研究の目的はすでに提案した方法で推定されたノイズ分散と同提案により復元された分光反射率の特徴を自己相関行列に採用したウィーナ推定方法が復元性能を大幅に改善するのに有用であることを示すことである。

スキャナーを用いてエマルション・チェンバーで観測されている宇宙線ファミリーデータの測定がどの程度可能であるかを調べてきた。これまでに得られた結果はつぎのようにまとめることができる。 1)フォトメータを用いて測定されたシャワーの黒化度とスキャナーによる測定で得られた黒化度の間には非常によい相関があり、スキャナーを用いて宇宙線ファミリーデータの測定は可能である。 2)シャワースポットの取り出し(すなわち、ノイズスポットの除去)は真のシャワースポット中での局所的な黒化度の分布がノイズスポットのそれとは異なることを利用して、その特徴を取り出すことによりある程度まで行うことができた。さらにニューラルネットワークを用いた識別方法も適用してみたが、完全にノイズスポットを取り除くまでには至らなかった。シャワーを特徴づけるパラメータの取り方などもう少し工夫が必要かもしれない。しかし、ノイズスポットが完全に除去できなくても、以下のような方法でシャワーの取り出し、ファミリーの取り出しは可能である。 3)シャワーの取り出し(トラッキング)については、シャワースポットの配置の幾何学的な条件を考慮した方法と、ニューラルネットワークによる方法の2通りを適用してみた。前者の方法では、あらかじめ少なくとも1個のシャワースポットについて、こらが下の層にあるどのシャワースポットに対応するかの検討をしておけば、ほぼ確実にシャワートラックを同定でき、また、その中から宇宙線ファミリーに属するシャワーの同定もできることがわかった。取り出す基準がチェンバーブロックにより異なるのか、常に同じパラメータを用いてよいのか、今後さらに適用事例を増して調べる必要がある。ニューラルネットワークによる方法では、模擬的に作った事例についてその有効性を調べた。ノイズが含まれる場合、各層のフィルムにズレがある場合など様々な場合について考えてみたが誤ったトラッキングが少し含まれるものの、大部分は正しいトラッキングが出来ていることがわかった。実際の実験データに適用して、その有効性を確かめる必要がある。