Search

Images of the same object taken by multiple different cameras should have the same color reproduction. However, the images sometimes show different color reproduction due to the individual differences of cameras or internal camera parameters automatically determined when the images are taken. Conventional color transfer methods can be used for unifying the color reproduction of images by transforming the color distribution of an image to that of a reference image. However, conventional methods do not always lead to a good color reproduction and sometimes result in the loss of color impression of original images. In the present paper, we propose a color calibration method for images of the same object taken by different cameras. Two color transfer methods are combined to realize color calibration without the loss of color impression of an original image. Resultant images obtained by the color transfer methods are appropriately mixed into an output image. In experiments, the proposed method is applied to a variety of images and the effectiveness of the proposed method is confirmed by subjective and objective evaluations.

In the present paper, we propose an image contrast enhancement method that can enhance the contrast of a color image naturally by taking account of a color space shape. The proposed method realizes the natural enhancement based on two kinds of intensity histograms: a gradient-norm-based histogram and an ideal histogram derived from the shape of a color space. The former histogram is used to suppress over-enhancement in the flat regions of an image and the latter histogram is used to prevent the whole image from being darken. Concretely, the aforementioned intensity histograms are appropriately mixed into a histogram with a weight based on the average intensity of the input image. The contrast enhancement of the input image is realized using the cumulative histogram of the mixed histogram as an intensity transform function. To verify the validity of the proposed method, in experiments, the proposed method is applied to a variety of images and experimental results are evaluated qualitatively and quantitatively.

The dynamic range of human light perception for natural scenes is wider than the dynamic ranges of cameras. When an image of a natural scene is taken by a camera, the image may contain under-exposed or over-exposed regions. A remedy for this problem is to fuse multi-exposure images which are taken by the camera at different exposure levels. Since most fusion methods use a weighted average of multi-exposure images, the appropriate calculation of its weights is important. In this paper, we propose a method for fusing multi-exposure images which are newly generated by a light conversion with preserving hue and saturation. In the proposed method, the lightness conversion is based on local color correction (LCC) and a fused image is obtained by a weighted average whose weights are calculated based on saturation. The proposed method is applied to several images and the effectiveness of the proposed method is investigated.

In this paper, we propose a new color quantization method that can preserve infrequent salient colors of an original image. The infrequent salient colors mean that they are not dominant globally, but are dominant locally and are important to keep the impression of the original image. In the proposed method, color quantization is realized by k-means clustering and an input dataset for the clustering are adaptively and repeatedly modified based on local quantization errors to preserve the infrequent salient colors. The proposed method is implemented as an Android application to verify the feasibility of the use of the proposed method on mobile devices.

Generally, in color image contrast enhancement, the intensity or saturation component of an image is processed without the change of hue component of the image. In some color space used for contrast enhancement, processed pixel values are located outside of the gamut of the RGB color space. In that case, a clipping process is required to make the pixel values located inside of the gamut. However, the clipping process changes the combination of R, G, and B components and causes unexpected hue changes. In the present paper, we propose a hue-preserving contrast enhancement method for color images without gamut problem. In the proposed method, the contrast enhancement of a color image is realized by spreading out the distribution of pixel values on a constant hue plane in the RGB color space. The constant hue plane is represented as the triangle whose vertices correspond to white, black, and the maximally saturated colors. By spreading out the distribution of the pixel values on the constant hue plane, a remedy for the gamut problem and hue-preserving contrast enhancement are realized. Through experiments, the effectiveness of the proposed method is verified.

In the present paper, we address the problem of extrapolating group proximities from member relations, which we refer to as the group proximity problem. We assume that a relational dataset consists of several groups and that pairwise relations of all members can be measured. Under these assumptions, the goal is to estimate group proximities from pairwise relations. In order to solve the group proximity problem, we present a method based on embedding and distribution mapping, in which all relational data, which consist of pairwise dissimilarities or dissimilarities between members, are transformed into vectorial data by embedding methods. After this process, the distributions of the groups are obtained. Group proximities are estimated as distances between distributions by distribution mapping methods, which generate a map of distributions. As an example, we apply the proposed method to document and bacterial flora datasets. Finally, we confirm the feasibility of using the proposed method to solve the group proximity problem.

情動は，動物が生存していく上で非常に重要な要素であり，社会的な意思決定にも関係していると考えられている．本論文では，恐怖情動に基づく利己的な判断による社会的行動の創発を検証するために，強化学習と情動学習によって学習した価値に基づいて行動選択を行う意志決定法を提案する．強化学習ではタスクを達成するための行動規範を学習し，情動学習では環境からの入力に対する情動的な価値を学習する．自身に対する不快を回避するという情動の割り込み機能を模擬した意思決定では，強化学習と情動学習によって得られた価値を用いて，他のエージェントや状況に合わせて自身の行動を調整する．目標探索問題のシミュレーション実験の結果，提案手法を用いたエージェントが社会的行動である協調行動を創発すること，環境変化への適応性を有することを確認した．

RA (Rheumatoid Arthritis) is a chronic inflammatory joint disease characterized by a distinctive pattern of bone and joint destruction. To give an RA diagnosis, a hand radiograph is taken and analyzed. Hand bone radiograph analysis starts with the detection of the boundary of bones. It is, however, an extremely exhausting and time consuming task for radiologists, not only because of the complexity, but also because of the precision required for a correct diagnosis. Automatic bone boundary detection is thus required. The Level Set Method has been widely used in boundary detection. However, the convergence and stability of the level set are strongly affected by the speed function and the parameters of the level set, which often leads to either a complete breakdown or a premature termination of the curve evolution process, resulting in unsatisfactory results. In this paper, we propose a modified speed function of the level set for bone boundary detection in hand radiographs. And in order to preserve the boundary of an image and to reduce noise, we further apply diffusion filter to substitute Gaussian Filter in the standard Level Set Method. Evaluating the experiments using a particular set of hand bones radiographs, the proposed method worked well for almost all of the images that we used.

In this paper, we present a method for parameter tuning of membership functions in Takagi-Sugeno (T-S) fuzzy model using Particle Swarm Optimization (PSO). This is applied to plaque boundary extraction in Intravascular Ultrasound (IVUS) image. Searching areas for coronary plaque boundaries are automatically set by using weighted image separability and some heuristic rules. The coronary plaque boundaries are interpolated by polynomials inferred by fuzzy rules. PSO tunes the parameters of the membership functions in the antecedent parts of the fuzzy rules. The accuracy of the proposed method is better than that of our previous method.

At Nissan we have developed many different environmental technologies to date and launched sales of an electric vehicle (EV) in December 2010 that achieves zero carbon dioxide and zero exhaust emissions. Besides its outstanding environmental performance, this zero-emission EV also embodies new potentialities not found in conventional gasoline-engine vehicles. The responsiveness and controllability of the electrified powertrain (EV drive motor) combine to provide an unprecedented driving experience that transforms conventional thinking about automobiles.This paper presents an EV drive torque control system that takes advantage of this EV-unique performance to provide handling capabilities for driving ease that anyone can enjoy.