For dichromats to receive the information represented in color images, it is important to study contrast improvement methods and quantitative evaluation indices of color conversion results. There is an index to evaluate the degree of contrast improvement and in this index, the contrast for dichromacy caused by the lightness component is given importance. In addition, random sampling was introduced in the computation of this index. Although the validity of the index has been shown through comparison with a subjective evaluation, it is considered that the following two points should be examined. First, should contrast for normal trichromacy caused by the lightness component also be attached importance. Second, the influence of random sampling should be examined in detail. In this paper, a new index is proposed and the above-mentioned points are examined. For the first point, the following is revealed through experiment. Consideration of the contrast for normal trichromacy caused by a lightness component that is the same as that for dichromacy may or may not result in a good outcome. The evaluation performance of the proposed index is equivalent to that of the previous index overall. It can be said that the proposed index is superior to the previous one in terms of the unity of evaluating contrast. For the second point, the computation time and the evaluation of significant digits are shown. In this paper, a sampling number such that the number of significant digits can be considered as three is used. In this case, the variation caused by random sampling is negligible compared with the range of the proposed index, whereas the computation time is about one-seventh that when the sampling is not adopted.

During the execution of software systems, their execution data can be recorded. By fully exploiting these data, software practitioners can discover behavioral models describing the actual execution of the underlying software system. The recorded unstructured software execution data may be too complex, spanning over several days, etc. Applying existing discovery techniques results in spaghetti-like models with no clear structure and no valuable information for comprehension. Starting from the observation that a software system is composed of a set of logical components, Liu et al. propose to decompose the software behavior discovery problem into smaller independent ones by discovering a behavioral model per component in [1]. However, the effectiveness of the proposed approach is not fully evaluated and compared with existing approaches. In this paper, we evaluate the quality (in terms of understandability/complexity) of discovered component behavior models in a quantitative manner. Based on evaluation, we show that this approach can reduce the complexity of the discovered model and gives a better understanding.

In this letter, a quantitative evaluation index of contrast improvement of color images for dichromats is proposed. The index is made by adding two parameters to an existing index to make evaluation results consistent with human evaluation results. The effectiveness and validity of the proposed index are verified by experiments.

This paper proposes a method for evaluating visual differences caused by decimation. In many applications it is important to evaluate visual differences of two different images. There exist many image assessment methods that utilize the model of the human visual system (HVS), such as the visual difference predictor (VDP) and the Sarnoff visual discrimination model. In this paper, we extend and elaborate on the conventional image assessment method for the purpose of evaluating the visual difference caused by the image decimation. Our method matches actual human evaluation more and requires less computational complexity than the conventional method.

We developed a quantitative evaluation method for luminance and color uniformity on a display screen. In this paper, we report the analysis result of a viewer perception of luminance and color uniformity. In experiments, observers subjectively evaluated Mura images which were showed on the light emitting diode (LED) backlight screen by adjusting the luminance of each LED. We measured the luminance and color distributions of the Mura images by a 2D colorimeter, then, the measured data was converted into S-CIELAB. In S-CIELAB calculations, two dimensional MTF (Modulation Transfer Function) of human eye were used in which anisotropic properties of the spatial frequency response of human vision were considered. Some indexes for a quantitative evaluation model were extracted by the image processing. The significant indexes were determined by the multiple regression analysis to quantify the degree of uniformity of the backlight screen. The luminance uniformity evaluation model and color uniformity evaluation model were derived from this analysis independently. In addition, by integrating both of these models we derived a quantitative evaluation model for luminance and color unevenness simultaneously existing on the screen.

In order to perform the registration of virtual objects in vision-based augmented reality systems, the estimation of the relation between the real and virtual worlds is needed. This paper presents a three-point vision-based registration method for video see-through augmented reality systems using binocular cameras. The proposed registration method is based on a combination of monocular and stereoscopic registration methods. A correction method that performs an optimization of the registration by correcting the 2D positions in the images of the marker feature points is proposed. Also, an extraction strategy based on color information is put forward to allow the system to be robust to fast user's motion. In addition, a quantification method is used in order to evaluate the stability of the produced registration. Timing and stability results are presented. The proposed registration method is proven to be more stable than the standard stereoscopic registration method and to be independent of the distance. Even when the user moves quickly, our developed system succeeds in producing stable three-point based registration. Therefore, our proposed methods can be considered as interesting alternatives to produce the registration in binocular augmented reality systems when only three points are available.

To enhance safety and traffic efficiency, a driver assistance system and an autonomous vehicle system are being developed. A preceding vehicle recognition method is important to develop such systems. In this paper, a vision-based preceding vehicle recognition method, based on supervised learning from sample images is proposed. The improvement for Modified Quadratic Discriminant Function (MQDF) classifier that is used in the proposed method is also shown. And in the case of road environment recognition including the preceding vehicle recognition, many researches have been reported. However in those researches, a quantitative evaluation with large number of images has rarely been done. Whereas, in this paper, over 1,000 sample images for passenger vehicles, which are recorded on a highway during daytime, are used for an evaluation. The evaluation result shows that the performance in a low order case is improved from the ordinary MQDF. Accordingly, the calculation time is reduced more than 20% by using the proposed method. And the feasibility of the proposed method is also proved, due to the result that the proposed method indicates over 98% as classification rate.

This paper first discusses the software reusability-based development process in a lifecycle and reusable objects modification process called differentiation. Next, the supporting environment is described. Both the method and the environment allow developers to carry out requirement definitions, specification and implementation in a reusable way. Some quantitative evaluations are given about how productivity and quality have been improved by using this method and environment, based on a number of case studies made on development projects. Reusability has been largely improved by differential specification, and adjustment method and a direct transformation capability.