論文

About 250 million people in the world suffer from color vision deficiency (CVD).Contact lenses and glasses with a color filter are available to partially improve the vision of people with CVD. Tinted glasses uniformly change the colors in a user’s field of view (FoV), which can improve the contrast of certain colors while making others hard to identify. On the other hand,an optical see-through head-mounted display (OST-HMD) provides a new alternative by applying a controllable overlay to a user’s FoV. The method of color calibration for people with CVD, such as the Daltonization process, needs to make the calibrated color darker, which has not yet been featured on recent commercial OST-HMDs. We propose a new approach to realize light subtraction on OST-HMDs using a transmissive LCD panel, a prototype system, named ALCC-glasses, to validate and demonstrate the new arriving light chroma controllable augmented reality technology for CVD compensation.

© 2019 Zhejiang University and Zhejiang University Press This paper describes an extendable graphical framework, aflak, which provides a visualization and provenance management environment for the analysis of multi-spectral astronomical datasets. Via its node editor interface, aflak allows the astronomer to compose transforms on input datasets queryable from public astronomical data repositories, then to export the results of the analysis as Flexible Image Transport System (FITS) files, in a manner such that the full provenance of the output data be preserved and reviewable, and that the exported file be usable by other common astronomical analysis software. FITS is the standard of data interchange in astronomy. By embedding aflak's provenance data into FITS files, we both achieve interoperability with existing software and full reproducibility of the process by which astronomers make discoveries.

© 2019 The Authors More than 100 million individuals around the world suffer from color vision deficiency (CVD). Image recoloring algorithms have been proposed to compensate for CVD. This study has proposed a new recoloring algorithm to make up shortages of contrast enhancement and naturalness preservation of the state-of-the-art methods. The recoloring task is formulated as an optimization problem that is solved by using the colors in a simulated CVD color space to maximize contrast and to preserve the original color as much as possible. In addition, the dominant colors are extracted for recoloring. They are then propagated to the whole image so that the optimization problem could be solved at a reasonable cost independent of the image size. In the quantitative evaluation, the results of the proposed method are competitive with those of the best existing method. The evaluation involving subjects with CVD demonstrates that the proposed method outperforms the state-of-the-art method in preserving both the information and the naturalness of images.

Color vision deficiency (CVD) is caused by anomalies in the cone cells of the human retina. It affects approximately 200 million individuals throughout the world. Although previous studies have proposed compensation methods, contrast and naturalness preservation have not been adequately and simultaneously addressed in the state-of-the-art studies. This paper focuses on red–green dichromats’ compensation and proposes a recoloring algorithm that combines contrast enhancement and naturalness preservation in a unified optimization model. In this implementation, representative color extraction and edit propagation methods are introduced to maintain global and local information in the recolored image. The quantitative evaluation results showed that the proposed method is competitive with state-of-the-art methods. A subjective experiment was also conducted and the evaluation results revealed that the proposed method obtained the best scores in preserving both naturalness and information for individuals with severe red–green CVD.

With the advent of the high-performance graphics and networking technologies that enable us to create virtual worlds networked via the Internet, various virtual environments have been developed to support mathematics education at around the beginning of the 21st century. In the environments that have been inherently two- or threedimensional Euclidean, students have discovered and experienced mathematical concepts and processes in almost the same ways that they can do in real life. Although elementary mathematics, for instance, calculus and linear algebra, plays an essential role in areas of understanding and knowledge to solve real-world problems, there are traditionally three general areas in pure mathematics for advanced problem-solving techniques: algebra, analysis, and geometry. So, using Virtual Reality (VR) as a general and advanced tool for mathematics education to support students not only in the primary and secondary, also in higher education,the virtual environment ideally provides a wide variety of mathematical domains as possible. We present an immersive virtual environment that allows the user to set environmental limits beyond three-dimensional Euclidean space. More specifically, by setting the limits to n-dimensional complex projective space, an element of both complex and infinitely distant domain can be naturally visualized as a recognizable form in the Euclidean 3-space. The problem here is that the higher the level of mathematics, the more the visualization method tends to become abstract that only experts with advanced degrees can fathom. We also show how our figurative approach is essential for bridging the gap between elementary and more advanced mathematical visualizations.

This paper describes an extendable graphical framework,aflak, which provides a collaborative visualization environment for the analysis of multi-spectral astronomical datasets. aflak allows the astronomer to share and define analytic pipelines through a node editing interface, in which the user can compose together a set of built-in transforms (e.g. dataset import, integration, Gaussian fit) over astronomical datasets. Not only is aflak fast and responsive,but its macro can be conveniently exported, imported and shared among researchers using a custom data interchange format. aflak,while providing domain-specific features for astronomy,enables collaboration through shareability,guarantees end-to-end provenance management and achieves astronomer-in-the-loop. This paper compares aflak with conventional tools used in astronomy for specific use cases,such as the computing of equivalent widths. Finally,the paper demonstrates that aflak provides a reference implementation for the Provenance DM data model.

Real paper gets deformed anisotropically due to a uni-directional placement of the inner fibers. Few existing CG researchers allow for such fibers to represent virtual papers, and thus anisotropic paper deformation has not been well represented. In this study, we present a two-dimensional visual simulation model for anisotropic papers, which abstracts mutual mechanical relationships among intersecting fibers by a network of filler and hinge springs, and incorporates connection points for keeping the shape of each bended fiber. By releasing the network connections when the filler and hinge spring extend above a certain limit, the paper provides a plausible tear. We succeeded in generating a different appearance of the torn-off line of papers according as the pulling direction.

A contour tree is a powerful tool for delineating the topological evolution of isosurfaces of a single-valued function, and thus has been frequently used as a means of extracting features from volumes and their time-varying behaviors. Several sophisticated algorithms have been proposed for constructing contour trees while they often complicate the software implementation especially for higher-dimensional cases such as time-varying volumes. This paper presents a simple yet effective approach to plotting in 3D space, approximate contour trees from a set of scattered samples embedded in the high-dimensional space. Our main idea is to take advantage of manifold learning so that we can elongate the distribution of high-dimensional data samples to embed it into a low-dimensional space while respecting its local proximity of sample points. The contribution of this paper lies in the introduction of new distance metrics to manifold learning, which allows us to reformulate existing algorithms as a variant of currently available dimensionality reduction scheme. Efficient reduction of data sizes together with segmentation capability is also developed to equip our approach with a coarse-to-fine analysis even for large-scale datasets. Examples are provided to demonstrate that our proposed scheme can successfully traverse the features of volumes and their temporal behaviors through the constructed contour trees.

Visual field defect (VFD) is a type of ophthalmic disease that causes the loss of part of the patient’s field of view (FoV). In this paper, we propose a method to enlarge the restricted FoV with an optical see-through head-mounted display (OST-HMD) equipped with a camera that captures an overview and overlays it on the persisting FoV. Because the overview window occludes the real background scene, it is important to create a balance between the augmented contextual information and the unscreened local information. We recruited twelve participants and conducted an experiment to seek the best size, position, and opacity for the overview window through a Whac-A-Mole task (a touchscreen game). We found that the performance was better when the overview window was of medium size (FoV of 9.148 degree x 5.153 degree, nearly one third of FoV of the used OST-HMD) and placed lower in the visual field. Either too large or too small a size decreases the performance. The performance increases with increased opacity. The obtained results can legitimate the default setting for the overview window.

Weathering, referring to techniques for depicting outdoor objects as having suffered rain and wind damage, has a long history of research, and many methods can be found in the literature. Metal is a representative target of weathering, as metallic surface corrosion is a widespread deterioration phenomenon in our daily life. However, few weathering methods consider the effects of rust-preventive paint, while real metal is usually treated with a preservative. This paper presents a novel procedural method for weathering coated metal objects. In our method, a coated surface is imposed on a 3D triangular mesh. We enable the mechanical deterioration of coating film, and we deform the mesh model to express cracking and peeling. As further defacing elements on the coating film, runoff rust and darkening are reproduced by changing the color and reflectance of the surface. Besides, by locally adjusting the control parameters of the designated areas on the surface, the process of deterioration can be arbitrarily directed. Several visual simulation results are shown to prove empirically the faithfulness of the proposed methods.

When a musical instrument player performs music, the accompanying visual information can have a significant effect on the performance. In this paper, we present an ambient music co-player (AMP) as a system that generates background videos in response to the impromptu performance of a single musical instrument player. The AMP system evaluates the performance to interpret the real player's emotional impression and generates an influential video based on the results of the evaluation. The player tends to change their performance while being inspired by the generated video, further triggering the system to modify the video. The AMP system aims to establish an affective loop where the system continues applying stimuli to the performance of the real player. The final goal of this study is to make the system act as a “co-player” of the player and to amplify the quality of the player's performing experience entirely through interactions between the two. By conducting a user evaluation, it was proven that the AMP system was able to inspire an amateur guitarist as the subject through affective video generation and to make his performance better than when playing alone.

In consideration of interdependency between image sensing/recognition (computer vision, CV) and 3D image synthesis (computer graphics, CG) in visual computing, Keio University, Department of Information and Computer Science, reorganized its first course on CV and CG in the undergraduate program into a series of three courses on visual computing in the 2019 academic year. One salient feature of these courses is its newly introduced programming assignment with two specific goals: to experience GPU computing and to understand the path tracing algorithm. The purpose is to help students easily understand the trend in visual computing and vividly envision the future CG area. Specifically, two types of tasks were given to students: material design and analysis of the relationship between sampling and noise. The educational material builds on Google Colaboratory, a cloud-based development environment, to be independent of the students’ hardware. Owing to the judicious design, students can unhesitatingly work on the programming assignment with relatively inexpensive hardware, such as laptop PCs, tablets, or even smartphones, whenever they have a standard off-campus network environment. In the second academic year (2020) of these courses, this type of exercise was especially valuable for students who had to take the courses online because of the COVID-19 pandemic. The effects of the new courses with the educational materials were empirically proven in terms of quantitative and qualitative perspectives.

Several image recoloring methods have been proposed to compensate for the loss of contrast caused by color vision deficiency (CVD). However, these methods only work for dichromacy (a case in which one of the three types of cone cells loses its function completely), while the majority of CVD is anomalous trichromacy (another case in which one of the three types of cone cells partially loses its function). In this paper, a novel degree-adaptable recoloring algorithm is presented, which recolors images by minimizing an objective function constrained by contrast enhancement and naturalness preservation. To assess the effectiveness of the proposed method, a quantitative evaluation using common metrics and subjective studies involving 14 volunteers with varying degrees of CVD are conducted. The results of the evaluation experiment show that the proposed personalized recoloring method outperforms the state-of-the-art methods, achieving desirable contrast enhancement adapted to different degrees of CVD while preserving naturalness as much as possible.