A key compositional technique, leading lines guide the observer’s gaze across visual scenes. This study presents a novel approach to the visual analysis of leading lines in artworks by applying discrete Morse theory to saliency maps refined through persistent homology. The saliency map’s gradient field is abstracted into a structured representation using the maximum graph, enabling the identification of both explicit and implicit leading lines. To enhance the analysis, texture gradient cues are incorporated to embed depth information into the saliency map. Our visual analysis system also features semiautomatic thresholding and tools to depict the direction of visual guidance. An experimental study with participants from an art university validated the system’s feasibility by analyzing various Western paintings. This research contributes to the field of digital and public humanities and computational art analysis by offering an environment in which to explore visual composition.

Diffusion models have achieved great success in dual-conditioned image generation. However, they still face significant challenges in image-guided sketch colorization, where reference and sketch images usually exhibit different semantics and spatial structures. This mismatch, termed “distribution shift” in this peper, results in various artifacts and degrades the colorization quality. To address this issue, we conducted thorough investigations into the image-prompted latent diffusion model and developed a two-stage training framework to mitigate the effects of distribution shift based on our analysis. Comprehensive quantitative comparisons, qualitative evaluations, and user studies were performed to demonstrate the superiority of our proposed methods. Additionally, ablation studies were conducted to assess the impact of the distribution shift and the selection of reference embeddings. Codes are made publicly available at https://github.com/tellurion-kanata/colorizeDiffusion.

Virtual reality (VR) live concerts have recently attracted much attention because of their various possibilities for future music entertainment in cyberworlds. However, it has been reported that current VR live experiences do not reach the levels of enthusiasm observed at actual live concerts, and achieving a sense of enthusiasm in VR live concerts is a challenge to offer enough engagement. According to studies in the field of social psychology, psychological interactions with the surrounding audience are crucial to generating a sense of enthusiasm at actual live events. In response, this paper proposes a method for synthesizing enthusiastic collective non-player characters (NPCs) for VR live concerts. The proposed method computes the psychological interactions among the audience members, including the viewer, according to two psychological parameters. The synthesized audience NPCs are affected by both the music and their psychological interactions according to the surrounding audience, including the viewer, and they exhibit the features of an enthusiastic collective crowd. As such, an empirical study was conducted to explore the psychological influence of the proposed audience NPCs on VR live experiences. The results indicate that the proposed method could give the impression of an enthusiastic crowd at VR live experiences. In addition, it was suggested that social sensations between the audience NPCs and the viewer should be enhanced to increase the level of enthusiasm portrayed during VR live experiences. What was further suggested is the need for a certain degree of individual difference and diversity in appearance and motions for a natural collective audience.

Creating realistic materials is essential in the construction of immersive virtual environments. While existing techniques for material capture and conditional generation rely on flash-lit photos, they often produce artifacts when the illumination mismatches the training data. In this study, we introduce DiffMat, a novel diffusion model that integrates the CLIP image encoder and a multi-layer, cross-attention denoising backbone to generate latent materials from images under various illuminations. Using a pre-trained StyleGAN-based material generator, our method converts these latent materials into high-resolution SVBRDF textures, a process that enables a seamless fit into the standard physically based rendering pipeline, reducing the requirements for vast computational resources and expansive datasets. DiffMat surpasses existing generative methods in terms of material quality and variety, and shows adaptability to a broader spectrum of lighting conditions in reference images.

The digitization of material objects is rapidly advancing, progressively offering people an unprecedented level of convenience and accessibility. However, studies have shown that people feel a lesser attachment to digital contents than to material objects. In this paper, we focus on shelves, thought to be hubs for cultivating individual relationships with material objects in daily life. We thus propose HomeShelf, an application that accelerates interactions with digital contents and fosters deeper attachments to them. Further, we discuss the features to be added using new technologies and the impact on digitized material objects.

People with metamorphopsia suffer from perceiving things in a distorted way. Various methods for examining metamorphopsia have been suggested in the current literature, with the most advanced techniques demonstrating the ability to yield quantitative measurements. However, these cutting-edge methods necessitate extended examination durations and impose challenging manipulations on patients. In this study, our objective is to enhance the time efficiency of the inspection process and alleviate the burden placed on the user. We propose a novel user-friendly quantitative inspection system which utilizes interactive reinforcement learning. Instead of having users directly operate the system, we ask them to evaluate the stimuli generated by the system. Based on their evaluations, the system gradually refines the deformation map representing the distortion perceived by the user. The reinforcement learning scheme is implemented using relevance feedback approach based on optimum-path forest classifier. To evaluate the effectiveness of the proposed system, subjective evaluation experiments involving simulated and real metamorphopsia participants were conducted in this study. The experimental findings reveal that, when compared to the state-of-the-art method, our proposed system yields comparable inspection out-comes while significantly reducing both the inspection duration and the mental workload.

Abstract Game creators always use prefabricated models as non‐player characters (NPCs) to create an atmosphere for game scenes. However, players are likely to see different NPCs with the same 3D models because creators usually cannot prepare a high number of NPC models for big game scenes. In this article, we present an anime‐like character customization system, where each customizing parameter can adjust the shape or color for the corresponding part of the character model. Based on this system, we propose an improved approach for generating a rich variety of 3D anime‐like NPCs including body models and clothing items in different styles. We introduce a neural network to control the facial appearances, Gaussian mixture models to control the colors of hair and clothes, and a Bayesian network to control the outfits of clothing items. We demostrate that the proposed approach can maintain the variety and stability of the generated characters.

This paper proposes a stable method for reconstructing spatially varying appearances (SVBRDFs) from multiview images captured under casual lighting conditions. Unlike flat surface capture methods, ours can be applied to surfaces with complex silhouettes. The proposed method takes multiview images as inputs and outputs a unified SVBRDF estimation. We generated a large-scale dataset containing the multiview images, SVBRDFs, and lighting appearance of vast synthetic objects to train a two-stream hierarchical U-Net for SVBRDF estimation that is integrated into a differentiable rendering network for surface appearance reconstruction. In comparison with state-of-the-art approaches, our method produces SVBRDFs with lower biases for more casually captured images.

In modern society, digital signage installed in many large-scale facilities supports our daily life. However, with a limited screen size, it is difficult to provide different types of information for many viewers at varying distances from the screen simultaneously. Therefore, in this study, we extend existing research on the use of hybrid images for tiled displays. To facilitate smoother information selection, a new interactive display method is proposed that incorporates a touchactivated widget as a high-frequency part of the hybrid image; these widgets are novel in that they are more visible to the viewers near to the display. We develop an authoring tool that we call the Hybrid Image Display Resolutions Optimizer (HYDRO); it features two kinds of control functions by which to optimize the visibility of the touch-activated widgets in terms of placement and resolution. The effectiveness of the present method is shown empirically via a quantitative user study and an eye-tracking-based qualitative evaluation.

Human beings perceive scenes by enlarging an area of interest (AOI), whereas an ordinal camera captures the entire scene evenly. Therefore, it is not easy to share subjective impressions with others effectively only by sharing normal photos. Such challenges exist in cyberworlds as well. In this study, we design Digitus View Finder (DVF), a system that automatically outputs images close to the scenes a user perceives. A preliminary user study indicated that the DVF would be able to estimate a plausible scale factor of the AOI as a function of three parameters: the number of subjects in the AOI, the distance to the subjects, and a personal difference as a correction term.

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.

Blazars are celestial bodies of high interest to astronomers. In particular, through the analysis of photometric and polarimetric observations of blazars, astronomers aim to understand the physics of the blazar's relativistic jet. However, it is challenging to recognize correlations and time variations of the observed polarization, intensity, and color of the emitted light. In our prior study, we proposed TimeTubes to visualize a blazar dataset as a 3D volumetric tube. In this paper, we build primarily on the TimeTubes representation of blazar datasets to present a new visual analytics environment named TimeTubesX, into which we have integrated sophisticated feature and pattern detection techniques for effective location of observable and recurring time variation patterns in long-term, multi-dimensional datasets. Automatic feature extraction detects time intervals corresponding to well-known blazar behaviors. Dynamic visual querying allows users to search long-term observations for time intervals similar to a time interval of interest (query-by-example) or a sketch of temporal patterns (query-by-sketch). Users are also allowed to build up another visual query guided by the time interval of interest found in the previous process and refine the results. We demonstrate how TimeTubesX has been used successfully by domain experts for the detailed analysis of blazar datasets and report on the results.

Autopsy reports play a pivotal role in forensic science. Medical examiners (MEs) and diagnostic radiologists (DRs) cross-reference autopsy results in the form of autopsy reports, while judicial personnel derive legal documents from final autopsy reports. In our prior study, we presented a visual analysis system called the forensic autopsy system for e-court instruments (FORSETI) with an extended legal medicine markup language (x-LMML) that enables MEs and DRs to author and review e-autopsy reports. In this paper, we present our extended work to incorporate provenance infrastructure with authority management into FORSETI for forensic data accountability, which contains two features. The first is a novel provenance management mechanism that combines the forensic autopsy workflow management system (FAWfMS) and a version control system called lmmlgit for x-LMML files. This management mechanism allows much provenance data on e-autopsy reports and their documented autopsy processes to be individually parsed. The second is provenance-supported immersive analytics, which is intended to ensure that the DRs’ and MEs’ autopsy provenances can be viewed, listed, and analyzed so that a principal ME can author their own report through accountable autopsy referencing in an augmented reality setting. A fictitious case with a synthetic wounded body is used to demonstrate the effectiveness of the provenance-aware FORSETI system in terms of data accountability through the experience of experts in legal medicine.

Even when generated with the same weapon, a wound's appearance would be affected by its use and the assaulter's physique. In contrast, even if wounds look similar in shape and/or color, they could have a different wounding history. In this work, we strive to extract the features of shape and color from a given wound image and build on the mathematical model of meaning to create the associated semantics of the criminal act. We demonstrate that a system called THEMIS (theoretical estimation of the meaning of insults) provides a context-sensitive visual similarity analysis for wound imagery in computational forensics. The THEMIS system can help forensic doctors and e-court stakeholders potentially determine important aspects of a case through a comparison with wounds of corpses in past cases, in a way that is not currently possible.

Metamorphopsia is a symptom caused by abnormalities in the retina, and people with metamorphopsia experience distortions in their field of view. To compensate for this disorder, computer-based distortion assessment and compensation methods have been proposed. In the state-of-the-art method, compensation is carried out by dynamically deforming the image with a manipulation map, which is bound to the symptom of an individual user with metamorphopsia, according to the user's gaze data captured by an eye tracker. However, this method suffers from the instability and inaccuracy of the eye tracker, which leads to a poor compensation effect. In this paper, we propose a novel method for metamorphopsia compensation without using an eye tracker. The proposed method generates a compensation image by simultaneously applying multiple manipulation maps to the input image. To evaluate the effectiveness of the proposed method, a preliminary subjective experiment using a reading task and involving 10 participants with normal vision was conducted. The evaluation results show that the proposed method can compensate for visual distortion during reading tasks.

In this paper, a novel compositor to realize illumination-aware digital image compositing is proposed. Digital image compositing is a technique to re-synthesize a single image from multiple visual elements. Assembling separated visual elements leads to highly efficient content creation at risk of losing visual coherence. Visual coherence has two types: positional and illumination. Compositing while maintaining illumination coherence is considered a challenging task, because light transport information is needed to reproduce global illumination effects—color bleeding, soft shadows, caustics, and so on. The pipeline of the proposed compositor incorporates shape reconstruction, as well as global illumination simulation and relighting; thus, the compositor can reproduce background-to-foreground color bleeding and soft shadows. It is empirically evaluated that the proposed compositor compares favorably with rendering both in terms of quality and speed. Because it is in principle impossible to render the foreground and background elements together, the proposed compositor is inevitably selected to generate images with the two types of visual coherence. This leads to efficient construction of photorealistic cyberworlds.

Worldwide, 79.6 million people experience glaucoma, which can cause visual field loss to the individuals. Visual field examination plays an important role in the detection of glaucoma. However, current visual field examination approaches have disadvantages, such as requirements for expensive equipment, long testing time, location restrictions, and more. In the present study, we propose an assessment based on saccadic reaction time (SRT) to overcome the issues in the existing approaches. To confirm the effectiveness of our method, we simulated different stages of glaucoma using a visual field defect simulation system. The result of the visual experiment showed that the discrimination method using SRT can distinguish different symptoms with less testing time.

Homonymous hemianopia (HH) is one kind of visual field defect that the right or left half of the visual fields of both eyes are missing. HH is caused by damage to the neural pathways of the brain and a complete recovery is usually difficult. This study proposes a new information compensation method for HH patients using optical-see-through head mounted display. To avoid causing the occlusion, the proposed technique uses indicators placed at the boundary between the lost and remaining sides of visual field to notify patients about the changes in the lost side. Experiment involving simulated HH participants was conducted to verify the effectiveness of the proposed method and compare with the existing method using overlaid overview window. Objective and subjective evaluation results show that the proposed method can partially alleviate the occlusion problem of overlaid overview window approach and has lower physical and mental demand.

In role-playing games, creators always use specific models to generate non-player characters (NPCs). The prefabricated models are materialized in the scenes, and if more NPCs are included than character models prepared, the players may easily find many NPCs with similar appearances, which makes the scenes unnatural. In this paper, we propose a novel system for generating a rich variety of 3D anime-like NPCs in real time to make the scenes look more natural. We combine a proprietary character customization system with machine learning, where the customizing parameters are treated as feature vectors input in the neural network. The parameters are trained to avoid generating bad-looking models. We demonstrate that the proposed system can generate a natural school classroom scene with a variety of good-looking female student NPCs in a uniform.

Visual field defects (VFD) can be caused by a variety of conditions. Checking and tracking the progression of VFD is an important part of an eye assessment. Although the use of standard automatic perimetry (SAP) is very popular for VFD diagnosis, it limits the population because of its high requirement for patients. We used a video-watching task as a replacement modality, which precludes the long period of fixation and uses the on-screen gaze to replace the button response. We developed a simulation system to mimic the different types of VFD in people with a normal pattern.We hypothesize that patients with VFD need more eye movement to compensate for the unseen area. We proposed a metric that indicates the gross eye movements toward a specific direction and found a significant difference between the VFD and normal pattern. Furthermore, we found videos that show the unique eye movement pattern in different eye conditions.

Solving complex data analysis problems can often benefit a collaborative effort. For synchronous co-located collaboration, a well-recognized challenge is to deliver different contents to people with different privileges and different responsibilities. This challenge is becoming more obvious with the use of a shared display space such as a wall-size display. In particular, scenarios often arise that a privileged participant needs to access sensitive information that other participants are not permitted to view. This is nearly impossible to achieve with only a single display. As a result, it becomes clear that additional devices are needed to provide some of the participants the capability to access and manage certain information in a private space. In this work, we investigate incorporating optical see-through head-mounted displays (OST-HMDs) with a wall-size display to deliver sensitive information in a synchronous co-located, collaborative setting. With our prototype system, we conduct a user study to observe the collaboration styles under this unique setup. We also present the lessons learned by reflecting on the iterative design process of our prototype system.

The number of individuals with Age-related Macular Degeneration (AMD) is rapidly increasing. One of the main symptoms of AMD is "metamorphopsia," or distorted vision, which not only makes it difficult for individuals with AMD to do detailed-oriented tasks but also makes sufferers more vulnerable to certain risks in day-to-day life. Traditional clinical approaches to assess metamorphopsia have lacked mechanisms for quantifying the degree of distortion in space, making it impossible to know exactly how individuals with the condition see things. This paper proposes a new method for quantifying distortion in space and visualizing AMD patients' distorted views via line manipulation. By visualizing the distorted views stemming from metamorphopsia, the method gives doctors and others an intuitive picture of how patients see the world and thereby enables a broad range of options for treatment and support.

Rust on the surface of metal is one of the most common examples of deterioration with various appearances. Because metal is usually coated with rust-preventive paint, rust may mostly appear after the coat is peeled off due to various factors. In this poster paper, we propose a visual simulation method for peeling off coating on the surface of metal. We allow for the mechanical behavior of the deteriorated coating to express realistically mottled patterns and paint bulges.

Compositing two or more digital images without causing viewer discomfort is a difficult task. In this study, we focus on the emerging technology named "Deep Compositing," to consider a method for realizing consistent global illumination effects, such as color bleeding, soft shadows, and caustics within such a framework. In this poster paper, we regard the first onset of each deep pixel sample point as a virtual point light and attempt to realize pseudo color bleeding.

Visual field defect (VFD) refers to a symptom in which a patient loses part of his/her field of view (FoV). Medical therapy can halt the progression of VFD, but complete recovery is impossible. In this paper, we propose a computational method for alleviating the restricted FoV with an optical see-through head-mounted display (OST-HMD), where an overview scene captured by the installed camera is overlaid on the persisting FoV. Since the overview window occludes with the real world scene, there is a trade-off between the augmented contextual information and the local unscreened information. We hypothesized that such a trade-off can be resolved by taking into consideration the size of the overview window and its displacement from the center of the unimpaired FoV. We therefore conducted an empirical evaluation through a Whac-A-Mole type of task with ten VFD-imitative subjects, where three sizes of an overview window with a fixed aspect ratio and seven positions in terms of elevation and azimuth were used combinatorially on an OST-HMD to find the best size and position of the overview window. It was statistically proven that for left-sided homonymous VFD-imitative subjects, the performance of the task was better when the medium-sized overview window was placed in the lower right position. The obtained result can legitimate default settings for the proposed VFD alleviation method.

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.

This January 2019 issue of theIEEE Transactions on Visualization and Computer Graphics (TVCG)contains the proceedings of IEEE VIS 2018, held during 21-26 October 2018 at the Estrel Hotel & Congress Center in Berlin. With IEEE VIS 2018, the conference series is in its 29th year.IEEE VIS consists of three conferences, held concurrently: the IEEE Visual Analytics Science and Technology Conference (IEEE VAST), the IEEE Information Visualization Conference (IEEE InfoVis), and the IEEE Scientific Visualization Conference (IEEE SciVis). These three conferences are the premier venues for the visualization community to exchange the latest ideas and developments, attracting researchers and practitioners alike.

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.

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.

The term figurative is used to refer to any form of mathematical visualization that retains strong references to the geometry found in the real world. This article explains the figuration process for some basic mathematical functions definable in the n-dimensional complex projective space. In the latter part of this paper, we raise the question that has been neglected thus far: What does the Riemann sphere's axis stand for? We show that the answer can be obtained only by observing from the inside the sphere by setting the viewpoint of the immersive environment to the origin, which is always undefined in projective geometry. For another example, we can draw some basic math functions that we are familiar with on the projective plane, and observe the invariance that exists among the functions that were thought to be different from one another.

Various attempts at exploiting information visualization for sports have recently been reported in the literature, although it is still challenging to analyze continuous ball matches. In this paper, we propose a novel visual analytics system, called TideGrapher, to track the transition of tactile situations in a rugby match. With a particular focus on the side position of the ball, we designed a dedicated spatial substrate based on the spatio-temporal trajectory of the ball and provided a set of basic interactions. Quantitative analysis was strengthened by adding a new index, called initiative, to commonly used possession (ball occupation) and territory (dominance of territory). The feasibility of the proposed visual analytics system was proven empirically through application to datasets from real amateur and professional matches.

Datasets obtained through recently advanced measurement techniques tend to possess a large number of dimensions. This leads to explosively increasing computation costs for analyzing such datasets, thus making formulation and verification of scientific hypotheses very difficult. Therefore, an efficient approach to identifying feature subspaces of target datasets, that is, the subspaces of dimension variables or subsets of the data samples, is required to describe the essence hidden in the original dataset. This paper proposes a visual data mining framework for supporting semiautomatic data analysis that builds upon asymmetric biclustering to explore highly correlated feature subspaces. For this purpose, a variant of parallel coordinate plots, many-to-many parallel coordinate plots, is extended to visually assist appropriate selections of feature subspaces as well as to avoid intrinsic visual clutter. In this framework, biclustering is applied to dimension variables and data samples of the dataset simultaneously and asymmetrically. A set of variable axes are projected to a single composite axis while data samples between two consecutive variable axes are bundled using polygonal strips. This makes the visualization method scalable and enables it to play a key role in the framework. The effectiveness of the proposed framework has been empirically proven, and it is remarkably useful for many-to-many parallel coordinate plots.

Astronomers have been observing blazars to solve the mystery of the relativistic jet. A technique called TimeTubes uses a 3D volumetric tube to visualize the time-dependent multivariate observed datasets and allows astronomers to interactively analyze the dynamic behavior of and relationship among those variables. However, the observed datasets themselves exhibit uncertainty due to their errors and missing periods, whereas periods interpolated by TimeTubes result in a different type of uncertainty. In this paper, we present a technique for ameliorating such data- and mapping-inherent uncertainties: visual fusion of datasets for the same blazar from two different observatories. Visual data fusion with Time-Tubes enables astronomers to validate the datasets in a meticulous manner.

Groove, the sense of rhythmic feel or musical swing, is one of the most essential factors of a good musical performance. Groove visualization can help players/listeners acquire groove sensation and share impressive expressions of music, regardless of personal hearing ability or musical sense. In this paper, we present SeeGroove2 as an extension of our previous system SeeGroove, by featuring a new groove visualization scheme relying on an orbit metaphor. Rhythm patterns are visually interpreted as various orbital shapes by plotting each played note as an orbiting circle and smoothly interpolating them. Thanks to the adoption of a module-oriented and multi-threaded architecture, the system can change the orbit shapes interactively as the music performance progresses, and thereby the user can feel groove transitions in the music on the fly.

When a musical instrument player performs music, the accompanying visual information can have a significant effect on the performance. For example, several players in a jam session may change their style of playing immediately by closely examining the co-players' expressions and behaviors and predicting their emotion and intention on the fly. In this paper, we propose a system that generates videos in response to the impromptu performance of a single musical instrumental player. The system evaluates the input signals in an affective way and generates a corresponding video based on the results of the evaluation. The player tends to change his/her performance while being inspired by the generated video and giving further triggers for the system to modify the video. The system aims to establish such an affective loop, where it is expected to act as a “co-player” who continues having an influence on the performance of the real player. The final goal of this study is to improve the quality of the player's performing experience through such interactions between the player and the system. By conducting a user evaluation, it was proven that we were able to inspire an amateur guitarist as the subject through the affective video generation and provide a cyberworld where he is allowed to experience a better performance than playing alone.

Exploring feature subspaces is one of promising approaches to analyzing and understanding the important patterns in multivariate data. If relying too much on effective enhancements in manual interventions, the associated results depend heavily on the knowledge and skills of users performing the data analysis. This paper presents a novel approach to extracting feature subspaces from multivariate data by incorporating biclustering techniques. The approach has been maximally automated in the sense that highly-correlated dimensions are automatically grouped to form subspaces, which effectively supports further exploration of them. A key idea behind our approach lies in a new mathematical formulation of asymmetric biclustering, by combining spherical k-means clustering for grouping highly-correlated dimensions, together with ordinary k-means clustering for identifying subsets of data samples. Lower-dimensional representations of data in feature subspaces are successfully visualized by parallel coordinate plot, where we project the data samples of correlated dimensions to one composite axis through dimensionality reduction schemes. Several experimental results of our data analysis together with discussions will be provided to assess the capability of our approach.

"Groove" is a global feature of music performance that generalizes the surges, propulsive rhythmic feel, dynamics, and togetherness of sound to reflect overall rhythmic pleasure. Groove is vital to a good performance, though it is claimed that consierable experience is needed to be accumulated for the expression of groove at will. In this work, we propose a general visualization system, called See Groove, that allows performers to efficiently comprehend groove in various music genres. For each genre, the standard MIDI dataset was extracted from actual performances of professional players in advance. The system then takes the difference between an input data of performance and the standard of a chosen genre to quantify the degree of groove of the performance. To visualize the differences, the user is allowed to rely on any combination of two modes and two views of designated visualization in accordance with his/her preferences and target information.

Abstract We propose a stable and efficient particle‐based method for simulating highly viscous fluids that can generate coiling and buckling phenomena and handle variable viscosity. In contrast to previous methods that use explicit integration, our method uses an implicit formulation to improve the robustness of viscosity integration, therefore enabling use of larger time steps and higher viscosities. We use Smoothed Particle Hydrodynamics to solve the full form of viscosity, constructing a sparse linear system with a symmetric positive definite matrix, while exploiting the variational principle that automatically enforces the boundary condition on free surfaces. We also propose a new method for extracting coefficients of the matrix contributed by second‐ring neighbor particles to efficiently solve the linear system using a conjugate gradient solver. Several examples demonstrate the robustness and efficiency of our implicit formulation over previous methods and illustrate the versatility of our method.

Parallel coordinates is well-known as a popular tool for visualizing the underlying relationships among variables in high-dimension datasets. However, this representation still suffers from visual clutter arising from intersections among poly line plots especially when the number of data samples and their associated dimension become high. This paper presents a method of alleviating such visual clutter by contracting multiple axes through the analysis of correlation between every pair of variables. In this method, we first construct a graph by connecting axis nodes with an edge weighted by data correlation between the corresponding pair of dimensions, and then reorder the multiple axes by projecting the nodes onto the primary axis obtained through the spectral graph analysis. This allows us to compose a dendrogram tree by recursively merging a pair of the closest axes one by one. Our visualization platform helps the visual interpretation of such axis contraction by plotting the principal component of each data sample along the composite axis. Smooth animation of the associated axis contraction and expansion has also been implemented to enhance the visual readability of behavior inherent in the given high-dimensional datasets.

Digital signage has been getting more popular due to the recent development of underlying hardware technology and improvement in installing environments. In order to deliver a message from a sender effectively, it is necessary to check up viewers' evaluations towards the content actually displayed by the digital signage and utilize the evaluations to make it more adapted to the local viewers' tastes. However, previous methods for the survey such as interviewing require a lot of human resources, and thus there have been some research attempts to gather viewers' evaluations towards digital signage automatically. For example, some digital signage systems utilize a fixed video camera for the evaluation. They recognize viewers' faces, and understand their demographics segmentation, such as gender and age, though these values are not related to their actual feelings towards digital signage.

We have developed a hybrid wind tunnel, where 2D measurement-integrated (MI) simulation, which utilizes actual data acquired from real air flow behind a square cylinder, plays an important role in improving the accuracy of the numerical analysis. The wind tunnel requires an accompanying visual analysis tool with which we can effectively peer into the relationships between the actual and simulated flow fields. In this paper, we attempt to exploit an augmented reality display to that end. The basic idea is to superimpose the computationally-visualized MI simulated pressure field onto the actual flow velocity structure physically-visualized with oil misttraced streaklines instantaneously. Spatial registration of these two visual sources is rather straight-forward since the fixed cylinder of the wind tunnel is easily identified, whereas visualizing the MI simulated pressure field can be characterized with a sophisticated scheme based on differential topology. Considering the fact that vortex centers are located at local minima in the pressure field, and each minimum is surrounded by a derived topological feature called ridge cycle, we can colorize the field adaptively and keep track of Karman vortex streets robustly, regardless of drastic change in the Reynolds number of the flow field.

The most prevalent approach to simulating viscous fluids is based on the Navier-Stokes equations, and has extensively been adopted in computer graphics for the past two decades. When employing an explicit viscosity integration, however, time step size for numerically stable simulation is likely to be limited and necessitate an exceedingly long period of time for computation. In this paper, we present a novel particle-based method for efficiently simulating viscous fluids using position-based constraints. Our method utilizes the geometric configuration of particles for the positional constraints in the position-based dynamics, and thus can generate visually-plausible behavior of viscous fluids, while allowing for the use of much larger time steps than the ones previously adopted in the viscous fluid simulations. An associated boundary handling scheme for the position-based fluids is also proposed to properly address constraints for density and viscosity distributions on boundaries. In addition, by adjusting parameters of particles, our method can produce complicated dynamics of threads, sheets, and volumes of different viscosity values in a unified framework. Several examples demonstrate the efficiency as well as robustness and versatility of our approach.

Digital signage has been getting more popularity due to the recent development of underlying hardware technology and improvement in installing environments. Any signage is required to make its content more attractive to viewers by evaluating the current attractiveness on the fly, in order to deliver the message from the sender more effectively. However, most previous methods for signage require time to reflect the viewers' evaluations. In this paper, we present a novel, viewer-adaptive digital signage displaying academic conference posters, which automatically learns what structure and content are being attractive through the viewers' involuntary behaviors, and makes them more adapted to the viewers. This system takes away the current gap between viewers' evaluations and the content actually displayed on digital signage, and makes efficient mutual transmission of information between the cyber world and the reality.

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.

Parallel performance has been a challenging topic in streamline visualization for large unstructured flow datasets on parallel distributed-memory computers. It depends strongly on domain partitions. Unsuitable partitions often lead to severe load imbalance and high frequent communications among the domain partitions. To address the problem, we present an approach to flow data partitioning taking account of flow directions and features. Multilevel spectral graph bisection method is employed to reduce communication and synchronization overhead among distributed domains. Edge weights in the corresponding adjacent matrix is defined based on an anisotropic local diffusion operator which assigns strong coupling along flow direction and weak coupling orthogonal to flow. Meanwhile, the distributions of seed points and flow features such as vortex structure are also considered in partitioning so as to obtain good load balance. The experimental results are given to show the feasibility and effectiveness of our method.

In this paper, we describe the development of automatic drafting functions for men's and women's pants, which have three new features for educational purposes: (1) step mode for explaining the drafting order; (2) guidance for displaying drafting details and usage procedures; and (3) mechanism for displaying class references. The slopers are well-adapted to many different sizes since their dimensions are determined with reference to 1D and 3D body measurements of 28 third-year university students. We have made experimental use of the slopers for beginner level students (straight pants), intermediate level students (wide pants) and advanced level students (originally-designed pants). The feasibility and effectiveness of these newly developed automatic drafting functions have been proven on the basis of drafting results and evaluations submitted by the beginner level and intermediate level students, and drafting results, fitting and corrections by the advanced level students.

Optimal viewpoint selection is an important task because it considerably influences the amount of information contained in the 2D projected images of 3D objects, and thus dominates their first impressions from a psychological point of view. Although several methods have been proposed that calculate the optimal positions of viewpoints especially for 3D surface meshes, none has been done for solid objects such as volumes. This paper presents a new method of locating such optimal viewpoints when visualizing volumes using direct volume rendering. The major idea behind our method is to decompose an entire volume into a set of feature components, and then find a globally optimal viewpoint by finding a compromise between locally optimal viewpoints for the components. As the feature components, the method employs interval volumes and their combinations that characterize the topological transitions of isosurfaces according to the scalar field. Furthermore, opacity transfer functions are also utilized to assign different weights to the decomposed components so that users can emphasize features of specific interest in the volumes. Several examples of volume datasets together with their optimal positions of viewpoints are exhibited in order to demonstrate that the method can effectively guide naive users to find optimal projections of volumes.

Topological volume skeletons represent level-set graphs of 3D scalar fields, and have recently become crucial to visualizing the global isosurface transitions in the volume. However, it is still a time-consuming task to extract them, especially when input volumes are large-scale data and/or prone to small-amplitude noise. The paper presents an efficient method for accelerating the computation of such skeletons using adaptive tetrahedralization. The tetrahedralization is a top-down approach to linear interpolation of the scalar fields in that it selects tetrahedra to be subdivided adaptively using several criteria. As the criteria, the method employs a topological criterion as well as a geometric one in order to pursue all the topological isosurface transitions that may contribute to the global skeleton of the volume. The tetrahedralization also allows us to avoid unnecessary tracking of minor degenerate features that hide the global skeleton. Experimental results are included to demonstrate that the present method smoothes out the original scalar fields effectively without missing any significant topological features.

Direct volume rendering is a standard technique for projecting all the optically-encoded samples onto the screen at once to allow us to peer into the inner structures involved in a volume data. Datacentric approaches to the design of transfer functions (TFs) have recently been well-established, which perform mathematical analysis of the data prior to pertinent rendering. The advent of multidimensional TFs is one of the latest major achievements in the volume visualization research. As opposed to the traditional onedimensional TFs that only consider a voxel’s scalar field value, the multi-dimensional TFs assign auxiliary attributes to the voxels to construct their sophisticated parametric domains. For example, when visualizing volumes obtained by scientific simulations, the observers can utilize their own knowledge about the simulation settings to extract the global characteristics of the volumes and to locate regions of particular interest. If they are allowed to design multi-dimensional TFs using staff attributes so as to encapsulate such advance knowledge, they can readily yield visualization results to fulfill their purposes. Nevertheless, nearly all attributes for the conventional multi-dimensional TFs are based on local features, such as differentials and curvatures, and are difficult to capture the global structure of the volume contrary to the observer’s purposes. This paper therefore introduces a new set of topological attributes to establish a new framework that is intended to realize objective-based assistance. Topological attributes proposed herein are derived from the level-set graph, which delineates the topological evolution of an isosurface with respect to the scalar field.

No abstract available.

This paper describes performance visualization with its purposes, significance, and our prototype system. Performance visualization provides us with a new user interface for music systems, a non-subjective explanation and judgment for musical expression, and a visual aid to understand, analyze, and compare performances and their musical structures. It will also introduce a more challenging research on designing a musical data mining interface not by tags nor by contents but by the mood. Other challenges include finding qualitative characteristics of music with analysis methods for information visualization and synesthesia between visualized and sonified information. We will describe which information should be shown on the visualized figures for expressive performance. Then we will show our prototype system to visualize expressive performance consisting of several instruments.

Line Integral Convolution (LIC) is a promising method for visualizing 2D dense flow fields. Direct extensions of the LIC method to 3D have not been considered very effective, because optical integration in viewing directions tends to spoil the coherent structures along 3D local streamlines. In our previous reports, we have proposed a selective approach to volume rendering of LIC solid texture using 3D significance map (S-map), derived from the characteristics of flow structures, and a specific illumination model for 3D streamlines. In this paper, we take full advantage of scalar volume rendering hardware, such as VolumePro, to realize a realtime 3D flow field visualization environment with the LIC volume rendering method.

It is still challenging to generate hand-drawn pictures because they differ from ordinary photographs in that they are often drawn as seen from multiple viewpoints. This paper presents a new approach for modeling such surperspective projection based on shape deformation techniques. Specifically, surperspective landscape images for guide-maps are generated from 3D geographical elevation data. Our method first partitions a target geographical surface into feature areas to provide designers with landmarks suitable for editing. The system takes as input 2D visual effects, which are converted to 3D geometric constraints for geographical surface deformation. Using ordinary perspective projection, the deformed shape is then transformed into a target guide-map image where each landmark enjoys its own vista points. An algorithm for calculating such 2D visual effects semi-automatically from the geographical shape features is also considered.

Abstract An initial overview of parallel visualization in the GeoFEM software system is provided. Our visualization subsystem offers many kinds of parallel visualization methods for the users to visualize their huge finite‐element analysis datasets for scalar, vector and/or tensor fields at a reasonable cost. A polygonal simplification scheme is developed to make the transmission and rendition of output graphic primitives more efficient. A salient feature of the subsystem lies in its capability in the automatic setting of visualization parameter values based on the analysis of scalar/flow field topology and volumetric coherence, to improve the quality of visualization results with a minimized number of batch re‐executions. Representative experimental results illustrate the effectiveness of our subsystem. Copyright © 2002 John Wiley & Sons, Ltd.

Not only photorealistic rendering but non-photorealistic rendering is considered an important research topic in artistic image synthesis. However, little attention has been given to colored pencil drawing. This paper proposes a volume graphics model for colored pencil drawing. The model consists of three sub-models, which describe in a volumetric fashion, the microstructure of paper, pigment distribution on paper, and pigment redistribution, respectively. The model takes advantage of volumetric offset distance accessibility and line integral convolution, and thus is highly controllable with a small number of parameters. A couple of synthesized color pencil drawing data sets are rendered using an existing volume visualizer to empirically prove that the model is descriptive enough to produce realistic images of colored pencil drawing.

This paper describes initial results of a 3D field topology analysis for automating transfer function design aiming at comprehensible volume rendering. The conventional Reeb graph-based approach to describing topological features of 3D surfaces is extended to capture the topological skeleton of a volumetric field. Based on the analysis result, which is represented in the form of a hyper Reeb graph, a procedure is proposed for designing appropriate color/opacity transfer functions. Two analytic volume datasets are used to preliminarily prove the feasibility of the present design methodology.

Modular visualization environments (MVEs) have recently been regarded as the de facto standard for scientific data visualization, mainly due to adoption of the visual programming style, reusability, and extendability. However, since scientists and engineers as the MVE principal user are not always familiar with how to map numerical data to proper graphical primitives, the set of built-in modules is not fully used to construct necessary application networks. Therefore, a certain mechanism needs to be incorporated into MVEs, which makes use of heuristics and expertise of visualization specialists (visineers), and which supports the user in designing his/her applications with MVEs. The Wehrend's goal-oriented taxonomy of visualization techniques is adopted as the basic philosophy to develop a system, called GADGET, for application design guidance for MVEs. The GADGET system interactively helps the user design appropriate applications according to the specific visualization goals, temporal efficiency versus accuracy requirements, and such properties as dimension and mesh type of a given target dataset. Also the GADGET system is capable of assisting the user in customizing a prototype modular network for his/her desired applications by showing execution examples involving datasets of the same type. The paper provides an overview of the GADGET guidance mechanism and system architecture, with an emphasis on its knowledge base design. Sample data visualization problems are used to demonstrate the usefulness of the GADGET system.

A new type of geometric model called Interval volume for volumetric data exploration is presented. An interval volume represents a three dimensional subvolume for which the associate scalar values lie within a user specified interval, and provides one of the promising approaches to solid fitting, which is an extended concept of traditional surface fitting. A well known isosurfacing algorithm called Marching Cubes is extended to obtain a solid fitting algorithm, which extracts from a given volumetric data set a high resolution, polyhedral solid data structure of an interval volume. Branch-on-Need Octree is used as an auxiliary data structure to accelerate the extraction process. A variety of interval volume rendering methods and principal related operations, including measurements and focusing, are also presented. The effectiveness of measurement coupled visualization capabilities of the presented approach is demonstrated by application to visualizing a four dimensional simulated data from atomic collision research.

Proposes as a generalization of isosurfaces, the 'interval volume', which is a new type of geometric model representing 3D subvolumes with field values belonging to a closed interval. A dominant surface fitting algorithm called 'marching cubes' is extended to obtain a solid fitting algorithm, which extracts from a given volumetric dataset a high-resolution polyhedral solid data structure of the interval volume. Rendering methods for the interval volume and principal related operations are also presented. The effectiveness of this approach is illustrated with 4D simulated data from atomic collision research.

Abstract A new translucent display algorithm is proposed for grey‐scale images represented by G‐octrees. The algorithm generates a special triangle quadtree as the display image for the isometric projection of a G‐octree, while traversing the G‐octree from front to back, with respect to the viewpoint. A method of using the translucent effect to analyse the interiors of three‐dimensional objects is also described, together with its implementation results.