Journal of Visual Languages and Computing最新文献

In evaluating aesthetics of graph drawings, recent works have provided participants with an adjacency list of a graph and asked them to sketch a pleasing layout of the graph. However, this line of research has not considered aesthetics for user-sketched layouts of graphs with symmetries, which are an important property of a given graph drawing. Therefore, the work reported in this paper focuses on empirically evaluating aesthetics for user-sketched layouts of symmetric graphs. We provided participants adjacency lists of two symmetric graphs and asked them to sketch the two graphs. We analyzed whether symmetries were shown in user-sketched drawings, and further analyzed the aesthetics based on their drawing processes and drawing preferences. In addition, our experiment includes multiple stages to analyze whether the participants had the potential learning ability to draw symmetric structures of graphs. We found that in drawing symmetrical graphs, showing symmetry was likely considered more important than minimizing edge crossings, and that most participants made substantial progress in their ability to create symmetric drawings after going through the experimental stages.

Underground flow path (UFP) plays a significant role in illustrating seismic data and revealing stratigraphic structures presented in the data. It is challenging to interactively illustrate the UFP due to the well-known characteristics of seismic data, i.e., noisy, discontinuous, and low resolution. In this paper, we propose a novel interactive visualization approach to illustrate the seismic data, which employs a bit-array based 3D texture to organize different types of interactions. There are three major merits of the organization scheme: It enables to switch different types of interactions flexibly. It allows to perform progressive seed point tracing to get more accurate UFP structures, and supports different stratigraphic display modes and their flexible switching. The feedback from the domain experts suggests that the proposed approach is capable of better revealing the UFP structure and distribution compared with the existing UFP visualization approaches in this field.

In this paper, we present a practicable method to estimate individual 3D human model in a low cost multi-view realtime 3D human motion capture system. The key idea is: using human geometric model database and human motion database to establish geometric priors and pose prior model; when given the geometric prior, pose prior and a standard template geometry model, the individual human body model and its embedded skeleton can be estimated from the 3D point cloud captured from multiple depth cameras. Because of the introduction of the global prior model of body pose and shapes into a unified nonlinear optimization problem, the accuracy of geometric model estimation is significantly improved. The experiments on the synthesized data set with noise or without noise and the real data set captured from multiple depth cameras show that the estimation results of our method are more reasonable and accurate than the classical methods, and our method is better noise-immunity. The proposed new individual 3D geometric model estimation method is suitable for online realtime human motion tracking system.

To realistic and efficient capture microscopic features of fluid surface, we proposed a novel method for creating small-scale surface details. In this paper, we introduced a surface tension and adhesion model to simulate surface details, which refined the cohesion term and area minimization term. It modified the calculation of surface tension and adhesion and enlarged the support length for cohesion, which makes the microscopic characteristics of surface details more visible. In addition, we integrated this model with a Divergence-free SPH method which fulfills constant density condition and divergence-free condition simultaneously. The experimental results show that our method can well simulate small-scale details of fluid surface in various scenarios meanwhile improves the computational stability and efficiency.

City is a complex system containing various kinds of functional areas. They are always defined by government planning and refining based on the actual requirements of citizens, which are of significant importance to urban developments, ranging from environmental governance and rail transportation to disease prevention and public security. Taxi is a major means of urban transportation, and the taxi trips record human behaviors and mobility patterns, which offer a valuable opportunity for users to get insights into urban functions. Therefore, we propose a visual analysis system in this paper, for an insightful exploration of urban functions based on spatio-temporal taxi OD trips. First, a matrix is defined to restructure spatio-temporal attributes of taxi OD data, and a Non-negative Matrix Factorization(NMF) is applied to classify and identify urban functional areas. Then, a set of visual encodings are designed to visualize mobility patterns of urban areas with different functions, such as the radial chart, the timeline view and the force-directed view. In addition, the spatio-temporal clustering model and the visual designs are all implemented in a visualization framework, with a set of convenient interactions integrated, enabling users to quickly identify areas of different urban functions and analyze the human mobility patterns across different urban areas. Case studies based on real-world datasets and interviews with domain experts have demonstrated the effectiveness of our system.

When analyzing human behaviors, we need to construct the human behaviors from multiple sources of data, e.g. trajectory data, transaction data, identity data, etc. The problems we’re facing are the data conflicts, different resolution, missing and conflicting data, which together lead to the uncertainty in the spatial temporal data. Such uncertainty in data leads to difficulties and even failure in the visual analytics task for analyzing people behavior, pattern and outliers. However, traditional automatic methods can not solve the problems in such complex scenario, where the uncertain and conflicting patterns are not well-defined. To solve the problems, we proposed a semi-automatic approach, for users to solve the conflicts and identify the uncertainties. To be general, we summarized five types of uncertainties and solutions to conduct the tasks of behavior analysis. Combined with the uncertainty-aware methods, we proposed a visual analytics system to analyze human behaviors, detect patterns and find outliers. Case studies from the IEEE VAST Challenge 2014 dataset confirm the effectiveness of our approach.

Many problems in application domains deal with graphs. Graphs are often visualized into node-link diagrams for better understanding of them. Traditionally, graphs are visualized to meet some pre-defined quality metrics, or aesthetics. It is commonly agreed that graphs that are drawn based on aesthetics are more likely to be not only beautiful but also comprehensible. However, those aesthetics were originally proposed by researchers based on their intuitions from a designer’s perspective. Although some of them have been empirically validated, to produce truly user cognitively friendly visualizations,well-grounded cognitive theories and design guidelines are needed.

In this paper, we attempt to explore a cognitive approach that follows a user-centered process in visualizing graphs. We begin by proposing a graph visualization model, which is further conceptualized into a two-stage assessment cycle. It is argued that to provide effective cognitive support, it is essential to understand the cognitive process of a particular task in association with the attributes of the end user and the visual representation, and that to develop a set of design principles, what cognitive processes or graph reading strategies are involved should be part of evaluation. We materialized the model by reviewing the literature and providing relevant examples in the context of providing cognitive support and understanding cognitive process. We introduce theories from other disciplines such as perceptual and cognitive psychology, summarize methodologies that have been used in prior research, and discuss their implications for producing user-centered visualizations.

Animation is a commonly used technique in information visualization for smooth transitions between different views. When observing animations of moving objects, people often need to track several specific objects while identify the major trend of movement simultaneously. In this paper, we propose a novel focus+context grouping technique to facilitate target tracking and trend identification. It divides objects into several groups based on a comprehensive tree cut algorithm and generates a staggering animation in which groups are animated sequentially. A balance between efficiency and accuracy is achieved for an effective animation planning. To evaluate the effectiveness of the proposed technique, a carefully designed user study is conducted. The results indicate that focus+context grouping is effective for users to track targets without losing context (i.e., major trend of movement). Based on the study, we discuss advantages and limitations of the proposed grouping technique and conclude with design implications.