Journal of Visual Languages and Computing最新文献

This paper proposes a novel calculation method of personality based on Chinese physiognomy. The proposed solution combines ancient and modern physiognomy to understand the relationship between personality and facial features and to model a baseline to shape facial features. We compute a histogram of image by searching for threshold values to create a binary image in an adaptive way. The two-pass connected component method indicates the feature's region. We encode the binary image to remove the noise point, so that the new connected image can provide a better result. According to our analysis of contours, we can locate facial features and classify them by means of a calculation method. The number of clusters is decided by a model and the facial feature contours are classified by using the k-means method. The validity of our method was tested on a face database and demonstrated by a comparative experiment.

Maps have traditionally been used for displaying geographical information. However, apart from this obvious purpose, the metaphor of maps has been applied to other uses, such as information visualisation and novel user interfaces, since the map metaphor is easy-to-understand and allows users to explore data intuitively. There are several methods for creating these map-like visualisations and user interfaces, but there is little understanding on how people perceive these non-geographical maps, and how to make the visualisation output more realistic. As such, we aim to find preliminary answers on these issues by conducting user studies with a series of map-like visualisations. In this paper, we report on the results of the studies and reveal the factors that have an impact on the human perception of visualisations that are designed to resemble geographic maps. Based on these results, we propose design suggestions for building realistic map-like visualisations.

Temporal (Dynamic) multivariate networks consist of objects and relationships with a variety of attributes, and the networks change over time. Exploring such kind of networks in visualization is of great significance and full of challenges as its time-varying and multivariate nature. Most of the existing dynamic network visualization techniques focus on the topological structure evolution lacking of exploration on the multivariate data (multiple attributes) thoroughly, and do not cover comprehensive analyses on multiple granularities. In this paper, we propose TMNVis, an interactive visualization system to explore the evolution of temporal multivariate network. Firstly we list a series of tasks on three granularities: global level, subgroup level and individual level. Secondly three main views, which rely mainly on timeline-based method while animation subsidiary, are designed to resolve the analysis tasks. Thirdly we design a series of flexible interactions and develop a prototype system. At last we verify the effectiveness and usefulness of TMNVis using a real-world academic collaboration data.

Directed graphs are used to represent a variety of datasets, including friendship on social networking services (SNS), pathways of genes, and citations of research papers. Graph drawing is useful in representing such datasets. At the international conference on Information Visualization (IV), we have presented a convergent edge drawing and a node layout technique for tightly and mutually connected directed graphs. The edge drawing technique in the IV paper includes three features: ordinary bundling of edges connecting pairs of node clusters, convergence of multiple bundles that connect to the same node cluster, and shape adjustment of two bundles connecting the same pair of node clusters. In this paper, we present improved node layout and edge drawing techniques, which make our edge bundling more effective. This paper also introduces a case study with a directed paper citation graph dataset.vvvvv

With the development of human motion capture, realistic human motion capture data has been widely implemented to many fields. However, segmenting motion capture data sequences manually into distinct behavior is time-consuming and laborious. In this paper, we introduce an efficient unsupervised method based on graph partition for automatically segmenting motion capture data. For N-Frame motion capture data sequence, we construct an undirected, weighted graph $G=G(V,E),$ where the node set V represent frames of motion sequence and the weight of the edge set E describes similarity between frames. In this way, behavioral segmentation problem can be transformed into graph cut problem. However, traditional graph cut problem is NP hard. By analyzing the relationship between graph cut and spectral clustering, we apply spectral clustering to the NP hard problem of graph cut. In this paper, two methods of spectral clustering, t-nearest neighbors and the Nystrom method, are employed to cluster motion capture data for getting behavioral segmentation. In addition, we define an energy function to refine the results of behavioral segmentation. Extensive experiments are conducted on the dataset of multi-behavior motion capture data from CMU database. The experimental results prove that our novel method is robust and effective.

People flow information brings us useful knowledge in various industrial and social fields including traffic, disaster prevention, and marketing. However, it is still an open problem to develop effective people flow analysis techniques. We considered compression and data mining techniques are especially important for analysis and visualization of large-scale people flow datasets. This paper presents a visualization method for large-scale people flow dataset featuring compression and data mining techniques. This method firstly compresses the people flow datasets using UniversalSAX, an extended method of SAX (Symbolic Aggregate Approximation). Next, we apply algorithms inspired by natural language processing to extract movement patterns and classify walking routes. After this process, users can interactively observe trajectories and extracted features such as congestions and popular walking routes using a visualization tool. We had experiments of classifying and visualizing walking routes using two types of people flow dataset recorded at an exhibition and a corridor applying our method. The results allow us to discover characteristic movements such as stopping in front of particular exhibits, or persons who passed same places but walked at different speeds.

In 3D visualization of terrain, occlusion has detrimental impact on discovering, accessing, and spatially relating embedded data. This can lead to significant loss of information. To address this issue, we introduce visibility widgets: compact auxiliary views that visualize occluded data, their locations, and related properties, and thus support users in revealing hidden information. The widgets comprise different occlusion management strategies for detecting and analyzing invisible data inside as well as outside of the field of view. In addition, each widget is designed complementary and transient to facilitate interactive exploration and navigation while not monopolizing the user’s attention. We demonstrate the application of visibility widgets focusing the visual exploration of weather data in 3D terrain.

Mobile agents environment is a new application paradigm with unique features such as mobility and autonomy. Traditional deadlock detection algorithms in distributed computing systems do not work well in mobile agent systems due to the unique feature property of the mobile agent. Existing deadlock detection and resolution algorithms in mobile agent systems have limitations such as performance inefficiency and duplicate detection/resolution when multiple mobile agents simultaneously detect/resolve the same deadlock. To address these problems, we propose an improved deadlock detection and resolution algorithm that adopts priority-based technique and lazy reaction strategy. The priority-based technique aims to ensure that there is only one instance of deadlock detection and resolution, and it also helps reduce mobile agent movement and data traffic together with the lazy reaction strategy. The liveness and safety properties of the proposed algorithm are proved in this paper. Theoretical analysis and experimental results show that the proposed algorithm provides better performance in terms of agent movement, data traffic, and execution time.

ImAtHome is an iOS application for smart home configuration and management built over Apple HomeKit, a framework for communicating with and controlling home automation accessories. This paper describes the design and development of the visual interaction language made available in ImAtHome for empowering end users, without programming skills, to create event-condition-action rules that control home behavior. It can be regarded as an alternative approach to traditional trigger-action programming interfaces, where the user must define such rules by means of “if-then” constructs. Last but not least, attention has been put to make the interaction style as much coherent as possible with other iOS applications. The paper finally presents a user experiment, carried out with 30 participants according to a between-subject protocol, to evaluate the usability of ImAtHome and compare it with the official app for home automation recently released by Apple.

Dynamic programming is a popular optimization technique, developed in the 60’s and still widely used today in several fields for its ability to find global optimum. Dynamic Programming Algorithms (DPAs) can be developed in many dimension. However, it is known that if the DPA dimension is greater or equal to two, the algorithm is an NP complete problem. In this paper we present an approximation of the fully two-dimensional DPA (2D-DPA) with polynomial complexity. Then, we describe an implementation of the algorithm on a recent parallel device based on CUDA architecture. We show that our parallel implementation presents a speed-up of about 25 with respect to a sequential implementation on an Intel I7 CPU. In particular, our system allows a speed of about ten 2D-DPA executions per second for 85 × 85 pixels images. Experiments and case studies support our thesis.