Visual Informatics最新文献

We propose an approach to underpin interactive visual exploration of large data volumes by training Learned Visualization Index (LVI). Knowing in advance the data, the aggregation functions that are used for visualization, the visual encoding, and available interactive operations for data selection, LVI allows to avoid time-consuming data retrieval and processing of raw data in response to user’s interactions. Instead, LVI directly predicts aggregates of interest for the user’s data selection. We demonstrate the efficiency of the proposed approach in application to two use cases of spatio-temporal data at different scales.

In the immersive flow visualization based on virtual reality, how to meet the needs of complex professional flow visualization analysis by natural human–computer interaction is a pressing problem. In order to achieve the natural and efficient human–computer interaction, we analyze the interaction requirements of flow visualization and study the characteristics of four human–computer interaction channels: hand, head, eye and voice. We give out some multimodal interaction design suggestions and then propose three multimodal interaction methods: head & hand, head & hand & eye and head & hand & eye & voice. The freedom of gestures, the stability of the head, the convenience of eyes and the rapid retrieval of voices are used to improve the accuracy and efficiency of interaction. The interaction load is balanced by multimodal interaction to reduce fatigue. The evaluation shows that our multimodal interaction has higher accuracy, faster time efficiency and much lower fatigue than the traditional joystick interaction.

Song Ci is treasured in traditional Chinese culture, which indicates social and cultural evolution in ancient times. Despite the efforts by historians and litterateurs in investigating the characteristics of Song Ci, it is still unclear how to effectively distribute and promote Song Ci in the public sphere. The complexity and abstraction of Song Ci hamper the general public from closely reading, analyzing, and appreciating these excellent works. By means of a set of visual analysis methods, e.g. the spatio-temporal visualization, we exploit visual storytelling to explicitly present the latent and abstractive features of Song Ci. We apply straightway visual charts and lighten the burden of understanding the stories, in order to achieve an effective public distribution. The effectiveness and aesthetics of our work are demonstrated by a user study of three participants with different backgrounds. The result reveals that our story is effective in the distribution, understanding, and promotion of Song Ci.

Progression of Alzheimer’s disease (AD) bears close proximity with the tissue loss in the medial temporal lobe (MTL) and enlargement of lateral ventricle (LV). The early stage of AD, mild cognitive impairment (MCI), can be traced by diagnosing brain MRI scans with advanced fuzzy c-means clustering algorithm that helps to take an appropriate intervention. In this paper, firstly the sparsity is initiated in clustering method that too rician noise is also incorporated for brain MR scans of AD subject. Secondly, a novel neighbor pixel constrained fuzzy c-means clustering algorithm is designed where topoloty-based selection of parsimonious neighbor pixels is automated. The adaptability in choice of neighbor pixel class outliers more justified object edge boundary which outperforms a dynamic cluster output. The proposed adaptive neighbor constrained deviation sparse variant fuzzy c-means clustering (AN_DsFCM) can withhold imposed sparsity and withstands rician noise at imposed sparse environment. This novel algorithm is applied for MRI of AD subjects and normative data is acquired to analyse clustering accuracy. The data processing pipeline of theoretically plausible proposition is elaborated in detail. The experimental results are compared with state-of-the-art fuzzy clustering methods for test MRI scans. Visual evaluation and statistical measures are studied to meet both image processing and clinical neurophysiology standards. Overall the performance of proposed AN_DsFCM is significantly better than other methods.

Maritime transports play a critical role in international trade and commerce. Massive vessels sailing around the world continuously generate vessel trajectory data that contain rich spatial–temporal patterns of vessel navigations. Analyzing and understanding these patterns are valuable for maritime traffic surveillance and management. As essential techniques in complex data analysis and understanding, visualization and visual analysis have been widely used in vessel trajectory data analysis. This paper presents a literature review on the visualization and visual analysis of vessel trajectory data. First, we introduce commonly used vessel trajectory data sets and summarize main operations in vessel trajectory data preprocessing. Then, we provide a taxonomy of visualization and visual analysis of vessel trajectory data based on existing approaches and introduce representative works in details. Finally, we expound on the prospects of the remaining challenges and directions for future research.

Visual programming tools are widely applied in the game industry to assist game designers in developing game artificial intelligence (game AI) and gameplay. However, testing multiple game engines is a time-consuming operation, which degrades development efficiency. To provide an asynchronous platform for game designers, this paper introduces Asyncflow, an open-source visual programming solution. It consists of a flowchart maker for game logic explanation and a runtime framework integrating an asynchronous mechanism based on an event-driven architecture. Asyncflow supports multiple programming languages and can be easily embedded in various game engines to run flowcharts created by game designers.

Edges crossing and nodes overlapping have a significant effect on the users’ recognition and comprehension of network diagrams. In this study, we propose a visual evaluation method for users’ cognition of network diagrams. First, this method carries out a set of cognitive experiments to collect the user’s cognitive performance that affects the variables, including accuracy and response time. The user’s pupil diameter is measured through an eye tracker to reflect their cognitive load. Second, the significance test points out the visual features as independent variables and then establishes an evaluation regression model. The experimental results show that the number of edges, edge length, node visual interference, and edge occlusion contribute to the evaluation models of response time, and edge occlusion and the number of node connections contribute to the accuracy model. Finally, these evaluation models demonstrate good predictability when assessing users’ cognition of network diagrams and provide practical recommendations for their use.

Authoring graph visualization poses great challenges to developers due to its high requirements on both domain knowledge and development skills. Although existing libraries and tools reduce the difficulty of generating graph visualization, there are still many challenges. We work closely with developers and formulate several design goals, then design and implement G6, a web-based library for graph visualization. It combines template-based configuration for high usability and flexible customization for high expressiveness. To enhance development efficiency, G6 proposes a range of optimizations, including state management and interaction modes. We demonstrate its capabilities through an extensive gallery, a quantitative performance evaluation, and an expert interview. G6 was first released in 2017 and has been iterated for 317 versions. It has served as a web-based library for thousands of applications and received 8312 stars on GitHub.

Image Captioning is a cross-modal task that needs to automatically generate coherent natural sentences to describe the image contents. Due to the large gap between vision and language modalities, most of the existing methods have the problem of inaccurate semantic matching between images and generated captions. To solve the problem, this paper proposes a novel multi-level similarity-guided semantic matching method for image captioning, which can fuse local and global semantic similarities to learn the latent semantic correlation between images and generated captions. Specifically, we extract the semantic units containing fine-grained semantic information of images and generated captions, respectively. Based on the comparison of the semantic units, we design a local semantic similarity evaluation mechanism. Meanwhile, we employ the CIDEr score to characterize the global semantic similarity. The local and global two-level similarities are finally fused using the reinforcement learning theory, to guide the model optimization to obtain better semantic matching. The quantitative and qualitative experiments on large-scale MSCOCO dataset illustrate the superiority of the proposed method, which can achieve fine-grained semantic matching of images and generated captions.

Most of the existing blind super-resolution(SR) methods explicitly estimate the kernel in pixel space, which usually has a large deviation and results in poor SR performance. As a seminal work, DASR learns abstract representations to distinguish various degradations in the feature space, which effectively reduces degradation estimation bias. Therefore, we also employ the feature space to extract degradation representations for an ancient painting. However, most of the blind SR mehods, including DASR, are committed to removing degradations introduced by kernels, downsampling and additive noise. Among them, downsampling degradation is often accompanied by unpleasant artifacts. To address this issue, the paper designs a high-resolution(HR) representation encoder $E_{HR}$ based on contrastive learning to distinguish artifacts introduced by downsampling. Moreover, to optimize the ill-posed nature of blind SR, we propose a contrastive regularization$\left(CR\right)$ to minimize the contrastive loss based on VGG-19. With the help of $CR$, the SR images are pulled closer to the HR images and pushed far away from bicubic LR observations. Benefiting from these improvements, our method consistently achieves higher quantitative performance and better visual quality with more natural textures than state-of-the-art approaches on a specialized painting dataset.