Visual Informatics最新文献

A novel approach to visually represent meteorological data has emerged with the maturation of Immersive Analytics (IA). We have proposed an immersive meteorological virtual sandbox as a solution to the limitations of 2D analysis in expressing and perceiving data. This innovative visual method enables users to interact directly with data through non-contact aerial gestures (NCAG). Referring to the “What you see is what you get” concept in scientific visualization, we proposed a novel approach for the visual exploration of meteorological data that aims to immerse users in the analysis process. We hope this approach can inspire immersive visualization techniques for other types of geographic data as well. Finally, we conducted a user questionnaire to evaluate our system and work. The evaluation results demonstrate that our system effectively reduces cognitive burden, alleviates mental workload, and enhances users’ retention of analysis findings.

Immersive visualization utilizes virtual reality, mixed reality devices, and other interactive devices to create a novel visual environment that integrates multimodal perception and interaction. This technology has been maturing in recent years and has found broad applications in various fields. Based on the latest research advancements in visualization, this paper summarizes the state-of-the-art work in immersive visualization from the perspectives of multimodal perception and interaction in immersive environments, additionally discusses the current hardware foundations of immersive setups. By examining the design patterns and research approaches of previous immersive methods, the paper reveals the design factors for multimodal perception and interaction in current immersive environments. Furthermore, the challenges and development trends of immersive multimodal perception and interaction techniques are discussed, and potential areas of growth in immersive visualization design directions are explored.

How to explore fine-grained but meaningful information from the massive amount of social media data is critical but challenging. To address this challenge, we propose the TopicBubbler, a visual analytics system that supports the cross-level fine-grained exploration of social media data. To achieve the goal of cross-level fine-grained exploration, we propose a new workflow. Under the procedure of the workflow, we construct the fine-grained exploration view through the design of bubble-based word clouds. Each bubble contains two rings that can display information through different levels, and recommends six keywords computed by different algorithms. The view supports users collecting information at different levels and to perform fine-grained selection and exploration across different levels based on keyword recommendations. To enable the users to explore the temporal information and the hierarchical structure, we also construct the Temporal View and Hierarchical View, which satisfy users to view the cross-level dynamic trends and the overview hierarchical structure. In addition, we use the storyline metaphor to enable users to consolidate the fragmented information extracted across levels and topics and ultimately present it as a complete story. Case studies from real-world data confirm the capability of the TopicBubbler from different perspectives, including event mining across levels and topics, and fine-grained mining of specific topics to capture events hidden beneath the surface.

With the support of edge computing, the synergy and collaboration among central cloud, edge cloud, and terminal devices form an integrated computing ecosystem known as the cloud-edge-client architecture. This integration unlocks the value of data and computational power, presenting significant opportunities for large-scale 3D scene modeling and XR presentation. In this paper, we explore the perspectives and highlight new challenges in 3D scene modeling and XR presentation based on point cloud within the cloud-edge-client integrated architecture. We also propose a novel cloud-edge-client integrated technology framework and a demonstration of municipal governance application to address these challenges.

We present a visual analysis environment based on a multi-scale partitioning of a 2d domain into regions bounded by cycles in weighted planar embedded graphs. The work has been inspired by an application in granular materials research, where the question of scale plays a fundamental role in the analysis of material properties. We propose an efficient algorithm to extract the hierarchical cycle structure using persistent homology. The core of the algorithm is a filtration on a dual graph exploiting Alexander’s duality. The resulting partitioning is the basis for the derivation of statistical properties that can be explored in a visual environment. We demonstrate the proposed pipeline on a few synthetic and one real-world dataset.

Node-link visual representation is a widely used tool that allows decision-makers to see details about a network through the appropriate choice of visual metaphor. However, existing visualization methods are not always effective and efficient in representing bivariate graph-based data. This study proposes a novel node-link visual model – visual entropy (Vizent) graph – to effectively represent both primary and secondary values, such as uncertainty, on the edges simultaneously. We performed two user studies to demonstrate the efficiency and effectiveness of our approach in the context of static node-link diagrams. In the first experiment, we evaluated the performance of the Vizent design to determine if it performed equally well or better than existing alternatives in terms of response time and accuracy. Three static visual encodings that use two visual cues were selected from the literature for comparison: Width-Lightness, Saturation-Transparency, and Numerical values. We compared the Vizent design to the selected visual encodings on various graphs ranging in complexity from 5 to 25 edges for three different tasks. The participants achieved higher accuracy of their responses using Vizent and Numerical values; however, both Width-Lightness and Saturation-Transparency did not show equal performance for all tasks. Our results suggest that increasing graph size has no impact on Vizent in terms of response time and accuracy. The performance of the Vizent graph was then compared to the Numerical values visualization. The Wilcoxon signed-rank test revealed that mean response time in seconds was significantly less when the Vizent graphs were presented, while no significant difference in accuracy was found. The results from the experiments are encouraging and we believe justify using the Vizent graph as a good alternative to traditional methods for representing bivariate data in the context of node-link diagrams.

With the intersection and convergence of multiple disciplines and technologies, more and more researchers are actively exploring interdisciplinary cooperation outside their main research fields. Facing a new research field, researchers often hope to quickly learn what is being studied in this field, which research points are receiving high attention, which researchers are studying these research points, and then consider the possibility of collaborating with core researchers on these research points. In addition, students who are preparing for academic further education usually conduct research on mentors and mentors’ research platforms, including academic connections, employment opportunities, etc. In order to satisfy these requirements, we (1) design a research point state map based on a science map to help researchers and students understand the development state of a new research field; (2) design a bar-link author-affiliation information graph to help researchers and students clarify academic networks of scholars and find suitable collaborators or mentors; (3) designs citation pattern histogram to quickly discover research achievements with high research value, such as the Sleeping Beauty papers, recently hot papers, classic papers and so on. Finally, an interactive analytical system named PubExplorer was implemented with IEEE VIS publication data, and its effectiveness is verified through case studies.

Immersive environments have become increasingly popular for visualizing and exploring large-scale, complex scientific data because of their key features: immersion, engagement, and awareness. Virtual reality offers numerous new interaction possibilities, including tactile and tangible interactions, gestures, and voice commands. However, it is crucial to determine the most effective combination of these techniques for a more natural interaction experience. In this paper, we present MEinVR, a novel multimodal interaction technique for exploring 3D molecular data in virtual reality. MEinVR combines VR controller and voice input to provide a more intuitive way for users to manipulate data in immersive environments. By using the VR controller to select locations and regions of interest and voice commands to perform tasks, users can efficiently perform complex data exploration tasks. Our findings provide suggestions for the design of multimodal interaction techniques in 3D data exploration in virtual reality.

Balinese carvings are cultural objects that adorn sacred buildings. The carvings consist of several motifs, each representing the values adopted by the Balinese people. Detection of Balinese carving motifs is challenging due to the unavailability of a Balinese carving dataset for detection tasks, high variance, and tiny-size carving motifs. This research aims to improve carving motif detection performance on challenging Balinese carving motifs detection task through a modification of YOLOv5 to support a digital carving conservation system. We proposed CARVING-DETC, a deep learning-based Balinese carving detection method consisting of three steps. First, the data generation step performs data augmentation and annotation on Balinese carving images. Second, we proposed a network scaling strategy on the YOLOv5 model and performed non-maximum suppression (NMS) on the model ensemble to generate the most optimal predictions. The ensemble model utilizes NMS to produce higher performance by optimizing the detection results based on the highest confidence score and suppressing other overlap predictions with a lower confidence score. Third, performance evaluation on scaled-YOLOv5 versions and NMS ensemble models. The research findings are beneficial in conserving the cultural heritage and as a reference for other researchers. In addition, this study proposed a novel Balinese carving dataset through data collection, augmentation, and annotation. To our knowledge, it is the first Balinese carving dataset for the object detection task. Based on experimental results, CARVING-DETC achieved a detection performance of 98%, which outperforms the baseline model.

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.