Computer Graphics & Visual Computing (CGVC) 2024

Permanent URI for this collection

https://diglib.eg.org/handle/10.2312/3607040

Browse

Now showing 1 - 20 of 29

Does Empirical Evidence from Healthy Aging Studies Predict a Practical Difference Between Visualizations for Different Age Groups?
(The Eurographics Association, 2024) Shao, Shan; Li, Yiran; Meso, Andrew I.; Holliman, Nicolas S.; Hunter, David; Slingsby, Aidan
When communicating critical information to decision-makers, one of the major challenges in visualization is whether the communication is affected by different perceptual or cognitive abilities, one major influencing factor is age. We review both visualization and psychophysics literature to understand where quantitative evidence exists on age differences in visual perception. Using contrast sensitivity data from the literature we show how the differences between visualizations for different age groups can be predicted using a new model of visible frequency range with age. The model assumed that at threshold values some visual data will not be visible to older people (spatial frequency > 2 and contrast <=0.01). We apply this result to a practical visualization and show an example that at higher levels of contrast, the visual signal should be perceivable by all viewers over 20. Universally usable visualization should use a contrast of 0.02 or higher and be designed to avoid spatial frequencies greater than eight cycles per degree to accommodate all ages. There remains much research to do on to translate psychophysics results to practical quantitative guidelines for visualization producers.
Use of Notebooks and Role of Map features in Mapping Minority Women Bicycle Riding
(The Eurographics Association, 2024) Reljan-Delaney, Mirela; Wood, Jo D.; Taylor, Alex S.; Hunter, David; Slingsby, Aidan
Visualization has greatly enhanced our understanding of cycling trends [fL18], enabled the depiction and analysis of largescale cycling data [BWB14, RMGZALD18], and facilitated the tracking and interpretation of personal behaviour through the dashboards of personal tracking devices [NKKW20]. Data and visualization can be either vast and generalized or intimate and personal. There are significant challenges associated with big data as certain subgroups are underrepresented in data collection, making their presence difficult to detect and more targeted and smaller data collection can complement and expose facets of the population that are not visible in big data. Ethnic minority women cyclists are one such group. Research into their attitudes and cycling habits is often outdated [Lim10] or originates from contexts where their ethnicity is the majority [GOF∗22]. This study aims to shed light on the experiences ofMuslim and BAME women cyclists, uncovering hidden realities and challenging dominant narratives. A small group of ethnic minority women participated in the research, keeping diaries of their cycling experiences and using GPS trackers. The collected data was presented back to them in the form of individual data notebooks, combining technology, visualization, and ultimately qualitative analysis. This empirical work provides a fresh perspective on how female cyclists interact with their environment and offers valuable understanding of the preferences and challenges faced by this growing and vibrant group. This paper builds upon the previously published work [RDWT23], shifting the focus away from the methodological execution of the study and instead emphasizing the participants' interactions with the maps and the unique insights gained.
LLM-Assisted Visual Analytics: Opportunities and Challenges
(The Eurographics Association, 2024) Hutchinson, Maeve; Jianu, Radu; Slingsby, Aidan; Madhyastha, Pranava; Hunter, David; Slingsby, Aidan
We explore the integration of large language models (LLMs) into visual analytics (VA) systems to transform their capabilities through intuitive natural language interactions. We survey current research directions in this emerging field, examining how LLMs are integrated into data management, language interaction, visualisation generation, and language generation processes. We highlight the new possibilities that LLMs bring to VA, especially how they can change VA processes beyond the usual use cases. We especially highlight building new visualisation-language models, allowing access of a breadth of domain knowledge, multimodal interaction, and opportunities with guidance. Finally, we carefully consider the prominent challenges of using current LLMs in VA tasks. Our discussions in this paper aim to guide future researchers working on LLM-assisted VA systems and help them navigate common obstacles when developing these systems.
Serial Gaussian Blue Noise Stippling
(The Eurographics Association, 2024) Ahmed, Abdalla G. M.; Hunter, David; Slingsby, Aidan
We adapt the adaptive Gaussian Blue Noise (GBN) algorithm to iterate serially over the points, one by one, thus enabling its implementation on CPU. Towards that end, we propose an alternative kernel shaping model. Our implementation model is simpler and has a linear time complexity, replacing the quadratic complexity of the original model.
Min-Max Modifiable Nested Octrees M3NO: Indexing Point Clouds with Arbitrary Attributes in Real Time
(The Eurographics Association, 2024) Hermann, Paul; Krämer, Michel; Dorra, Tobias; Kuijper, Arjan; Hunter, David; Slingsby, Aidan
We present a data structure that allows 3D point clouds with arbitrary attributes to be indexed in real time. We focus on large datsets from mobile mapping systems such as airborne and terrestrial laser scanners. Compared to traditional indexing approaches running offline, our data structure can be created incrementally while the points are being recorded. This allows the data to be used (i.e. analyzed or visualized) already during acquisition or immediately after it has finished. The data structure enables queries based on spatial extent and value ranges of arbitrary attributes. This is in contrast to existing works, which focus on either spatial or attribute indexing, typically are not real-time capable, or only support a limited set of attributes. Our approach combines Modifiable Nested Octrees and extended Binned Min-Max Octrees. Using a subset of the well known AHN4 dataset with 138 million points, we evaluate the approach, assess quality and query performance, and compare it with an existing state-of-the-art solution. On commodity hardware, our data structure can process 1.97 million points per second, which is more than most commercially available laser scanners can record. When filtering points by attribute value ranges, it also reduces the number of octree nodes that have to be loaded, and it substantially outperforms naive sequential point filtering.
Exploring High-Dimensional Data by Pointwise Filtering of Low-Dimensional Embeddings
(The Eurographics Association, 2024) Atzberger, Daniel; Jobst, Adrian; Scheibel, Willy; Döllner, Jürgen; Hunter, David; Slingsby, Aidan
Dimensionality reductions are a class of unsupervised learning algorithms that aim to find a lower-dimensional embedding for a high-dimensional dataset while preserving local and global structures. By representing a high-dimensional dataset as a twodimensional scatterplot, a user can explore structures within the dataset. However, dimensionality reductions inherit distortions that might result in false deductions. This work presents a visualization approach that combines a two-dimensional scatterplot derived from a dimensionality reduction with two pointwise filtering possibilities. Each point is associated with two pointwise metrics that quantify the correctness of its neighborhood and similarity to surrounding data points. By setting threshold for these two metrics, the user is supported in several scatterplot analytics tasks, e.g., class separation and outlier detection. We apply our visualization to a text corpus to detect interesting data points visually and discuss the findings.
Investigation of the Organisation of the Extracellular Matrix Using Fibre Tracing
(The Eurographics Association, 2024) Arafat, Youssef; Cuesta-Apausa, Cristina; Castellano, Esther; Reyes-Aldasoro, Constantino Carlos; Hunter, David; Slingsby, Aidan
Throughout history, the observation of medical and biological samples has been of high importance and has led to many discoveries. When this process relies on human observation, it can be time-consuming, especially with the advent of technological advancements that generate more and more images at faster rates. Additionally, some features of the samples can be undetectable by the naked eye, but with the aid of visual computing techniques, these hidden details can be revealed. The morphological characteristics of the extracellular matrix play a vital role in cancer and other health conditions. Visual observations of the ECM can provide valuable insights; however, the task may be tedious and sometimes it is hard to quantify the differences between samples. In this work, a tracing algorithm is proposed. Furthermore, morphological characteristics of the extracellular matrix can be extracted with the algorithm to quantify and compare different biological populations. Experiments revealed that the removal of interactions in fibroblasts affected their ability to form a healthy extracellular matrix as compared with a wild type population. Here, an investigation of the morphological differences between the ECM of two populations was conducted. Five images of mutant and five images of wild type cells growing in culture were compared. A deconvolutional convolutional neural network was used as a pre-processing filtering method to remove noise from the images. The images are then traced by the proposed algorithm, Trace Ridges, to extract morphological features and visually present the edges and gaps extracted. Trace Ridges combines methods of Edge detection, watershed, and morphological characteristics to delineate fibre-like structures. Two morphological characteristics provided statistical differences between the populations: number of fibres (p−value = 0.00091) and relative area of gaps between the fibres (p−value = 0.014). The number of fibres detected in wild type was higher than mutant while the relative gaps area size of mutant was higher than that of WT. Trace Ridges was able to successfully delineate the ECM fibres of mutant and wild type cells and extract morphological features to show the difference between the populations.
DeFT-Net: Dual-Window Extended Frequency Transformer for Rhythmic Motion Prediction
(The Eurographics Association, 2024) Ademola, Adeyemi; Sinclair, David; Koniaris, Babis; Hannah, Samantha; Mitchell, Kenny; Hunter, David; Slingsby, Aidan
Enabling online virtual reality (VR) users to dance and move in a way that mirrors the real-world necessitates improvements in the accuracy of predicting human motion sequences paving way for an immersive and connected experience. However, the drawbacks of latency in networked motion tracking present a critical detriment in creating a sense of complete engagement, requiring prediction for online synchronization of remote motions. To address this challenge, we propose a novel approach that leverages a synthetically generated dataset based on supervised foot anchor placement timings of rhythmic motions to ensure periodicity resulting in reduced prediction error. Specifically, our model compromises a discrete cosine transform (DCT) to encode motion, refine high frequencies and smooth motion sequences and prevent jittery motions. We introduce a feed-forward attention mechanism to learn based on dual-window pairs of 3D key points pose histories to predict future motions. Quantitative and qualitative experiments validating on the Human3.6m dataset result in observed improvements in the MPJPE evaluation metrics protocol compared with prior state-of-the-art.
Interplay of Visual Analytics and Topic Modeling in Gameplay Analysis
(The Eurographics Association, 2024) Moussavi, Laleh; Andrienko, Gennady; Andrienko, Natalia; Slingsby, Aidan; Hunter, David; Slingsby, Aidan
Spatio-temporal event sequences consist of activities or occurrences involving various interconnected elements in space and time. Exploring these sequences with topic modeling is a relatively new and evolving research area. We use topic modeling to analyze football games, as an example of complex and under-explored spatio-temporal event data. A key challenge in topic modeling is selecting the most suitable number of topics for the downstream application. Selecting too few topics oversimplifies the data, merging distinct patterns, whereas selecting too many can fragment coherent themes into overlapping categories. We propose a visual analytics technique that uses dimensionality reduction on topics derived from multiple topic modeling runs, each with a different number of topics. Our technique organizes all the topics in a hierarchical layout based on their spatial similarity, making it easier to make an informed decision about selecting the most expressive set of topics that represent distinctive spatial patterns. We apply our visual analytics technique to a football dataset, illustrating how it can be used to select an appropriate set of topics for this data. We then use these topics to represent game episodes, which help us summarize game dynamics and uncover insights into the games.
Map Augmentation and Sketching for Cycling Experience Elicitation
(The Eurographics Association, 2024) Reljan-Delaney, Mirela; Wood, Jo D.; Taylor, Alex S.; Hunter, David; Slingsby, Aidan
This work examines the use of maps for knowledge elicitation in the sphere of urban cycling. The study involved running 14 distinct workshops, each serving as a unique data collection session for a particular individual. In each workshop, the participant was provided with 12 different renditions of the geographical areas as well as drawing materials. The geographical area renditions contained regions specified by the participant as cycling locations during the preparatory correspondence. The outputs were analysed for patterns in map augmentations and thematic content in the sketches. We have found that participants engaged deeply with the map augmentation process expressing their preferences and giving new insights. Themes such as connectivity, scenic beauty, and temporality emerged prominently from the analysed data, shedding light on the subjective experiences and preferences of urban cyclists.
View-Consistent Virtual Try-on of Glasses using a Hybrid NeRF-Mesh Rendering Approach
(The Eurographics Association, 2024) Rak, Arne; Wirth, Tristan; Lindemeier, Thomas; Knauthe, Volker; Kuijper, Arjan; Hunter, David; Slingsby, Aidan
In recent times, an increasing fraction of global purchases is conducted via the world wide web. For individual accessories, such as glasses, a purchase commonly involves trying on multiple products to fit individual aesthetic preferences. The experience of the try-on process differs greatly between online and offline shopping. While there are real-time methods that facilitate virtual try-on of glasses, they usually project them onto a 2D image. This leads to inconsistent positioning of the glasses model between different views, negatively influencing the shopping experience. We propose a strategy, that enables the virtual try-on of glasses using a Neural Radiance Field as head avatar and a meshed glasses model, leading to consistent positioning of the spectacle frame through multiple views while maintaining real world like visual quality. We contribute an approach for placing and aligning the glasses in relation to the human head in the given NeRF context. Furthermore, we propose a framework for realtime hybrid rendering of meshes and Neural Radiance Fields in the same scene. The proposed method requires training times around one minute and produces a freely explorable 3D model that achieves interactive framerates on end-consumer hardware.
A Stereo-Integrated Novel View Synthesis Pipeline for the Enhancement of Road Surface Reconstruction Dataset
(The Eurographics Association, 2024) Zhan, Mochuan; Morley, Terence; Turner, Martin; Hunter, David; Slingsby, Aidan
This proposal outlines a novel view synthesis pipeline designed for road reconstruction in autonomous driving scenarios that leverages virtual camera technology to synthesise images from unvisited camera poses, thereby enhancing and expanding current datasets. It consists of three main steps: data acquisition, data preprocessing and fusion, and then importantly interacting with new 3D view synthesis with geometric priors. The modular design allows each component to be independently optimised and upgraded, ensuring flexibility and adaptability to various datasets and task requirements. The proposed approach aims to improve the robustness, realism, and photometric consistency of novel view synthesis, effectively handling dynamic scenes and varying lighting conditions. Additionally, this research plans to open-source a low-cost stereo camera hardware solution with the included software implementation.
Comparing Distance Metrics in Space-time Clustering to Provide Visual Summaries of Traffic Congestion
(The Eurographics Association, 2024) Baudains, Peter; Holliman, Nicolas S.; Hunter, David; Slingsby, Aidan
Smart Cities are characterised by their ability to collect and process large volumes of sensor data. Visual analytics is then often required to make this data actionable and to allow decisions to be made in support of the well-being of inhabitants. In this study, using Bus Open Data, we consider how space-time clustering can be used to generate visual summaries of traffic congestion. Using a space-time extension of DBSCAN, our clustering procedure is evaluated with respect to both Euclidean distance and street network distance. Results show that network-based distance metrics improve the clustering procedure by generating clusters with less uncertainty. Moreover, congestion clusters derived from network-based distances are also more likely to last longer and to precede future congestion appearing nearby. We suggest that network-based distances might provide greater opportunity for more impactful traffic control room decision-making and we discuss steps towards a near real-time system design that can be used in support of operational decision-making.
Multi-fidelity Multi-disciplinary Optimisation of Propeller Design by Visual Analytics
(The Eurographics Association, 2024) Shubham, Shubham; Spinelli, Andrea; Kipouros, Timoleon; Hunter, David; Slingsby, Aidan
This paper introduces a comprehensive framework for multi-fidelity, multi-disciplinary optimization of propeller design using visual analytics. The proposed methodology integrates advanced data visualization techniques, surrogate modelling and optimisation methodologies to handle high-dimensional data across various disciplines, including aerodynamics, aeroacoustics, and structures. By leveraging multi-fidelity simulations, the framework balances accuracy with computational efficiency, enabling detailed exploration and optimization of propeller designs. Interactive visualization tools in the framework facilitate the identification of optimal design parameters and trade-offs, highlighting its potential to improve decision-making in engineering design processes in terms of confidence and knowledge creation.
Semantic UV Mapping to Improve Texture Inpainting for 3D Scanned Indoor Scenes
(The Eurographics Association, 2024) Vermandere, Jelle; Bassier, Maarten; Cuypers, Suzanna; Vergauwen, Maarten; Hunter, David; Slingsby, Aidan
This work aims to improve texture inpainting following clutter removal in scanned indoor meshes. This is achieved through a new UV mapping pre-processing step that leverages semantic information from indoor scenes to more accurately align the UV islands with the 3D representations of distinct structural elements, such as walls and floors. Semantic UV Mapping enhances traditional UV unwrapping algorithms by incorporating not only geometric features but also visual features derived from the existing texture. This segmentation improves UV mapping and simultaneously simplifies the 3D geometric reconstruction of the scene after the removal of loose objects. Each segmented element can then be reconstructed separately, using the boundary conditions of the adjacent elements. Since this is performed as a pre-processing step, other specialized methods for geometric and texture reconstruction can be employed in the future to further enhance the results.
Computer Graphics and Visual Computing (CGVC): Frontmatter
(The Eurographics Association, 2024) Slingsby, Aidan; Hunter, David; Slingsby, Aidan; Hunter, David
Visual Storytelling: A Methodological Approach to Designing and Implementing a Visualization Poster
(The Eurographics Association, 2024) Owen, Rhiannon S.; Roberts, Jonathan C.; Hunter, David; Slingsby, Aidan
We present a design study of developing a visualisation poster. Posters can be difficult to create, and the story on a poster is not always clear. Using a case-study approach we propose three important aspects: the poster should have a clear focus (especially a hero visualisation), envisioning its use helps to drive the important aspects, and third the essence (its fundamental concept and guiding idea) must be clear. We will use case studies that have focused on the use of the Five Design-Sheet method (FdS) as a way to sketch and plan a visualisation, before successfully implementing and creating the visual poster. The case studies serve as a practical illustration of the workflow, offering a means to explain the three key processes involved: (1) comprehending the data, (2) employing a design study with the FdS (Five Design-Sheet), (3) crafting, evaluating and refining the visualisation.
Reflections on the Evolution of the BookTracker Visualization Platform
(The Eurographics Association, 2024) Xing, Yiwen; Dondi, Cristina; Borgo, Rita; Abdul-Rahman, Alfie; Hunter, David; Slingsby, Aidan
Understanding the trade data of historical books is crucial for researchers investigating the distribution and provenance of Incunabula (books printed between 1450 and 1500). We incrementally developed BookTracker, a platform featuring multiple visualization and visual analytics applications to support these research efforts. This platform leverages data from the Material Evidence in Incunabula (MEI) database, which meticulously records detailed information on the provenance, ownership, and use of 15th-century printed books. BookTracker began with a focus on providing visualization and visual analytical solutions to effectively present each book provenance's chronological and geographical information. Through three years of collaborative work with domain experts, we continually explored the Material Evidence in Incunabula (MEI) data and discovered more possibilities for visualization to represent this rich information. Gradually, a suite of specialized visualization tools for specific analytical purposes was developed, including DanteSearchVis, DanteExploreVis, KURF2022, KURF2023, and OwnershipTracker. These tools now comprise the BookTracker platform, which has evolved to explore various features and aspects of the data. This paper details the evolution of BookTracker's design and development alongside domain experts, highlighting the reflections and lessons learned from its application in various research projects. We discuss this long-term collaborative visualization project, hoping to offer our experience as a case study for similar research in the future.
Authoring Visualisation of Routinely Collected Data Using LLMs
(The Eurographics Association, 2024) Hosseini, Amir; Wood, Jo; Elshehaly, Mai; Hunter, David; Slingsby, Aidan
The integration of routinely collected healthcare data into decision-making processes has the potential to revolutionise patient care and health outcomes. However, the complexity and heterogeneity of these datasets pose significant challenges for effective querying and analysis. Visualisation supports socio-technical processes where data analytics are augmented with human expertise to overcome data complexity. However, the authorship of effective visualisation is a challenging task, especially for users without a technical background, such as commissioners, clinicians and population health experts. This complexity calls for more efforts to develop natural language interfaces (NLIs) to democratise access to and understanding of routine data through visualisation. This short paper presents an innovative approach utilising Large Language Models (LLMs) to facilitate the querying and visualisation of routinely collected healthcare data. We present a preliminary framework for combining natural language queries with visualisation recommendation systems to retrieve and visualise relevant information from electronic health records (EHRs). We propose a human-in-the-loop approach for establishing accurate and efficient LLM-enabled information retrieval. Our preliminary findings suggest that LLMs can significantly streamline the visualisation authoring process, enabling stakeholders and healthcare professionals to access critical information rapidly and accurately. This work underscores the potential of LLM-driven solutions in advancing healthcare data utilisation and paves the way for future research in this promising intersection of artificial intelligence and medical informatics.
Exploring Data Analysts' Uncertainty Reasoning Strategies for Effective Uncertainty Visualization Design
(The Eurographics Association, 2024) Kinkeldey, Christoph; Reljan-Delaney, Mirela; Panagiotidou, Georgia; Dykes, Jason; Hunter, David; Slingsby, Aidan
Despite its proven positive effects, visual data analysis rarely includes information about data uncertainty. Building on past research, we explore the hypothesis that effective uncertainty visualizations must support reasoning strategies that enable data analysts to utilize uncertainty information ('uncertainty reasoning strategies', UnReSt). Through this work, we seek to gain insights into the reasoning strategies employed by domain experts for incorporating uncertainty into their visual analysis. Additionally, we aim to explore effective ways of designing uncertainty visualizations that support these strategies. For this purpose, we developed a methodology involving online meetings that included think-aloud protocols and interviews. We applied the methodology in a user study with five domain experts from the field of epidemiology. Our findings identify, describe, and discuss the UnReSt employed by our participants, allowing for initial recommendations as a foundation for future design guidelines: uncertainty visualization should (i) visually support data analysts in adapting or developing UnReSt, (ii) not facilitate ignoring the uncertainty, (iii) aid in the definition of acceptable levels of uncertainty, and (iv) not hide uncertain parts of the data by default. We reflect on the methodology we developed and applied in our study, addressing challenges related to the recruiting process, the examination of an existing tool along with familiar tasks and data, the design of bespoke prototypes in collaboration with visualization experts, and the timing of the meetings. We encourage visualization researchers to adapt this methodology to gain deeper insights into the UnReSt of data analysts and how uncertainty visualization can effectively support them. The supplemental materials can be found at https://osf.io/s2nwf/.

Browse

Browsing Computer Graphics & Visual Computing (CGVC) 2024 by Issue Date

Results Per Page

Sort Options