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Now showing 1 - 10 of 52
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    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.
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    EBBVH: A Novel Method for Constructing Bounding Volume Hierarchies
    (The Eurographics Association, 2024) Houghton, Matthew; Spoerer, Kristian; Hunter, David; Slingsby, Aidan
    We present an attempt to improve upon the construction of the most prevalent acceleration structure that is used in ray traced rendering techniques, the Bounding Volume Hierarchy. Our improvement is a novel technique for BVH construction called 'Edge-Based Bounding Volume Hierarchy'. This algorithm uses a hybrid top-down & bottom-up approach to improve performance for raytracing in large scenes, by up to 10x in some scenes.
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    Immersive WebXR Data Visualisation Tool
    (The Eurographics Association, 2023) Ogbonda, Ebube Glory; Vangorp, Peter; Hunter, David
    This paper presents a study of a WebXR data visualisation tool designed for the immersive exploration of complex datasets in a 3D environment. The application developed using AFrame, D3.js, and JavaScript enables an interactive, device-agnostic platform compatible with various devices and systems. A user study is proposed to assess the tool's usability, user experience, and mental workload using the NASA Task Load Index (NASA TLX). The evaluation is planned to employ questionnaires, task completion times, and open-ended questions to gather feedback and insights. The anticipated results aim to provide insights into the effectiveness of the application in supporting users in understanding and extracting insights from complex data while delivering an engaging and intuitive experience. Future work will refine and expand the tool's capabilities by exploring interaction guidance, visualisation layout optimisation, and long-term user experience assessment. This research contributes to the growing field of immersive data visualisation and informs future tool design.
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    Interweaving Data and Stories: A Case Study on Unveiling the Human Dimension of U.S. Refugee Movements through Narrative Visualisation
    (The Eurographics Association, 2023) Ogbonda, Ebube Glory; Roberts, Jonathan C.; Butcher, Peter W. S.; Vangorp, Peter; Hunter, David
    In response to the escalating global refugee crisis, we present a case-study of developing an advanced tool for interpreting high-dimensional refugee data. Developed using Mapbox and D3.js, our interactive visualisation harmonises geographical and temporal dimensions of U.S. refugee data from the State Department's Refugee Processing Center. Our modular approach and robust data preprocessing enable seamless interactions among diverse visual components. The result is a narrative-driven visualisation that reveals broad immigration trends and individual refugee movements, fostering a nuanced and empathetic understanding of refugee dynamics. This work highlights the power of narrative visualisations in shaping policy decisions and promoting global discourse on the refugee crisis, marking a significant leap in data visualisation for refugee and immigration challenges.
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    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.
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    Inpainting Normal Maps for Lightstage data
    (The Eurographics Association, 2023) Zuo, Hancheng; Tiddeman, Bernard; Vangorp, Peter; Hunter, David
    This paper presents a new method for inpainting of normal maps using a generative adversarial network (GAN) model. Normal maps can be acquired from a lightstage, and when used for performance capture, there is a risk of areas of the face being obscured by the movement (e.g. by arms, hair or props). Inpainting aims to fill missing areas of an image with plausible data. This work builds on previous work for general image inpainting, using a bow tie-like generator network and a discriminator network, and alternating training of the generator and discriminator. The generator tries to sythesise images that match the ground truth, and that can also fool the discriminator that is classifying real vs processed images. The discriminator is occasionally retrained to improve its performance at identifying the processed images. In addition, our method takes into account the nature of the normal map data, and so requires modification to the loss function. We replace a mean squared error loss with a cosine loss when training the generator. Due to the small amount of available training data available, even when using synthetic datasets, we require significant augmentation, which also needs to take account of the particular nature of the input data. Image flipping and in-plane rotations need to properly flip and rotate the normal vectors. During training, we monitored key performance metrics including average loss, Structural Similarity Index Measure (SSIM), and Peak Signal-to-Noise Ratio (PSNR) of the generator, alongside average loss and accuracy of the discriminator. Our analysis reveals that the proposed model generates high-quality, realistic inpainted normal maps, demonstrating the potential for application to performance capture. The results of this investigation provide a baseline on which future researchers could build with more advanced networks and comparison with inpainting of the source images used to generate the normal maps.
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    Skipping Spheres: SDF Scaling & Early Ray Termination for Fast Sphere Tracing
    (The Eurographics Association, 2024) Polychronakis, Andreas; Koulieris, George Alex; Mania, Katerina; Hunter, David; Slingsby, Aidan
    This paper presents a rapid rendering pipeline for sphere tracing Signed Distance Functions (SDFs), showcasing a notable boost in performance compared to the current state-of-the-art. Existing methods endeavor to reduce the ray step count by adjusting step size using heuristics or by rendering multiple intermediate lower-resolution buffers to pre-calculate non-salient pixels at reduced quality. However, the accelerated performance with low-resolution buffers often introduces artifacts compared to fully sphere-traced scenes, especially for smaller features, which might go unnoticed altogether. Our approach significantly reduces steps compared to prior work while minimising artifacts. We accomplish this based on two key observations and by employing a single low-resolution buffer: Firstly, we perform SDF scaling in the low-resolution buffer, effectively enlarging the footprint of the implicit surfaces when rendered in low resolution, ensuring visibility of all SDFs. Secondly, leveraging the low-resolution buffer rendering, we detect when a ray converges to high-cost surface edges and can terminate sphere tracing earlier than usual, further reducing step count. Our method achieves a substantial performance improvement (exceeding 3× in certain scenes) compared to previous approaches, while minimizing artifacts, as demonstrated in our visual fidelity evaluation.
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    Towards Ceramics Inspired Physiotherapy for Recovering Stroke Patients
    (The Eurographics Association, 2023) Hajzer, Sándor P.; Jones, Andra; Jones, David E.; Miles, Helen C.; Ellis, Victoria; Povina, Federico V.; Sganga, Magalí; Swain, Martin T.; Bennett-Gillison, Sophie; Vangorp, Peter; Hunter, David
    People prescribed physiotherapy exercises can struggle to engage with exercises due to a lack of mental stimulation in the repetitive tasks. The introduction of VR to motion-based physiotherapy can be beneficial, however, currently available physiotherapy applications are focused on gaming and the gamification of physiotherapy, something that will not appeal to all patients. This project presents work in-progress towards a VR ceramics painting inspired physiotherapy application, where patients are guided to perform a series of simple motion exercises under the supervision of physiotherapists. Literature shows that art-based therapy can improve patient outcome, and ceramics involves a range of 3D movements that can be aligned with physiotherapy exercises. The work presented is intended to inform future research and development efforts.
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    Using The Barnes-Hut Approximation for Fast N-Body Simulations in Computer Graphics
    (The Eurographics Association, 2023) Dravecky, Peter; Stephenson, Ian; Vangorp, Peter; Hunter, David
    Particle systems in CG often encounter performance issues when all the particles rely on mutual influence, producing an O(N2) performance. The Barnes-Hut approximation is used in the field of astrophysics to provide sufficiently accurate results in O(Nlog(N)) time. Here we explore a hardware accelerated implementation of this algorithm, implemented within SideFX Houdini - the commercial tool typically used for particle work in film. We are able to demonstrate a workflow with integrates into the existing artist friendly environment, with performance improved by orders of magnitudes for typically large simulations, and negligible visual change in results.
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    Real-time Data-Oriented Virtual Forestry Simulation for Games
    (The Eurographics Association, 2024) Williams, Benjamin; Oliver, Tom; Ward, Davin; Headleand, Chris; Hunter, David; Slingsby, Aidan
    The current frontier of virtual forestry algorithms remain largely unoptimised and ultimately unsuitable for real-time applications. Providing an optimisation strategy for the real-time simulation of virtual forestry would find particular utility in some areas, for example, in video games. With this motivation in mind, this paper presents a novel optimisation strategy for asymmetric plant competition models. In our approach, we utilise a data-oriented methodology with spatial hashing to enable the real-time simulation of virtual forests. Our approach also provides a significant improvement in performance when contrasted with existing serial implementations. Furthermore, we find that the introduction of our optimisation strategy can be used to simulate hundreds of thousands of virtual trees, in real-time, on a typical desktop machine.