Computer Graphics & Visual Computing (CGVC) 2020

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https://diglib.eg.org/handle/10.2312/2632929

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Browsing Computer Graphics & Visual Computing (CGVC) 2020 by Subject "Applied computing"

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    Breathing Life into Statues Using Augmented Reality
    (The Eurographics Association, 2020) Ioannou, Eleftherios; Maddock, Steve; Ritsos, Panagiotis D. and Xu, Kai
    AR art is a relatively recent phenomenon, one that brings innovation in the way that artworks can be produced and presented in real-world locations and environments. We present an AR art app, running in real time on a smartphone, that can be used to bring to life inanimate objects such as statues. The work relies on a virtual copy of the real object, which is produced using photogrammetry, as well as a skeleton rig for subsequent animation. As part of the work, we present a new diminishing reality technique, based on the use of particle systems, to make the real object 'disappear' and be replaced by the animating virtual copy, effectively animating the inanimate. The approach is demonstrated on two objects: a juice carton and a small giraffe sculpture.
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    Medical Ultrasound Training in Virtual Reality
    (The Eurographics Association, 2020) Elliman, James P.; Bethapudi, Sarath; Koulieris, George Alex; Ritsos, Panagiotis D. and Xu, Kai
    In this work we propose a novel training solution for learning and practising the core psychomotor skills required in Diagnostic Ultrasound examinations with a computer-based simulator. This is in response to the long-standing challenges faced by educators in providing regular training opportunities as a shortage of equipment, staff unavailability and cost, hamper the current training model. We propose an alternative, VR-based model with a highly realistic 3D environment. To further realism of the experience, 3D printed props that work in conjunction with the simulation software will be designed. Our approach further extends previous work in generative model-based US simulation by developing a ray-tracing algorithm for use with the recently released NVidia RTX technology.
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    Recognising Specific Foods in MRI Scans Using CNN and Visualisation
    (The Eurographics Association, 2020) Gardner, Joshua; Al-Maliki, Shatha; Lutton, Évelyne; Boué, François; Vidal, Franck; Ritsos, Panagiotis D. and Xu, Kai
    This work is part of an experimental project aiming at understanding the kinetics of human gastric emptying. For this purpose magnetic resonance imaging (MRI) images of the stomach of healthy volunteers have been acquired using a state-of-art scanner with an adapted protocol. The challenge is to follow the stomach content (food) in the data. Frozen garden peas and petits pois have been chosen as experimental proof-of-concept as their shapes are well defined and are not altered in the early stages of digestion. The food recognition is performed as a binary classification implemented using a deep convolutional neural network (CNN). Input hyperparameters, here image size and number of epochs, were exhaustively evaluated to identify the combination of parameters that produces the best classification. The results have been analysed using interactive visualisation. We prove in this paper that advances in computer vision and machine learning can be deployed to automatically label the content of the stomach even when the amount of training data is low and the data imbalanced. Interactive visualisation helps identify the most effective combinations of hyperparameters to maximise accuracy, precision, recall and F1 score, leaving the end-user evaluate the possible trade-off between these metrics. Food recognition in MRI scans through neural network produced an accuracy of 0.97, precision of 0.91, recall of 0.86 and F1 score of 0.89, all close to 1.
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    Simulating Dynamic Ecosystems with Co-Evolutionary Agents
    (The Eurographics Association, 2020) Ferguson, Gary; Vidal, Franck; Ritsos, Panagiotis D. and Xu, Kai
    As video games grow in complexity and require increasingly large and immersive environments, there is a need for more believable and dynamic characters not controlled by the player, known as non-player character (NPC). Video game developers will often face the challenge of designing these NPCs in a time efficient manner. We propose an agent-based Cooperative Co-evolution Algorithm (CCEA) where NPCs are implemented as artificial life (AL) agents that are created through an evolutionary process based on simple rules. The virtual environment can be filled with a range of interesting agents, each acting independently from one another, to fulfil their own wants and needs. The proposed middleware framework is suitable for computer animation of NPCs and the development of video games, especially where swarm intelligence is simulated. We proved that agents implemented with a very limited number of variables making up their genome can be successfully integrated in a co-evolutionary multi-agent system (CoEMAS). Results showed promising levels of speciation and interesting emergent and plausible behaviours amongst the agents.
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    Visualizing Usage Data from a Diabetes Management System
    (The Eurographics Association, 2020) Duce, David A.; Martin, Clare; Russell, Alex; Brown, Dan; Aldea, Arantza; Alshaigy, Bedour; Harrison, Rachel; Waite, Marion; Leal, Yenny; Wos, Marzena; Fernandez-Balsells, Mercè; Real, José Manuel Fernández; Nita, Lucian; López, Beatriz; Massana, Joaquim; Avari, Parizad; Herrero, Pau; Jugnee, Narvada; Oliver, Nick; Reddy, Monika; Ritsos, Panagiotis D. and Xu, Kai
    This article explores the role for visualization in interpreting data collected by a customised analytics framework within a healthcare technology project. It draws on the work of the EU-funded PEPPER project, which has created a personalised decision-support system for people with type 1 diabetes. Our approach was an exercise in exploratory visualization, as described by Bergeron's three category taxonomy. The charts revealed different patterns of interaction, including variability in insulin dosing schedule, and potential causes of rejected advice. These insights into user behaviour are of especial value to this field, as they may help clinicians and developers understand some of the obstacles that hinder the uptake of diabetes technology.