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Now showing 1 - 9 of 9
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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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    Segmenting Teeth from Volumetric CT Data with a Hierarchical CNN-based Approach
    (The Eurographics Association, 2018) Macho, Philipp Marten; Kurz, Nadja; Ulges, Adrian; Brylka, Robert; Gietzen, Thomas; Schwanecke, Ulrich; {Tam, Gary K. L. and Vidal, Franck
    This paper addresses the automatic segmentation of teeth in volumetric Computed Tomography (CT) scans of the human skull. Our approach is based on a convolutional neural network employing 3D volumetric convolutions. To tackle data scale issues, we apply a hierarchical coarse-to fine approach combining two CNNs, one for low-resolution detection and one for highresolution refinement. In quantitative experiments on 40 CT scans with manually acquired ground truth, we demonstrate that our approach displays remarkable robustness across different patients and device vendors. Furthermore, our hierarchical extension outperforms a single-scale segmentation, and network size can be reduced compared to previous architectures without loss of accuracy.
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    AUPE: An Emulator for the ExoMars PanCam Instrument
    (The Eurographics Association, 2023) Ladegaard, Ariel; Gunn, Matt; Miles, Helen C.; Tyler, Laurence; Vangorp, Peter; Hunter, David
    The European Space Agency's ExoMars mission will be the first European-led planetary rover mission and much preparation and rehearsal is required, both for the personnel involved and the data processing pipelines and analysis software. The long instrument development cycle and significant cost associated with flight hardware prohibits their use for extensive field deployment and testing and so emulator systems are required. For this reason an emulator for the PanCam camera system was developed using commercial off-the-shelf components. PanCam's multispectral imaging capabilities will be used to guide the rover to sites of scientific interest, and development of this emulator and the associated data processing techniques are proving invaluable in ensuring the visual-based data products provided to scientists are accurate and that their processing is a transparent and traceable process.
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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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    Augmenting Anomaly Detection Datasets with Reactive Synthetic Elements
    (The Eurographics Association, 2023) Nikolov, Ivan; Vangorp, Peter; Hunter, David
    Automatic anomaly detection for surveillance purposes has become an integral part of accident prevention and early warning systems. The lack of sufficient real datasets for training and testing such detectors has pushed a lot of research into synthetic data generation. A hybrid approach by combining real images with synthetic elements has been proven to produce the best training results.We aim to extend this hybrid approach by combining the backgrounds and real people captured in datasets with synthetic elements which dynamically react to real pedestrians and create more coherent video sequences. Our pipeline is the first to directly augment synthetic objects like handbags and suitcases to real pedestrians and provides dynamic occlusion between real and synthetic elements in the images. The pipeline can be easily used to produce a continuous stream of randomized augmented normal and abnormal data for training and testing. As a basis for our augmented images, we use one of the most widely used classical datasets for anomaly detection - the UCSD dataset. We show that the synthetic data produced by our proposed pipeline can be used to make the dataset harder for state-of-the-art models, by introducing more varied and challenging anomalies. We also demonstrate that the additional synthetic normal data can boost the performance of some models. Our solution can be easily extended with additional 3D models, animations, and anomaly scenarios.
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    SunburstChartAnalyzer: Hierarchical Data Retrieval from Images of Sunburst Charts for Tree Visualization
    (The Eurographics Association, 2023) Rastogi, Prakhar; Singh, Karanveer; Sreevalsan-Nair, Jaya; Vangorp, Peter; Hunter, David
    Data extraction from visualization is a challenging problem in computer vision owing to the huge ''design space of possible vis idioms.'' Different visualizations pose different challenges in automated data extraction from their images, which is needed in document analysis. In the case of sunburst charts for hierarchical data, the extracted data has to be also correctly organized as a tree data structure. Overall, data extraction has to consider different components of a chart image, such as text, annular sectors, levels, etc., and their ordering. We propose an end-to-end algorithm, SunburstChartAnalyzer, for data extraction from sunburst charts. The algorithm includes chart classification, component extraction, and hierarchical data organization. We further propose a composite metric to evaluate the correctness of SunburstChartAnalyzer. Our experimental results show that our proposed method works for trees of all sizes, and particularly well for shallow and medium-depth trees.
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    A Deep Learning Approach to No-Reference Image Quality Assessment For Monte Carlo Rendered Images
    (The Eurographics Association, 2018) Whittle, Joss; Jones, Mark W.; {Tam, Gary K. L. and Vidal, Franck
    In Full-Reference Image Quality Assessment (FR-IQA) images are compared with ground truth images that are known to be of high visual quality. These metrics are utilized in order to rank algorithms under test on their image quality performance. Throughout the progress of Monte Carlo rendering processes we often wish to determine whether images being rendered are of sufficient visual quality, without the availability of a ground truth image. In such cases FR-IQA metrics are not applicable and we instead must utilise No-Reference Image Quality Assessment (NR-IQA) measures to make predictions about the perceived quality of unconverged images. In this work we propose a deep learning approach to NR-IQA, trained specifically on noise from Monte Carlo rendering processes, which significantly outperforms existing NR-IQA methods and can produce quality predictions consistent with FR-IQA measures that have access to ground truth images.
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    Image Inpainting for High-Resolution Textures using CNN Texture Synthesis
    (The Eurographics Association, 2018) Laube, Pascal; Grunwald, Michael; Franz, Matthias O.; Umlauf, Georg; {Tam, Gary K. L. and Vidal, Franck
    Deep neural networks have been successfully applied to problems such as image segmentation, image super-resolution, coloration and image inpainting. In this work we propose the use of convolutional neural networks (CNN) for image inpainting of large regions in high-resolution textures. Due to limited computational resources processing high-resolution images with neural networks is still an open problem. Existing methods separate inpainting of global structure and the transfer of details, which leads to blurry results and loss of global coherence in the detail transfer step. Based on advances in texture synthesis using CNNs we propose patch-based image inpainting by a CNN that is able to optimize for global as well as detail texture statistics. Our method is capable of filling large inpainting regions, oftentimes exceeding the quality of comparable methods for high-resolution images. For reference patch look-up we propose to use the same summary statistics that are used in the inpainting process.
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    Groupwise Non-rigid Image Alignment With Graph-based Initialisation
    (The Eurographics Association, 2018) Aal-Yhia, Ahmad; Malcolm, Paul; Akanyeti, Otar; Zwiggelaar, Reyer; Tiddeman, Bernard; {Tam, Gary K. L. and Vidal, Franck
    Groupwise image alignment automatically provides non-rigid registration across a set of images. It has found applications in facial image analysis and medical image analysis by automatically generating statistical models of shape and appearance. The main approaches used previously include iterative and graph-based approaches. In iterative approaches, the registration of each image is iteratively updated to minimise an error measure across the set. Various metrics and optimisation strategies have been proposed to achieve this. Graph-based methods perform registration of each pair of images in the set, to form a weighted graph of the ''distance'' between all the images, and then finds the optimal paths between the most central image and every other image. In this paper, we use a graph-based approach to perform initialisation, which is then refined with an iterative approach. Pairwise registration is performed using demons registration, then shortest paths identified in the resulting graph are used to provide an initial warp for each image by concatenating warps along the path. The warps are refined using an iterative Levenberg-Marquardt minimisation to the mean, based on updating the locations of a small number of points and incorporating a stiffness constraint. This optimisation approach is efficient, has very few free parameters to tune and we show how to tune the few remaining parameters. We compare the combined approach to both the iterative and graph-based approaches used independently. Results demonstrate that the combined method improves the alignment of various datasets, including two face datasets and a difficult medical dataset of prostate MRI images.