Search Results

Now showing 1 - 10 of 23
  • Item
    Virtual Reality: A Literature Review and Metrics-based Classification
    (The Eurographics Association, 2018) Ankomah, Peter; Vangorp, Peter; {Tam, Gary K. L. and Vidal, Franck
    This paper presents a multi-disciplinary overview of research evaluating virtual reality (VR). The main aim is to review and classify VR research based on several metrics: presence and immersion, navigation and interaction, knowledge improvement, performance and usability. With the continuous development and consumerisation of VR, several application domains have studied the impact of VR as an enhanced alternative environment for performing tasks. However, VR experiment results often cannot be generalised but require specific datasets and tasks suited to each domain. This review and classification of VR metrics presents an alternative metrics-based view of VR experiments and research.
  • Item
    Screen Space Particle Selection
    (The Eurographics Association, 2018) Köster, Marcel; Krüger, Antonio; {Tam, Gary K. L. and Vidal, Franck
    Analyses of large 3D particle datasets typically involve many different exploration and visualization steps. Interactive exploration techniques are essential to reveal and select interesting subsets like clusters or other sophisticated structures. State-of-the-art techniques allow for context-aware selections that can be refined dynamically. However, these techniques require large amounts of memory and have high computational complexity which heavily limits their applicability to large datasets. We propose a novel, massively parallel particle selection method that is easy to implement and has a processing complexity of O(n*k) (where n is the number of particles and k the maximum number of neighbors per particle) and requires only O(n) memory. Furthermore, our algorithm is designed for GPUs and performs a selection step in several milliseconds while still being able to achieve high-quality results.
  • Item
    Evolutionary Interactive Analysis of MRI Gastric Images Using a Multiobjective Cooperative-coevolution Scheme
    (The Eurographics Association, 2018) Al-Maliki, Shatha F.; Lutton, Évelyne; Boué, François; Vidal, Franck; {Tam, Gary K. L. and Vidal, Franck
    In this study, we combine computer vision and visualisation/data exploration to analyse magnetic resonance imaging (MRI) data and detect garden peas inside the stomach. It is a preliminary objective of a larger project that aims to understand the kinetics of gastric emptying. We propose to perform the image analysis task as a multi-objective optimisation. A set of 7 equally important objectives are proposed to characterise peas. We rely on a cooperation co-evolution algorithm called 'Fly Algorithm' implemented using NSGA-II. The Fly Algorithm is a specific case of the 'Parisian Approach' where the solution of an optimisation problem is represented as a set of individuals (e.g. the whole population) instead of a single individual (the best one) as in typical evolutionary algorithms (EAs). NSGA-II is a popular EA used to solve multi-objective optimisation problems. The output of the optimisation is a succession of datasets that progressively approximate the Pareto front, which needs to be understood and explored by the end-user. Using interactive Information Visualisation (InfoVis) and clustering techniques, peas are then semi-automatically segmented.
  • Item
    When Size Matters: Towards Evaluating Perceivability of Choropleths
    (The Eurographics Association, 2018) McNabb, Liam; Laramee, Robert S.; Wilson, Max; {Tam, Gary K. L. and Vidal, Franck
    Choropleth maps are an invaluable visualization type for mapping geo-spatial data. One advantage to a choropleth map over other geospatial visualizations such as cartograms is the familiarity of a non-distorted landmass. However, this causes challenges when an area becomes too small in order to accurately perceive the underlying color. When does size matter in a choropleth map? We conduct an experiment to verify the relationship between choropleth maps, their underlying color map, and a user's perceivability. We do this by testing a user's perception of color relative to an administrative area's size within a choropleth map, as well as user-preference of fixed-locale maps with enforced minimum areas. Based on this initial experiment we can make the first recommendations with respect to a unit area's minimum size in order to be perceivably useful.
  • Item
    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.
  • Item
    Cartograms with Topological Features
    (The Eurographics Association, 2018) Tong, Chao; McNabb, Liam; Laramee, Robert S.; {Tam, Gary K. L. and Vidal, Franck
    Cartograms are a popular and useful technique for depicting geo-spatial data. Dorling style and rectangular cartograms are very good for facilitating comparisons between unit areas. Each unit area is represented by the same shape such as a circle or rectangle, and the uniformity in shapes facilitates comparative judgment. However, the layout of these more abstract shapes may also simultaneously reduce the map's legibility and increase error. When we integrate univariate data into a cartogram, the recognizability of cartogram may be reduced. There is a trade-off between information recognition and geo-information accuracy. This is the inspiration behind the work we present. We attempt to increase the map's recognizability and reduce error by introducing topological features into the cartographic map. Our goal is to include topological features such as a river in a Dorling-style or rectangular cartogram to make the visual layout more recognizable, increase map cognition and reduce geospatial error. We believe that compared to the standard Dorling and rectangular style cartogram, adding topological features provides familiar geo-spatial cues and flexibility to enhance the recognizability of a cartogram.
  • Item
    GPU-Assisted Scatterplots for Millions of Call Events
    (The Eurographics Association, 2018) Rees, Dylan; Roberts, Richard C.; Laramee, Robert S.; Brookes, Paul; D'Cruze, Tony; Smith, Gary A.; {Tam, Gary K. L. and Vidal, Franck
    With four percent of the working population employed in call centers in both the United States and the UK, the contact center industry represents a sizable proportion of modern industrial landscapes. As with most modern industries, data collection is de rigueur, producing gigabytes of call records that require analysis. The scatterplot is a well established and understood form of data visualization dating back to the 17th century. In this paper we present an application for visualizing large call centre data sets using hardware-accelerated scatterplots. The application utilizes a commodity graphics card to enable visualization of a month's worth of data, enabling fast filtering of multiple attributes. Filtering is implemented using the Open Computing Language (OpenCL), providing significant performance improvement over traditional methods. We demonstrate the value of our application for exploration and analysis of millions of call events from a real-world industry partner. Domain expert feedback from our industrial partners is reported.
  • Item
    Towards a Survey of Interactive Visualization for Education
    (The Eurographics Association, 2018) Fırat, Elif E.; Laramee, Robert S.; {Tam, Gary K. L. and Vidal, Franck
    Graphic design and visualization are becoming fundamental components of education. The use of advanced visual design in pedagogy is growing and evolving rapidly. One of their aims is to enhance the educational process by facilitating better understanding of the subject with the use of graphical representation methods. Research papers in this field offer important opportunities to examine previously completed experiments and extract useful educational outcomes. This paper analyzes and classifies pedagogical visualization research papers to increase understanding in this area. To our knowledge, this is the first (work-in-progress) survey paper on advanced visualization for education. We categorize related research papers into original subject groups that enable researchers to compare related literature. Our novel classification enables researchers to find both mature and unexplored directions which can inform directions for future work. This paper serves as a valuable resource for both beginners and experienced researchers who are interested in interactive visualization for education.
  • Item
    Combining Accumulated Frame Differencing and Corner Detection for Motion Detection
    (The Eurographics Association, 2018) Algethami, Nahlah; Redfern, Sam; {Tam, Gary K. L. and Vidal, Franck
    Detecting and tracking people in a meeting room is very important for many applications. In order to detect people in a meeting room with no prior knowledge (e.g. background model) and regardless of whether their motion is slow or significant, this paper proposes a coarse-to-fine people detection algorithm by combining a novel motion detection process, namely, adaptive accumulated frame differencing (AAFD) combined with corner features. Firstly, the region of movement is extracted adaptively using AAFD, then motion corner features are extracted. Finally, the minimum area rectangle fitting these corners is found. The proposed algorithm is evaluated using the AMI meeting data set and this indicates promising results for people detection.
  • Item
    Knowledge-based Discovery of Transportation Object Properties by Fusing Multi-modal GIS Data
    (The Eurographics Association, 2018) Maroun, Pedro Eid; Mudur, Sudhir; Popa, Tiberiu; {Tam, Gary K. L. and Vidal, Franck
    3D models of transportation objects like a road, bridge, underpass, etc. are required in many domains including military training, land development, etc. While remote sensed images and LiDaR data can be used to create approximate 3D representations, detailed 3D representations are difficult to create automatically. Instead, interactive tools are used with rather laborious effort. For example, the top commercial interactive model generator we tried required 94 parameters in all for different bridge types. In this paper, we take a different path.We automatically derive these parameter values from GIS (Geographic Information Systems) data, which normally contains detailed information of these objects, but often only implicitly. The framework presented here transforms GIS data into a knowledge base consisting of assertions. Spatial/numeric relations are handled through plug-ins called property extractors whose results get added to the knowledge base, used by a reasoning engine to infer object properties. A number of properties have to be extracted from images, and are dependent on the accuracy of computer vision methods. While a comprehensive property extractor mechanism is work in progress, . a prototype implementation illustrates our framework for bridges with GIS data from the real world. To the best of our knowledge, our framework is the first to integrate knowledge inference and uncertainty for extracting landscape object properties by fusing facts from multi-modal GIS data sources.