Computer Graphics & Visual Computing (CGVC) 2018

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https://diglib.eg.org/handle/10.2312/2632659

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Browsing Computer Graphics & Visual Computing (CGVC) 2018 by Subject "Human"

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    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.
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    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.
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    RiverState: A Visual Metaphor Representing Millions of Time-Oriented State Transitions
    (The Eurographics Association, 2018) Roberts, Richard C.; Rees, Dylan; Laramee, Robert S.; Brookes, Paul; Smith, Gary A.; {Tam, Gary K. L. and Vidal, Franck
    Developing a positive relationship between a business and its customers is vital to success. The outcome of any customer interaction can determine future patronage of the business. Many industry's only point of interaction with their customers is through a contact centre where everything from sales to complaints are handled. This places tremendous importance on the operational efficiency of the contact centre and the level of care provided to the customers. These customer interactions are recorded and archived in large databases, but undertaking insightful analysis is challenging due to both the size and complexity of the data. We present a visual solution to the tracking of customer interactions at a large scale. RiverState visualises the collective flow of callers through the process of interacting with a contact centre using a river metaphor. We use finite state transition machines with customised edges to depict millions of events and the states callers go through to complete their journey. We implement a range of novel features to enhance the analytical qualities of the application, and collect feedback from domain experts to analyse and evaluate the use of the software.
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    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.
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    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.
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    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.