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 "Graphics systems and interfaces"

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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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    Image Based Proximate Shadow Retargeting
    (The Eurographics Association, 2018) Casas, Llogari; Fauconneau, Matthias; Kosek, Maggie; Mclister, Kieran; Mitchell, Kenny; {Tam, Gary K. L. and Vidal, Franck
    We introduce Shadow Retargeting which maps real shadow appearance to virtual shadows given a corresponding deformation of scene geometry, such that appearance is seamlessly maintained. By performing virtual shadow reconstruction from un-occluded real shadow samples observed in the camera frame, we recover the deformed shadow appearance efficiently. Our method uses geometry priors for the shadow casting object and a planar receiver surface. Inspired by image retargeting approaches [VTP10] we describe a novel local search strategy, steered by importance based deformed shadow estimation. Results are presented on a range of objects, deformations and illumination conditions in real-time Augmented Reality (AR) on a mobile device. We demonstrate the practical application of the method in generating otherwise laborious in-betweening frames for 3D printed stop motion animation.