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 "Computing methodologies"

Now showing 1 - 11 of 11
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    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.
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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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    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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    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.
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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.
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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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    Keys-to-Sim: Transferring Hand-Crafted Key-framed Animations to Simulated Figures using Wide Band Stochastic Trajectory Optimization
    (The Eurographics Association, 2018) Borer, Dominik; Guay, Martin; Sumner, Robert W.; {Tam, Gary K. L. and Vidal, Franck
    The vision of fully simulating characters and their environments has the potential to offer rich interactions between characters and objects in the virtual world. However, this introduces a challenging problem similar to controlling robotic figures: computing the necessary torques to perform a given task. In this paper, we address the problem of transferring hand-crafted kinematic motions to a fully simulated figure, by computing open-loop controls necessary to reproduce the target motion. One key ingredient to successful control is the mechanical feasibility of the target motion. While several methods have been successful at replicating human captured motion, there has not yet been a method capable of handling the case of artist-authored key-framed movements that can violate the laws of physics or go beyond the mechanical limits of the character. Due to the curse of dimensionality, sampling-based optimization methods typically restrict the search to a narrow band which limits exploration of feasible motions—resulting in a failure to reproduce the desired motion when a large deviation is required. In this paper, we solve this problem by combining a window-based breakdown of the controls on the temporal dimension, together with a global wide search strategy that keeps locally sub-optimal samples throughout the optimization.
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    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.
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    Physically-based Sticky Lips
    (The Eurographics Association, 2018) Leach, Matthew; Maddock, Steve; {Tam, Gary K. L. and Vidal, Franck
    Abstract In this paper, a novel solution is provided for the sticky lip problem in computer facial animation, recreating the way the lips stick together when drawn apart in speech or in the formation of facial expressions. Traditional approaches to modelling this rely on an artist estimating the correct behaviour. In contrast, this paper presents a physically-based model. The mouth is modelled using the total Lagrangian explicit dynamics finite element method, with a new breaking element modelling the saliva between the lips. With this approach, subtle yet complex behaviours are recreated implicitly, giving rise to more realistic movements of the lips. The model is capable of reproducing varying degrees of stickiness between the lips, as well as asymmetric effects.
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    Single ImageWatermark Retrieval from 3D Printed Surfaces via Convolutional Neural Networks
    (The Eurographics Association, 2018) Zhang, Xin; Wang, Qian; Ivrissimtzis, Ioannis; {Tam, Gary K. L. and Vidal, Franck
    In this paper we propose and analyse a method for watermarking 3D printed objects, concentrating on the watermark retrieval problem. The method embeds the watermark in a planar region of the 3D printed object in the form of small semi-spherical or cubic bumps arranged at the nodes of a regular grid. The watermark is extracted from a single image of the watermarked planar region through a Convolutional Neural Network. Experiments with 3D printed objects, produced by filaments of various colours, show that in most cases the retrieval method has a high accuracy rate.
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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.