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 "Computer vision"

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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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    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.