Browsing by Author "Nonato, Luis"

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    Combinatorial Laplacian image cloning
    (Scopus, 2011) Cuadros Vargas, Alex Jesús; Nonato, Luis; Pascucci, Valerio
    Seamless image cloning has become one of the most important editing operation for photomontage. Recent coordinate-based methods have lessened considerably the computational cost of image cloning, thus enabling interactive applications. However, those techniques still bear severe limitations as to concavities and dynamic shape deformation. In this paper we present novel methodology for image cloning that turns out to be highly efficient in terms of computational times while still being more flexible than existing techniques. Our approach builds on combinatorial Laplacian and fast Cholesky factorization to ensure interactive image manipulation, handling holes, concavities, and dynamic deformations during the cloning process. The provided experimental results show that the proposed technique outperforms existing methods in requisites such as accuracy and flexibility. © 2011 IEEE.
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    iStar (i*): An interactive star coordinates approach for high-dimensional data exploration
    (Elsevier Ltd, 2016) Garcia Zanabria, Germain; Nonato, Luis; Gómez Nieto, Erick Mauricio
    Star Coordinates is an important visualization method able to reveal patterns and groups from multidimensional data while still showing the impact of data attributes in the formation of such patterns and groups. Despite its usefulness, Star Coordinates bears limitations that impair its use in several scenarios. For instance, when the number of data dimensions is high, the resulting visualization becomes cluttered, hampering the joint analysis of attribute importance and group/pattern formation. In this paper, we propose a novel method that renders Star Coordinates a feasible alternative to analyze high-dimensional data. The proposed method relies on a clustering mechanism to group attributes in order to mitigate visual clutter. Clustering can be performed automatically as well as interactively, allowing the analysis of how particular groups of attributes impact on the radial layout, thus assisting users in the understanding of data. The effectiveness of our approach is shown through a set of experiments and case studies, which attest its usefulness in practical applications. © 2016 Elsevier Ltd