Browsing by Author "Patanè, Giuseppe"

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    Comparison and Integration of Erosion Evaluation Methods for Rheumatic Degenerative Diseases
    (The Eurographics Association, 2020) Paccini, Martina; Patanè, Giuseppe; Spagnuolo, Michela; Biasotti, Silvia and Pintus, Ruggero and Berretti, Stefano
    Monitoring the development of degenerative rheumatic diseases is at the core of an efficient medical evaluation of the patient. Acquiring information on the pathology progression, indeed, helps to personalize the therapy in order to slow the pathology degeneration. Follow-up exams allow medical doctors to evaluate the situation of the patient over time. Through medical imaging scans, these exams help to identify erosion processes, which are typical indicators of a rheumatic illness. The paper presents a comparison between different methods aiming to identify erosion sites in follow-up exams. In particular, geometricbased and texture-based approaches are compared in terms of extracted information and achieved results. Finally, these two approaches are integrated in order to achieve a more complete analysis of the input anatomical district and of the underlying pathology.
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    EUROGRAPHICS 2022: CGF 41-2 STARs Frontmatter
    (The Eurographics Association and John Wiley & Sons Ltd., 2022) Meneveaux, Daniel; Patanè, Giuseppe; Meneveaux, Daniel; Patanè, Giuseppe
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    Kernel-Based Sampling of Arbitrary Data
    (The Eurographics Association, 2020) Cammarasana, Simone; Patanè, Giuseppe; Biasotti, Silvia and Pintus, Ruggero and Berretti, Stefano
    Point sampling is widely used in several Computer Graphics' applications, such as point-based modelling and rendering, image and geometric processing. Starting from the kernel-based sampling of signals defined on a regular grid, which generates adaptive distributions of samples with blue-noise property, we specialise this sampling to arbitrary data in terms of dimension and structure, such as signals, vector fields, curves, and surfaces. To demonstrate the novelties and benefits of the proposed approach, we discuss its applications to the resampling of 2D/3D domains according to the distribution of physical quantities computed as solutions to PDEs, and to the sampling of vector fields, 2D curves and 3D point sets. According to our experiments, the proposed sampling achieves a high approximation accuracy, preserves the features of the input data, and is computationally efficient.
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    Wavelet‐based Heat Kernel Derivatives: Towards Informative Localized Shape Analysis
    (© 2021 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd, 2021) Kirgo, Maxime; Melzi, Simone; Patanè, Giuseppe; Rodolà, Emanuele; Ovsjanikov, Maks; Benes, Bedrich and Hauser, Helwig
    In this paper, we propose a new construction for the Mexican hat wavelets on shapes with applications to partial shape matching. Our approach takes its main inspiration from the well‐established methodology of diffusion wavelets. This novel construction allows us to rapidly compute a multi‐scale family of Mexican hat wavelet functions, by approximating the derivative of the heat kernel. We demonstrate that this leads to a family of functions that inherit many attractive properties of the heat kernel (e.g. local support, ability to recover isometries from a single point, efficient computation). Due to its natural ability to encode high‐frequency details on a shape, the proposed method reconstructs and transfers ‐functions more accurately than the Laplace‐Beltrami eigenfunction basis and other related bases. Finally, we apply our method to the challenging problems of partial and large‐scale shape matching. An extensive comparison to the state‐of‐the‐art shows that it is comparable in performance, while both simpler and much faster than competing approaches.