Browsing by Author "CATALANO, CHIARA EVA"

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    Challenges in the Digitisation of a High-reflective Artwork
    (The Eurographics Association, 2021) Catalano, Chiara Eva; Brunetto, Erika; Mortara, Michela; Pizzi, Corrado; Hulusic, Vedad and Chalmers, Alan
    In this paper we report about the photogrammetric acquisition and reconstruction of a contemporary artwork, performed by offthe- shelf software. The ceramic piece of art is "Il Libro d'Oro del Terzo Paradiso" ("The Golden Book of the Third Paradise") by Michelangelo Pistoletto, accessed and studied in the framework of a regional project. This artefact is particularly challenging. On the one hand, it is golden coated and, as such, highly reflective. Hence, images are likely to suffer from highlight spots, shadows or self-reflections, and the reconstructed point cloud is typically noisy. On the other hand, the object exhibits simple geometry, mainly composed of planar surfaces, and is highly symmetric; however, it possesses detail features and undercuts. The symmetric nature of the object and reflections misled the image alignment, and the noise in the data turned out to be of the same scale as the detail features. We will discuss all the steps of the process, aimed at obtaining a high quality and accurate 3D model using low-cost tools.
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    Feature-based Characterisation of Patient-specific 3D Anatomical Models
    (The Eurographics Association, 2019) Banerjee, Imon; Paccini, Martina; Ferrari, Enrico; CATALANO, CHIARA EVA; Biasotti, Silvia; Spagnuolo, Michela; Agus, Marco and Corsini, Massimiliano and Pintus, Ruggero
    This paper aims to examine the potential of 3D shape analysis integrated to machine learning techniques in supporting medical investigation. In particular, we introduce an approach specially designed for the characterisation of anatomical landmarks on patient-specific 3D carpal bone models represented as triangular meshes. Furthermore, to identify functional articulation regions, two novel district-based properties are defined. The performance of both state of the art and novel features has been evaluated in a machine learning setting to identify a set of significant anatomical landmarks on patient data. Experiments have been performed on a carpal dataset of 56 patient-specific 3D models that are segmented from T1 weighed magnetic resonance (MR) scans of healthy male subjects. Despite the typical large inter-patient shape variation within the training samples, our framework has achieved promising results.
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    Smart Tools and Applications in Graphics - Eurographics Italian Chapter Conference: Frontmatter
    (The Eurographics Association, 2022) Cabiddu, Daniela; Schneider, Teseo; Allegra, Dario; Catalano, Chiara Eva; Cherchi, Gianmarco; Scateni, Riccardo; Cabiddu, Daniela; Schneider, Teseo; Allegra, Dario; Catalano, Chiara Eva; Cherchi, Gianmarco; Scateni, Riccardo