Italian Chapter Conference 2019 - Smart Tools and Apps in computer Graphics
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Browsing Italian Chapter Conference 2019 - Smart Tools and Apps in computer Graphics by Author "Biasotti, Silvia"
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Item 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, RuggeroThis 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.Item HT-based Recognition of Patterns on 3D Shapes Using a Dictionary of Mathematical Curves(The Eurographics Association, 2019) Romanengo, Chiara; Biasotti, Silvia; FALCIDIENO, BIANCA; Agus, Marco and Corsini, Massimiliano and Pintus, RuggeroCharacteristic curves play a fundamental role in the way a shape is perceived and illustrated. To address the curve recognition problem on surfaces, we adopt a generalisation of the Hough Transform (HT) which is able to deal with mathematical curves. In particular, we extend the set of curves so far adopted for curve recognition with the HT and propose a new dictionary of curves to be selected as templates. In addition, we introduce rules of composition and aggregation of curves into patterns, not limiting the recognition to a single curve at a time. Our method recognises various curves and patterns, possibly compound on a 3D surface. It selects the most suitable profile in a family of curves and, deriving from the HT, it is robust to noise and able to deal with data incompleteness. The system we have implemented is open and allows new additions of curves in the dictionary of functions already available.