| DOI | Trouver le DOI : https://doi.org/10.15221/19.074 |
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| Auteur | Rechercher : Shu, Chang1; Rechercher : Xi, Pengcheng1; Rechercher : Keefe, Allan |
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| Affiliation | - Conseil national de recherches Canada. Technologies numériques
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| Format | Texte, Article |
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| Conférence | 3DBODY.TECH 2019 - 10th International Conference and Exhibition on 3D Body Scanning and Processing Technologies, October 22-23, 2019, Lugano, Switzerland |
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| Résumé | Accurate localization of anthropometric landmarks is crucial for processing and analyzing 3-D anthropometric data. For example, landmarks are used to extract dimensional measurements from 3-D scans of human bodies. They can also be used to fit a template model to the scans such that a correspondence across the scans can be established. From this correspondence, we can perform statistical shape analysis to understand the variabilities of human shapes. In this paper, we propose a new method for localizing anthropometric landmarks using a combination of 3-D surface features and the latest deep learning techniques. The method makes use of geometric features represented as descriptor vectors. We first identify a set of locations that exhibit salient geometric features. Then we use pre-registered 3-D models to train a classifier for each geometric landmark. With the geometric landmarks, we fit a template to the data scan. The full set of anthropometric landmarks can be predicted from the template-fitted model. We validate our method using the 2012 Canadian Forces Anthropometric Survey (CFAS) dataset where 2,200 full-body scans were collected. |
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| Date de publication | 2019-10-22 |
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| Maison d’édition | Hometrica Consulting |
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| Emplacement | Ascona, Switzerland |
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| Dans | |
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| Langue | anglais |
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| Publications évaluées par des pairs | Oui |
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| Exporter la notice | Exporter en format RIS |
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| Signaler une correction | Signaler une correction (s'ouvre dans un nouvel onglet) |
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| Identificateur de l’enregistrement | 6b345d7b-bbb7-403c-b92e-35d662bfdfbd |
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| Enregistrement créé | 2021-08-23 |
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| Enregistrement modifié | 2021-08-24 |
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