| DOI | Resolve DOI: https://doi.org/10.15221/19.074 |
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| Author | Search for: Shu, Chang1; Search for: Xi, Pengcheng1; Search for: Keefe, Allan |
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| Affiliation | - National Research Council of Canada. Digital Technologies
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| Format | Text, Article |
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| Conference | 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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| Abstract | 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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| Publication date | 2019-10-22 |
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| Publisher | Hometrica Consulting |
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| Place | Ascona, Switzerland |
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| In | |
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| Language | English |
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| Peer reviewed | Yes |
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| Export citation | Export as RIS |
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| Report a correction | Report a correction (opens in a new tab) |
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| Record identifier | 6b345d7b-bbb7-403c-b92e-35d662bfdfbd |
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| Record created | 2021-08-23 |
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| Record modified | 2021-08-24 |
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