Deep Learning and Sensing Technologies for Anthropometry
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".
Deadline for manuscript submissions: closed (11 November 2021) | Viewed by 6404
Special Issue Editors
Interests: machine learning; computer vision; 3D graphics; anthropometry
Special Issues, Collections and Topics in MDPI journals
Interests: biometrics; measurement; point cloud processing; deep learning; 3D body scanning
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear colleagues,
The extraction of accurate anthropometric data is important in many domains. E-commerce has become part of our lives, with an increasing number of consumers preferring to buy clothing online. Despite the large online transaction figures, it is still challenging to sell clothes online due to the high return rate, with most of the returns resulting from the poor estimation of appropriate cloth sizing.
In healthcare, in determining human body volume, the whole-body volume and segmental volumes are important indicators for monitoring body health. To estimate body volume, the traditional methods currently used in clinical settings are very cumbersome, especially for bedridden patients. In 3D graphics applications, human models can be generated using high-quality 3D scanners, based on which accurate anthropometric data can be extracted. However, such systems are generally very expensive and bulky, and require expert knowledge for operation. With the advent of lightweight 3D scanners on mobile devices, the generation of 3D human body models and extraction of anthropometric data based on such models will become affordable and widespread.
In past years, deep learning methods have revolutionized numerous domains, significantly changing processing paradigms in computer vision, natural language processing, and 3D processing, to name a few. Deep learning has also a major impact in anthropometry, which is of paramount importance in fashion industry, healthcare monitoring, and 3D graphics applications.
The objective of this special issue is to publish high-quality papers that address the challenging domain of non-contact anthropometry, with a particular focus on deep learning methods. We solicit original, full-length, unpublished contributions in this domain. Potential topics of interest include:
- Human body scanning;
- Sensors and systems for non-contact anthropometry;
- Deep-learning-based anthropometric measurements based on RGB or depth images;
- Deep learning for anthropometry based on point clouds;
- Cloth size prediction based on deep learning;
- Deep learning for anthropometric-based healthcare monitoring;
- Deep-learning-based body volume estimation;
- Three-dimensional human body shape reconstruction using deep learning.
Prof. Dr. Adrian Munteanu
Dr. Pengpeng Hu
Guest Editors
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Keywords
- anthropometry
- deep learning
- 3D human shape reconstruction
- healthcare monitoring
- cloth size prediction
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