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Wang, D., Li, Z., Dey, N., Ashour, A. S., Moraru, L., Sherratt, S. 
ORCID: https://orcid.org/0000-0001-7899-4445 and Shi, F.
(2020)
Deep-segmentation of plantar pressure images incorporating fully convolutional neural networks.
Biocybernetics and Biomedical Engineering, 40 (1).
pp. 546-558.
ISSN 0208-5216
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To link to this item DOI: 10.1016/j.bbe.2020.01.004
Abstract/Summary
Comfort shoe-last design relies on the key points of last curvature. Traditional plantar pressure image segmentation methods are limited to their local and global minimization issues. In this work, an improved fully convolutional networks (FCN) employing SegNet (SegNet+FCN 8s) is proposed. The algorithm design and operation are performed using the visual geometry group (VGG). The method has high efficiency for the segmentation in positive indices of global accuracy (0.8105), average accuracy (0.8015), and negative indices of average cross-ratio (0.6110) and boundary F1 index (0.6200). The research has potential applications in improving the comfort of shoes.
| Item Type: | Article |
|---|---|
| Refereed: | Yes |
| Divisions: | Life Sciences > School of Biological Sciences > Biomedical Sciences Life Sciences > School of Biological Sciences > Department of Bio-Engineering |
| ID Code: | 88481 |
| Publisher: | Elsevier |
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