@inproceedings{067ead22621648fdb38e861cf3ad6311,
title = "A New Female Body Segmentation and Feature Localisation Method for Image-Based Anthropometry",
abstract = "An increasingly growing demand on the bespoke service for buying clothes online presents a new challenge of how to efficiently and precisely acquire anthropometric data of distant customers. The conventional 2D anthropometric methods are efficient but face a problem of imperfect body segmentation because they cannot automatically deal with arbitrary background. To address this problem this paper aimed at female anthropometry proposes to segment the female body out of an orthogonal photo pair with deep learning, and to extract a group of body feature points according to curvature and bending direction of the segmented body contour. With the located feature points we estimate six body parameters with two existing mathematical models and assess their pros and cons in this paper.",
keywords = "Anthropometric methods, Deep learning, Feature points",
author = "Dan Wang and Yun Sheng and Zhang, \{Gui Xu\}",
note = "Publisher Copyright: {\textcopyright} 2019, Springer Nature Switzerland AG.; 25th International Conference on MultiMedia Modeling, MMM 2019 ; Conference date: 08-01-2019 Through 11-01-2019",
year = "2019",
doi = "10.1007/978-3-030-05710-7\_47",
language = "英语",
isbn = "9783030057091",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "567--577",
editor = "Ioannis Kompatsiaris and Stefanos Vrochidis and Vasileios Mezaris and Wen-Huang Cheng and Benoit Huet and Cathal Gurrin",
booktitle = "MultiMedia Modeling - 25th International Conference, MMM 2019, Proceedings",
address = "德国",
}