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A New Female Body Segmentation and Feature Localisation Method for Image-Based Anthropometry

  • Dan Wang
  • Yun Sheng
  • GuiXu Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11295)

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 

Notes

Acknowledgements

This work was supported by the Open Research Fund of Shanghai Key Laboratory of Multidimensional Information Processing, East China Normal University.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Shanghai Key Laboratory of Multidimensional Information ProcessingEast China Normal UniversityShanghaiPeople’s Republic of China
  2. 2.The Department of Computer Science and TechnologyEast China Normal UniversityShanghaiPeople’s Republic of China

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