Research Article
Parallax information fusion-based for dance moving image posture extraction
@ARTICLE{10.4108/eai.16-12-2021.172437, author={Yin Lyu and Lin Teng}, title={Parallax information fusion-based for dance moving image posture extraction}, journal={EAI Endorsed Transactions on Scalable Information Systems}, volume={9}, number={36}, publisher={EAI}, journal_a={SIS}, year={2021}, month={12}, keywords={dance motion image, parallax information fusion, sequence image contour}, doi={10.4108/eai.16-12-2021.172437} }
- Yin Lyu
Lin Teng
Year: 2021
Parallax information fusion-based for dance moving image posture extraction
SIS
EAI
DOI: 10.4108/eai.16-12-2021.172437
Abstract
This article has been retracted, and the retraction notice can be found here: http://dx.doi.org/10.4108/eai.8-4-2022.173799. The existing motion image posture contour extraction results have low definition and serious detail loss. To solve this problem, we propose a novel dance moving image posture extraction method based on parallax information fusion. Firstly, the image with motion information is statistically analyzed by using the information fusion process to determine the position of the motion region. After the noise is reduced by morphological processing, the initial motion posture profile is obtained. The parallax between different control points and the center is used as the active contour model to shape the contraction force and expansion force, which can effectively assist the initial edge contour curve to gradually approach the real edge contour. Finally, the contour of the current moving image is extracted from the sequence image contour to obtain the attitude contour of the moving image. The experimental results show that the proposed method can extract the contour of the moving image clearly with less detail loss, which proves that the proposed method has strong practical performance and can effectively find the contour of the moving object.
Copyright © 2021 Yin Lyu et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution license, which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.