Title: A method of athlete's starting image posture correction based on deformation model and image restoration

Authors: Xingbo Zhou; Yong Yang

Addresses: Department of Physical Education, Zhangjiakou University, Zhangjiakou 075000, China ' Department of Physical Education, Zhangjiakou University, Zhangjiakou 075000, China

Abstract: In order to improve the similarity between the starting pose and the actual structure and the signal to noise ratio (SNR) of the image, this paper proposed a new approach to correct the starting pose of the athlete based on deformation model and image repair. Firstly, dual Kinect sensors were used to collect the starting posture data of athletes and construct the three-dimensional image of the starting posture of athletes. Secondly, the method of attitude 3D image segmentation based on self-segmentation theory is used to obtain the attitude feature artefact region. Finally, after the artefact is repaired, the image attitude correction method based on B-spline deformation model completes the attitude correction. The test results show that the similarity between the image pose structure and the actual structure is as high as 0.98 and the minimum peak signal-to-noise ratio is 0.93 dB.

Keywords: deformation model; image repair; athletes; start image; attitude correction; three-dimensional reconstruction.

DOI: 10.1504/IJCISTUDIES.2023.132485

International Journal of Computational Intelligence Studies, 2023 Vol.12 No.1/2, pp.15 - 32

Received: 20 Sep 2022
Accepted: 15 Nov 2022
Published online: 24 Jul 2023 *

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