A method of athlete's starting image posture correction based on deformation model and image restoration Online publication date: Mon, 24-Jul-2023
by Xingbo Zhou; Yong Yang
International Journal of Computational Intelligence Studies (IJCISTUDIES), Vol. 12, No. 1/2, 2023
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.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Computational Intelligence Studies (IJCISTUDIES):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook