{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,16]],"date-time":"2025-12-16T12:35:21Z","timestamp":1765888521354,"version":"3.41.2"},"reference-count":21,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2021,1,19]],"date-time":"2021-01-19T00:00:00Z","timestamp":1611014400000},"content-version":"vor","delay-in-days":18,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Guangxi Higher Education Undergraduate Teaching Reform Project","award":["2019JGA174","15XMZ063"],"award-info":[{"award-number":["2019JGA174","15XMZ063"]}]},{"name":"National Social Science Foundation of China","award":["2019JGA174","15XMZ063"],"award-info":[{"award-number":["2019JGA174","15XMZ063"]}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Wireless Communications and Mobile Computing"],"published-print":{"date-parts":[[2021,1]]},"abstract":"<jats:p>In order to improve the training level of Sanda movement, this article uses an image analysis method to reconstruct the detailed characteristics of the movement and apply them to the actual training process. Since the traditional wavelet reconstruction method is affected by the accuracy of the decomposition scale, this paper proposes an improved method of Sanda action based on 3D image reconstruction. First, the method relies on frame adjacent phase compensation and digital image stabilization techniques to perform digital frame operations on the image. Then, scanning and corner detection are used for image reconstruction, where adjacent phase compensation methods are used to match feature points and gray pixels. Image extraction is performed by extracting key feature points of the action 3D image, and a fast frame detection method is used to stabilize the image of the digital image, thereby improving the image quality of image reconstruction. The experimental results show that the method has good image output effect and has a high application value in Sanda guidance and optimization.<\/jats:p>","DOI":"10.1155\/2021\/6668612","type":"journal-article","created":{"date-parts":[[2021,1,20]],"date-time":"2021-01-20T03:50:11Z","timestamp":1611114611000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Design of Sanda Action Reconstruction Model Based on 3D Images"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8735-2572","authenticated-orcid":false,"given":"Hongwei","family":"Zhao","sequence":"first","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2021,1,19]]},"reference":[{"key":"e_1_2_9_1_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2015.2439257"},{"volume-title":"A Framework and Modeling Method of Data-Driven Soft Sensors Based on Semisupervised Gaussian Regression","year":"2016","author":"Weiwu Y.","key":"e_1_2_9_2_2"},{"key":"e_1_2_9_3_2","first-page":"63","article-title":"Large data clustering algorithm based on particle swarm differential perturbation optimization[J]","volume":"28","author":"Jie M. 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