Artificial Intelligence Aerobics Action Image Simulation Based on the Image Segmentation Algorithm

被引:0
|
作者
Jiang, Tao [1 ]
机构
[1] Hebei Normal Univ, Sch Phys Educ, Shijiazhuang 050000, Hebei, Peoples R China
关键词
Artificial intelligence;
D O I
10.1155/2022/7438159
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
At present, aerobics is becoming a popular fashion with the continuous development of cultural needs. Because aerobics has the characteristics of many movements, rapid changes, strong complexity, and difficult performance of difficult movements, the current aerobics teaching still presents shortcomings such as low teaching level, limited teachers' resources, and energy. Therefore, it is difficult to effectively meet the actual learning needs of students. Based on this point, artificial intelligence can be used to simulate and guide the technical movements of aerobics to effectively teach students. In this paper, an artificial intelligence aerobics image simulation system is researched and developed and the GrabCut image segmentation algorithm is mainly used. After analyzing some shortcomings of the algorithm, the GrabCut algorithm cascade and graph-based are selected to complete the optimization, so as to lay a good system foundation and then build the aerobics artificial intelligence image simulation system according to the algorithm foundation. Finally, it analyzes and researches the actual problems of aerobics teaching activities in colleges and universities and focuses on the problems, achievements, and personal satisfaction of students who use the system in actual learning, which proves that the system can effectively assist aerobics teaching activities. By studying the image segmentation algorithm and artificial intelligence technology, this paper applies it to the field of aerobics action image simulation, so as to promote its technological development.
引用
收藏
页数:11
相关论文
共 50 条
  • [41] Algorithm for segmentation of texture image based on image variogram function
    Wu, G.
    Yang, J.A.
    Wang, H.Y.
    Tien Tzu Hsueh Pao/Acta Electronica Sinica, 2001, 29 (01): : 44 - 47
  • [42] Multilevel thresholding selection based on the artificial bee colony algorithm for image segmentation
    Horng, Ming-Huwi
    EXPERT SYSTEMS WITH APPLICATIONS, 2011, 38 (11) : 13785 - 13791
  • [43] Artificial Intelligence Image Recognition Method Based on Convolutional Neural Network Algorithm
    Tian, Youhui
    IEEE ACCESS, 2020, 8 : 125731 - 125744
  • [44] Design of computer image automatic processing system based on artificial intelligence algorithm
    You G.
    International Journal of Manufacturing Technology and Management, 2024, 38 (4-5) : 321 - 341
  • [45] Research on QR image code recognition system based on artificial intelligence algorithm
    Huo, Lina
    Zhu, Jianxing
    Singh, Pradeep Kumar
    Pavlovich, Pljonkin Anton
    JOURNAL OF INTELLIGENT SYSTEMS, 2021, 30 (01) : 855 - 867
  • [46] Automatic Color Extraction Algorithm of Graphic Design Image Based on Artificial Intelligence
    Zhao Q.
    Zhang H.
    International Journal of Circuits, Systems and Signal Processing, 2022, 16 : 374 - 384
  • [47] Intelligence Digital Image Watermark Algorithm Based on Artificial Neural Networks Classifier
    Jin, Cong
    Jin, Shu-Wei
    MODERN TRENDS AND TECHNIQUES IN COMPUTER SCIENCE (CSOC 2014), 2014, 285 : 3 - 16
  • [48] RETRACTED: Image Recognition and Simulation Based on Distributed Artificial Intelligence (Retracted Article)
    Fan, Tao
    COMPLEXITY, 2021, 2021
  • [49] A Retinal Image Segmentation Algorithm Based on Threshold Segmentation
    Zhang, Hong-qiang
    Wang, Shu-wen
    Ma, Cong
    Pi, Bing-kun
    COMPUTER SCIENCE AND TECHNOLOGY (CST2016), 2017, : 733 - 742
  • [50] Artificial intelligence based image recognition system
    Paul M.
    Vivek K.
    Jo Joseph P.
    Sharanjith V.P.
    Malik S.
    Rajeev S.
    Materials Today: Proceedings, 2023, 72 : 3222 - 3227