Accurate Recognition of Motion Patterns Based on Artificial Visual Neural Network

被引:0
|
作者
Li M. [1 ]
机构
[1] Hubei University of Technology, Wuhan
来源
Advances in Multimedia | 2022年 / 2022卷
关键词
D O I
10.1155/2022/4321750
中图分类号
学科分类号
摘要
In order to improve the accuracy of motion pattern recognition, this paper combines the artificial visual neural network to construct a motion pattern recognition system. Moreover, this paper discusses the psychological perception properties of human eyes to color stimuli and gives a description of the observation field of view where the color stimuli are located. At the same time, this paper analyzes the phenomenon of color adaptation and provides a method of color appearance matching through modeling to achieve color appearance matching under variable observation conditions. Based on the chromatic adaptation transformation, a chromatic appearance model is given, which can predict the corresponding color and also predict the chromatic appearance properties of color stimuli under given observation conditions. In addition, this paper constructs an intelligent motion pattern recognition system combined with artificial visual neural network. The experimental results show that the motion pattern recognition system based on artificial visual neural network can accurately identify the motion pattern category. © 2022 Meiqi Li.
引用
收藏
相关论文
共 50 条
  • [41] Development an Accurate Neural Network for Coin Recognition
    Fonov, Nikolay
    Ksenia, Urkaeva
    PROCEEDINGS OF THE 2021 IEEE CONFERENCE OF RUSSIAN YOUNG RESEARCHERS IN ELECTRICAL AND ELECTRONIC ENGINEERING (ELCONRUS), 2021, : 337 - 341
  • [42] NEURAL NETWORK BASED RECOGNITION OF FLOW-INJECTION PATTERNS
    HARTNETT, M
    DIAMOND, D
    BARKER, PG
    ANALYST, 1993, 118 (04) : 347 - 354
  • [43] Human-visual-perception-like intensity recognition for color rust images based on artificial neural network
    Shen, Heng-Kuang
    Chen, Po-Han
    Chang, Luh-Maan
    AUTOMATION IN CONSTRUCTION, 2018, 90 : 178 - 187
  • [44] Recognition and tracking of weld line by visual sensing system using artificial neural network
    Suga, Yasuo
    Naruse, Masao
    Tokiwa, Takuya
    Nippon Kikai Gakkai Ronbunshu, C Hen/Transactions of the Japan Society of Mechanical Engineers, Part C, 1994, 60 (574): : 2178 - 2183
  • [45] Recognition Patterns Construction of Coronary Heart Disease Patients with Qi Deficiency Syndrome Based on Artificial Neural Network
    Shi, Qi
    Zhao, Huihui
    Chen, Jianxin
    Yang, Yi
    Zheng, Chenglong
    Wang, Wei
    BIOTECHNOLOGY, CHEMICAL AND MATERIALS ENGINEERING, PTS 1-3, 2012, 393-395 : 916 - 920
  • [46] EMG Signal Patterns Recognition based on Feedforward Artificial Neural Network Applied to Robotic Prosthesis Myoelectric Control
    Calderon-Cordova, Carlos
    Ramirez, Cristian
    Barros, Veronica
    Alejandro Quezada-Sarmiento, Pablo
    Barba-Guaman, Luis
    PROCEEDINGS OF 2016 FUTURE TECHNOLOGIES CONFERENCE (FTC), 2016, : 868 - 875
  • [47] Motion estimation based on optical flow and an artificial neural network (ANN)
    Jiafeng Zhang
    Feifei Zhang
    Masanori Ito
    Artificial Life and Robotics, 2009, 14 (4) : 502 - 505
  • [48] Motion estimation based on optical flow and an artificial neural network (ANN)
    Zhang, Jiafeng
    Zhang, Feifei
    Ito, Masanori
    ARTIFICIAL LIFE AND ROBOTICS, 2009, 14 (04) : 502 - 505
  • [49] Epileptic Seizure Motion Classification based on sEMG and Artificial Neural Network
    Djemal, Achraf
    Bouchaala, Dhouha
    Fakhfakh, Ahmed
    Kanoun, Olfa
    PROCEEDINGS OF INTERNATIONAL WORKSHOP ON IMPEDANCE SPECTROSCOPY (IWIS 2021), 2021, : 141 - 145
  • [50] Artificial neural network properties associated with wiring patterns in the visual projections of vertebrates and arthropods
    Tosh, Colin R.
    Ruxton, Graeme D.
    AMERICAN NATURALIST, 2006, 168 (02): : E38 - E52