Optimal Extraction Method of Feature Points in Key Frame Image of Mobile Network Animation

被引:2
|
作者
Yin, Tao [1 ]
Lv, Zhihan [2 ]
机构
[1] Xihua Univ, Sch Fine Arts & Design, Chengdu, Peoples R China
[2] Uppsala Univ, Dept Game Design, Uppsala, Sweden
来源
MOBILE NETWORKS & APPLICATIONS | 2022年 / 27卷 / 06期
关键词
Mobile network animation; Keyframe image; Feature point extraction; Optimization;
D O I
10.1007/s11036-022-02070-x
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
In order to effectively extract the feature points of mobile network animation images and accurately reflect the main content of the video, an optimization method to extract the feature points of key frame images of mobile network animation is proposed. Firstly, the key frames are selected according to the content change degree of the animation video. The scale invariant feature transformation algorithm is used to describe the feature points of the key frame image of the animation video. The local feature points of the image are estimated by the constraint optimization method to realize the optimization extraction of the feature points of the key frame image of the mobile network animation. The efficiency of feature points extraction is analyzed from the number and effectiveness of feature points extraction, time-consuming and similarity invariance of feature points. The experimental results show that the proposed method has excellent adaptability, and can effectively extract feature points of mobile network animation image.
引用
收藏
页码:2515 / 2523
页数:9
相关论文
共 50 条
  • [31] Method for extraction of a characteristic feature of a contour image
    Niedziela, T
    Stankiewicz, A
    Szymcak, R
    Swietochowski, M
    [J]. OPTICA APPLICATA, 1999, 29 (04) : 573 - 581
  • [32] Method in image's feature extraction and matching
    School of Automation Science and Electrical Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100083, China
    [J]. Beijing Hangkong Hangtian Daxue Xuebao, 2008, 5 (516-519): : 516 - 519
  • [33] New image color feature extraction method
    Qiu, Zhao-Wen
    Zhang, Tian-Wen
    [J]. Harbin Gongye Daxue Xuebao/Journal of Harbin Institute of Technology, 2004, 36 (12): : 1699 - 1701
  • [34] Low exposure image frame generation algorithms for feature extraction and classification
    Vemuru, Krishnamurthy V.
    Clark, Jeffrey D.
    [J]. REAL-TIME IMAGE PROCESSING AND DEEP LEARNING 2019, 2019, 10996
  • [35] Fast and robust key frame extraction method for gesture video based on high-level feature representation
    Yang, Huimin
    Tian, Qiuhong
    Zhuang, Qiaoli
    Li, Linye
    Liang, Qinglong
    [J]. SIGNAL IMAGE AND VIDEO PROCESSING, 2021, 15 (03) : 617 - 626
  • [36] Fast and robust key frame extraction method for gesture video based on high-level feature representation
    Huimin Yang
    Qiuhong Tian
    Qiaoli Zhuang
    Linye Li
    Qinglong Liang
    [J]. Signal, Image and Video Processing, 2021, 15 : 617 - 626
  • [37] Feature Extraction Processing Method of Medical Image Fusion Based on Neural Network Algorithm
    Song, Tianming
    Yu, Xiaoyang
    Yu, Shuang
    Ren, Zhe
    Qu, Yawei
    [J]. COMPLEXITY, 2021, 2021
  • [38] DDAC: a feature extraction method for model of image steganalysis based on convolutional neural network
    Wang X.
    Li J.
    Song Y.
    [J]. Tongxin Xuebao/Journal on Communications, 2022, 43 (05): : 68 - 81
  • [39] Human action classification using adaptive key frame interval for feature extraction
    Lertniphonphan, Kanokphan
    Aramvith, Supavadee
    Chalidabhongse, Thanarat H.
    [J]. JOURNAL OF ELECTRONIC IMAGING, 2016, 25 (01)
  • [40] Unsupervised Feature Based Key-frame Extraction Towards Face Recognition
    Selvaganesan, Jana
    Natarajan, Kannan
    [J]. INTERNATIONAL ARAB JOURNAL OF INFORMATION TECHNOLOGY, 2016, 13 (6A) : 777 - 783