A Method of Recursive Target Extraction Based on Multi-Level Features

被引:0
|
作者
Dong, H. Y. [1 ]
Zhao, P. [1 ]
Wang, X. W. [2 ]
机构
[1] Shenyang Ligong Univ, Sch Informat Sci & Engn, Shenyang 110159, Liaoning, Peoples R China
[2] Shenyang Ligong Univ, Sch Mech Engn, Shenyang 110159, Liaoning, Peoples R China
关键词
Multi-level features; Extraction of target; Recursive extraction; Dilate with conditions;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Due to the complexity and asymmetrical illumination, some images are difficult to be effectively segmented by some routine method. In this paper, an algorithm based on multi-level features is designed and proposed, and which can be used for target extraction from the images with more noises, interference, uneven illumination and changeable scene. The algorithm first transfers the original image into a gray one. And then features of every level the target are extracted inheritably from the high or low level feature message. Furthermore, it also can track back to the original image or the features of low level, and the extraction goes on by recursion. So the target can be separated from the background. The algorithm experiment results indicates the target can be correctly extracted with high-efficiency and great precision, and with different sizes of the target and SNR also.
引用
收藏
页码:690 / 693
页数:4
相关论文
共 50 条
  • [41] A recursive inference method based on invertible neural network for multi-level model updating using video monitoring data
    Zeng, Jice
    Todd, Michael D.
    Hu, Zhen
    MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2023, 203
  • [42] Target Broker Compression for Multi-Level Fusion
    Blasch, Erik
    Chen, Huamei
    Wang, Zhonghai
    Jia, Bin
    Liu, Kui
    Chen, Genshe
    Shen, Dan
    PROCEEDINGS OF THE 2016 IEEE NATIONAL AEROSPACE AND ELECTRONICS CONFERENCE (NAECON) AND OHIO INNOVATION SUMMIT (OIS), 2016, : 36 - 43
  • [43] Unsupervised classification for PolSAR images based on multi-level feature extraction
    Han, Ping
    Han, Binbin
    Lu, Xiaoguang
    Cong, Runmin
    Sun, Dandan
    INTERNATIONAL JOURNAL OF REMOTE SENSING, 2020, 41 (02) : 534 - 548
  • [44] SPL Features Quantification and Selection Based on Multiple Multi-Level Objectives
    Khan, Fazal Qudus
    Musa, Shahrulniza
    Tsaramirsis, Georgios
    Buhari, Seyed M.
    APPLIED SCIENCES-BASEL, 2019, 9 (11):
  • [45] Video shot boundary detection based on multi-level features collaboration
    Shangbo Zhou
    Xia Wu
    Ying Qi
    Shuyue Luo
    Xianzhong Xie
    Signal, Image and Video Processing, 2021, 15 : 627 - 635
  • [46] Video shot boundary detection based on multi-level features collaboration
    Zhou, Shangbo
    Wu, Xia
    Qi, Ying
    Luo, Shuyue
    Xie, Xianzhong
    SIGNAL IMAGE AND VIDEO PROCESSING, 2021, 15 (03) : 627 - 635
  • [47] Saliency Detection Based on Multi-Level Deep Features and Random Walk
    Cui D.
    Wang M.
    Li G.
    Gu G.
    Li H.
    Li, Gang (lg@ysu.edu.cn), 1600, South China University of Technology (48): : 49 - 55
  • [48] Flexible thin parts multi-target positioning method of multi-level feature fusion
    Deng, Yaohua
    Liu, Xiali
    Yang, Kenan
    Li, Zehang
    IET IMAGE PROCESSING, 2024, 18 (11) : 2996 - 3012
  • [49] Multi-level boundary classification for information extraction
    Finn, A
    Kushmerick, N
    MACHINE LEARNING: ECML 2004, PROCEEDINGS, 2004, 3201 : 111 - 122
  • [50] Coastline extraction based on multi-scale segmentation and multi-level inheritance classification
    Hui, Sheng
    Mengliang, Guo
    Yuliang, Gan
    Mingming, Xu
    Shanwei, Liu
    Yasir, Muhammad
    Jianyong, Cui
    Jianhua, Wan
    FRONTIERS IN MARINE SCIENCE, 2022, 9