IMAGE SEGMENTATION OF TYPHOON SPIRAL CLOUD BANDS BASED ON SUPPORT VECTOR MACHINE

被引:6
|
作者
Xu, Jin-Wei [1 ]
Wang, Ping [1 ]
Xie, Yi-Yang [2 ]
机构
[1] Tianjin Univ, Automat Sch, Tianjin 300072, Peoples R China
[2] Tianjin Acad Met Sci, Tianjin 300074, Peoples R China
来源
PROCEEDINGS OF 2009 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-6 | 2009年
关键词
Support vector machine; Typhoon; Spiral cloud bands;
D O I
10.1109/ICMLC.2009.5212398
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Typhoon is one type of disaster weather which can impose serious impact on the life and production of human society. It has special physical characteristics of clouds with the structure of cloud eye, cloud walls and spiral cloud bands. Much information about Typhoon motion, wind field and heavy rainfall is contained in spiral cloud bands. Therefore it is very important to segment and recognize its spiral cloud bands as clearly as possible. As it is difficult to achieve the expected result with the existing image segmentation algorithms, in the study support vector machine was applied to segmentation of Typhoon spiral cloud bands while the problem of image segmentation can be solved by transforming it to the problem of classification. In order to achieve ideal results of segmentation, it is necessary to analyze and compare all aspects carefully in the selection of training samples, the determination of kernel function, the setting of its parameters and the common segmentation algorithms. The results demonstrated that the algorithm proposed in this paper has much more advantages than the existing algorithms, being more accurate, rapid and robust.
引用
收藏
页码:1088 / +
页数:3
相关论文
共 50 条
  • [1] Image Segmentation Based on Support Vector Machine
    Wang, Xuejun
    Wang, Shuang
    Zhu, Yubin
    Meng, Xiangyi
    PROCEEDINGS OF 2012 2ND INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE AND NETWORK TECHNOLOGY (ICCSNT 2012), 2012, : 202 - 206
  • [2] Image Segmentation Based on Support Vector Machine
    徐海祥
    朱光喜
    田金文
    张翔
    彭复员
    Journal of Electronic Science and Technology of China, 2005, (03) : 226 - 230
  • [3] A Cloud Support Vector Machine Model based on image semantics
    Xing, Ling
    Zhao, Wei
    Fu, Rong
    INFORMATION TECHNOLOGY APPLICATIONS IN INDUSTRY II, PTS 1-4, 2013, 411-414 : 1170 - 1173
  • [4] Neural Cell Image Segmentation Method Based on Support Vector Machine
    Niu, Shiwei
    Ren, Kan
    AOPC 2015: IMAGE PROCESSING AND ANALYSIS, 2015, 9675
  • [5] A morphological approach to pipe image interpretation based on segmentation by support vector machine
    Mashford, John
    Rahilly, Mike
    Davis, Paul
    Burn, Stewart
    AUTOMATION IN CONSTRUCTION, 2010, 19 (07) : 875 - 883
  • [6] Online learning method based on support vector machine for metallographic image segmentation
    Li, Mingchun
    Chen, Dali
    Liu, Shixin
    Guo, Dinghao
    SIGNAL IMAGE AND VIDEO PROCESSING, 2021, 15 (03) : 571 - 578
  • [7] Online learning method based on support vector machine for metallographic image segmentation
    Mingchun Li
    Dali Chen
    Shixin Liu
    Dinghao Guo
    Signal, Image and Video Processing, 2021, 15 : 571 - 578
  • [8] A modified support vector machine and its application to image segmentation
    Yu, Zhiwen
    Wong, Hau-San
    Wen, Guihua
    IMAGE AND VISION COMPUTING, 2011, 29 (01) : 29 - 40
  • [9] Support Vector Machine-based Image Segmentation Approach for Automatic Agriculture Vehicle
    Han, Yonghua
    Wang, Yaming
    Zhao, Yun
    PROCEEDINGS OF 2012 INTERNATIONAL CONFERENCE ON IMAGE ANALYSIS AND SIGNAL PROCESSING, 2012, : 251 - 255
  • [10] Integrating support vector machine and graph cuts for medical image segmentation
    Zheng, Qiang
    Li, Honglun
    Fan, Baode
    Wu, Shuanhu
    Xu, Jindong
    JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION, 2018, 55 : 157 - 165