Cluster Classification Characteristics of the Critical Principal Image Histogram Component

被引：0
|
作者：
Homnan, Bongkarn [1 ]
Benjapolakul, Watit [2 ]
机构：
[1] Dhurakij Pundit Univ, Res Ctr, Bangkok 10210, Thailand
[2] Chulalongkorn Univ, Fac Engn, Dept Elect Engn, Bangkok 10330, Thailand
来源：
2014 INTERNATIONAL ELECTRICAL ENGINEERING CONGRESS (IEECON) | 2014年
关键词：
cluster; component; critical; histogram; image;
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
There are a lot of limit image histogram components in any image. This paper selects the principal image histogram components and evaluates them to get the critical one. As the central value of the image the critical principal image histogram component hold clusters and their distributions in the image. On the concept of the pixel distance, determinate mathematical model of probability and cumulative density functions categorize image sub clusters and their member details with the threshold of the difference and the threshold of the number of pixels, within the image coverage of the critical principal image histogram component.
引用
收藏
页数：4
相关论文
共 50 条
  • [1] TRUNCATED ISOTROPIC PRINCIPAL COMPONENT CLASSIFIER FOR IMAGE CLASSIFICATION
    Rozza, Alessandro
    Serra, Giuseppe
    Grana, Costantino
    2014 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP), 2014, : 986 - 990
  • [2] Classification of biological spectrum based on principal component cluster analysis
    Zhang, Lianshun
    Shi, Aijuan
    ADVANCED DESIGNS AND RESEARCHES FOR MANUFACTURING, PTS 1-3, 2013, 605-607 : 2245 - 2248
  • [3] Robust principal component analysis with projection learning for image classification
    Liang, Yingyi
    He, Zhenyu
    JOURNAL OF MODERN OPTICS, 2020, 67 (08) : 704 - 720
  • [4] Nonlinear Discriminant Principal Component Analysis for Image Classification and Reconstruction
    Filisbino, Tiene Andre
    Giraldi, Gilson Antonio
    Thomaz, Carlos Eduardo
    2018 7TH BRAZILIAN CONFERENCE ON INTELLIGENT SYSTEMS (BRACIS), 2018, : 312 - 317
  • [5] New versions of principal component analysis for image enhancement and classification
    Cheng, QM
    IGARSS 2002: IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM AND 24TH CANADIAN SYMPOSIUM ON REMOTE SENSING, VOLS I-VI, PROCEEDINGS: REMOTE SENSING: INTEGRATING OUR VIEW OF THE PLANET, 2002, : 3372 - 3374
  • [6] Classification of Image Database Using Independent Principal Component Analysis
    Kekre, H. B.
    Sarode, Tanuja K.
    Save, Jagruti K.
    INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS, 2013, 4 (07) : 109 - 116
  • [7] WRAPPER BASED PRINCIPAL COMPONENT SELECTION FOR HYPERSPECTRAL IMAGE CLASSIFICATION
    Singh, Arvind Kumar
    Sunkara, Renuvenkataswamy
    Kadambi, Govind R.
    2022 12TH WORKSHOP ON HYPERSPECTRAL IMAGING AND SIGNAL PROCESSING: EVOLUTION IN REMOTE SENSING (WHISPERS), 2022,
  • [8] Automatic Damage Classification Based on Wave Cluster and Principal Component Analysis
    Gharibnezhad, F.
    Mujica, L. E.
    Rodellar, J.
    Fritzen, C. P.
    STRUCTURAL HEALTH MONITORING 2013, VOLS 1 AND 2, 2013, : 2760 - 2767
  • [9] The application of principal component cluster analysis in environment classification for Chinese cities
    Wang, Jingcheng
    Zhang, Lunwu
    Zhang, Dingfei
    Zhao, Fangchao
    Yang, Xiaokui
    2020 THIRD INTERNATIONAL WORKSHOP ON ENVIRONMENT AND GEOSCIENCE, 2020, 569
  • [10] Image-based classification of driving scenes by Hierarchical Principal Component Classification (HPCC)
    Kastner, Robert
    Schneider, Frank
    Michalke, Thomas
    Fritsch, Jannik
    Goerick, Christian
    2009 IEEE INTELLIGENT VEHICLES SYMPOSIUM, VOLS 1 AND 2, 2009, : 341 - 346
12345