Color Image Segmentation of Seed Images Based on Self-Organizing Maps (SOM) Neural Network

被引：0
|
作者：
Barron-Adame, J. M. [1 ]
Acosta-Navarrete, M. S. [2 ]
Quintanilla-Dominguez, J. [3 ]
Guzman-Cabrera, R. [4 ]
Cano-Contreras, M. [1 ]
Ojeda-Magana, B. [5 ]
Garcia-Sanchez, E. [6 ]
机构：
[1] Univ Tecnol Suroeste Guanajuato, Guanajuato, Mexico
[2] Inst Tecnol Celaya, Guanajuato, Mexico
[3] Univ Politecn Juventino Rosas, Guanajuato, Mexico
[4] Univ Guanajuato, Guanajuato, Mexico
[5] Univ Guadalajara, Guadalajara, Jalisco, Mexico
[6] Inst Tecnol Estudios Super Guanajuato, Guanajuato, Mexico
来源：
COMPUTACION Y SISTEMAS | 2019年 / 23卷 / 01期
关键词：
Image segmentation; neural network; self-organizing maps; SYSTEM;
D O I：
10.13053/CyS-23-1-3141
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This paper presents a threshold color image segmentation methodology based on Self-Organizing Maps (SOM) Neural Network. The objective of segmentation methodology is to determine the minimum number of color features in six seed lines ("nh1", "nh2", "nh3", "nh4", "nh5" y "nh6") of seed castor (Ricinus comunnis L.) images for future seed characterization. Seed castor lines are characterized for pigmentation regions that not allow an optimum segmentation process. In some cases, seed pigmentation regions are similar to background make difficult their segmentation characterization. Methodology proposes to segment the seed image in a SOM-based idea in an increasing way until to some of SOM neuron not have allocated none of the image pixels. Several experiments were carried out with others two standard test images ("House and "Girl") and results are presented both visual and numerical way.
引用
收藏
页码：47 / 62
页数：16
相关论文
共 50 条
  • [41] Echocardiographic image sequence segmentation and analysis using self-organizing maps
    Siqueira, ML
    Scharcanski, J
    Navaux, POA
    JOURNAL OF VLSI SIGNAL PROCESSING SYSTEMS FOR SIGNAL IMAGE AND VIDEO TECHNOLOGY, 2002, 32 (1-2): : 135 - 145
  • [42] Using Self-Organizing Neural Network for Image Mosaicing
    Sharma, Shipra
    Tuli, Hitesh
    Nagar, Shivam
    Dhir, Tanuj
    Tayal, Sumit
    PROCEEDINGS OF THE 8TH INTERNATIONAL CONFERENCE ON APPLICATIONS OF ELECTRICAL ENGINEERING/8TH INTERNATIONAL CONFERENCE ON APPLIED ELECTROMAGNETICS, WIRELESS AND OPTICAL COMMUNICATIONS, 2009, : 76 - 80
  • [43] Unified Entropy in Self-organizing Feature Maps Neural Network
    Zhu, Chunyang
    PROCEEDINGS OF THE 2017 2ND INTERNATIONAL SYMPOSIUM ON ADVANCES IN ELECTRICAL, ELECTRONICS AND COMPUTER ENGINEERING (ISAEECE 2017), 2017, 124 : 14 - 22
  • [44] NOISY IMAGE SEGMENTATION USING A SELF-ORGANIZING MAP NETWORK
    Gorjizadeh, Saleh
    Pasban, Sadegh
    Alipour, Siavash
    ADVANCES IN SCIENCE AND TECHNOLOGY-RESEARCH JOURNAL, 2015, 9 (26) : 118 - 123
  • [45] Grey self-organizing map based image segmentation
    School of Computer Science and Technology, Tianjin University of Technology, Tianjin 300191, China
    不详
    J. Inf. Comput. Sci., 2008, 1 (329-336):
  • [46] Neural Network Based SOM for Multispectral Image Segmentation in RGB and HSV Color Space
    Ganesan
    Shaik, Khamar Basha
    Sathish, B. S.
    Kalist, V.
    2015 INTERNATIONAL CONFERENCED ON CIRCUITS, POWER AND COMPUTING TECHNOLOGIES (ICCPCT-2015), 2015,
  • [47] Intelligent understanding of hyperspectral images through self-organizing neural maps
    Merenyi, Erzsebet
    CITSA/ISAS 2005: 2ND INTERNATIONAL CONFERENCE ON CYBERNETICS AND INFORMATION TECHNOLOGIES SYSTEMS AND APPLICATIONS: 11TH INTERNATIONAL CONFERENCE ON INFORMATION SYSTEMS ANALYSIS AND SYNTHESIS, VOL 1, 2005, : 30 - 35
  • [48] Catchment classification by runoff behaviour with self-organizing maps (SOM)
    Ley, R.
    Casper, M. C.
    Hellebrand, H.
    Merz, R.
    HYDROLOGY AND EARTH SYSTEM SCIENCES, 2011, 15 (09) : 2947 - 2962
  • [49] Exploring financial crises data with self-organizing maps (SOM).
    Arciniegas, I
    Daniel, B
    Embrechts, MJ
    ADVANCES IN SELF-ORGANISING MAPS, 2001, : 39 - 46
  • [50] SOMDNCD: Image Change Detection Based on Self-Organizing Maps and Deep Neural Networks
    Xiao, Ruliang
    Cui, Runxi
    Lin, Mingwei
    Chen, Lifei
    Ni, Youcong
    Lin, Xinhong
    IEEE ACCESS, 2018, 6 : 35915 - 35925
12345