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

页码：47 / 62

页数：16

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