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

共 50 条

[21] Self-organizing Feature Map and Linear Discriminant Analysis based Image Segmentation
Halder, Amiya
Hassan, Sk. Sajid
2015 1ST INTERNATIONAL CONFERENCE ON FUTURISTIC TRENDS ON COMPUTATIONAL ANALYSIS AND KNOWLEDGE MANAGEMENT (ABLAZE), 2015, : 394 - 399
[22] Fast CEUS image segmentation based on Self Organizing Maps
Paire, Julie
Sauvage, Vincent
Albouy-Kissi, Adelaide
Ladam Marcus, Viviane
Marcus, Claude
Hoeffel, Christine
MEDICAL IMAGING 2014: IMAGE PROCESSING, 2014, 9034
[23] New adaptive color quantization method based on self-organizing maps
Chang, CH
Xu, PF
Xiao, R
Srikanthan, T
IEEE TRANSACTIONS ON NEURAL NETWORKS, 2005, 16 (01): : 237 - 249
[24] Satellite Image Segmentation Using Self-Organizing Maps and Fuzzy C-Means
Awad, Mohamad M.
Nasri, Ahmad
2009 IEEE INTERNATIONAL SYMPOSIUM ON SIGNAL PROCESSING AND INFORMATION TECHNOLOGY (ISSPIT 2009), 2009, : 398 - +
[25] Self-organizing Maps as Feature Detectors for Supervised Neural Network Pattern Recognition
Cordel, Macario O., II
Antioquia, Arren Matthew C.
Azcarraga, Arnulfo P.
NEURAL INFORMATION PROCESSING, ICONIP 2016, PT IV, 2016, 9950 : 618 - 625
[26] Video Object Segmentation Using Color-Component-Selectable Learning for Self-Organizing Maps
Umata, Shin-ya
Kamiura, Naotake
Saitoh, Ayumu
Isokawa, Teijiro
Matsui, Nobuyuki
PROCEEDINGS OF THE SIXTEENTH INTERNATIONAL SYMPOSIUM ON ARTIFICIAL LIFE AND ROBOTICS (AROB 16TH '11), 2011, : 850 - 853
[27] Video object segmentation using color-component-selectable learning for self-organizing maps
Kamiura, Naotake
Umata, Shin-ya
Saitoh, Ayumu
Isokawa, Teijiro
Matsui, Nobuyuki
ARTIFICIAL LIFE AND ROBOTICS, 2011, 16 (02) : 258 - 261
[28] Stereo Ficho Cancellation based on Self-Organizing Maps Neural Networks
Kunarak, Sunisa
2019 7TH INTERNATIONAL ELECTRICAL ENGINEERING CONGRESS (IEECON 2019), 2019,
[29] Self-organizing map combined with a fuzzy clustering for color image segmentation of edible beans
Chtioui, Y
Panigrahi, S
Backer, LF
TRANSACTIONS OF THE ASAE, 2003, 46 (03): : 831 - 838
[30] Intelligent Diagnosis Method of MRI Brain Image Using Parallel Self-Organizing Feature Maps Neural Network
Liu, Li
Hua, Chi
Cheng, Zixuan
Ji, Yunfeng
JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS, 2021, 11 (02) : 487 - 496

← 1 2 3 4 5 →