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

Authors

  • J. M. Barrón Adame Universidad Tecnológica del Suroeste de Guanajuato, Valle de Santiago
  • M. S. Acosta Navarrete Instituto Tecnológico de Celaya, Guanajuato
  • J. Quintanilla Domínguez Universidad Politécnica de Juventino Rosas, Guanajuato
  • R. Guzmán Cabrera Universidad de Guanajuato, Salamanca, Guanajuato
  • M. Cano Contreras Universidad Tecnológica del Suroeste de Guanajuato
  • B. Ojeda Magaña Universidad de Guadalajara, Jalisco
  • E. García Sánchez Instituto Tecnológico de Estudios Superiores de Guanajuato

DOI:

https://doi.org/10.13053/cys-23-1-3141

Keywords:

Image segmentatio, neural networks, self-organizing maps

Abstract

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.

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Published

2019-03-24

Issue

Vol. 23 No. 1 (2019): Recent Advances in Optics and Photonics (Guest Editors: J. J. Sánchez Mondragón, M. Torres Cisneros, R. Guzmán Cabrera)

Section

Articles of the Thematic Issue

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