Color Image Segmentation of Seed Images Based on Self-Organizing Maps (SOM) Neural Network
Título del documento: Color Image Segmentation of Seed Images Based on Self-Organizing Maps (SOM) Neural Network
Revista: Computación y sistemas
Base de datos:
Número de sistema: 000560136
ISSN: 1405-5546
Autores: 1
2
3
4
1
5
6
Instituciones: 1Universidad Tecnológica Suroeste de Guanajuato, Guanajuato. México
2Instituto Tecnológico de Celaya, Guanajuato. México
3Universidad Politécnica de Juventino Rosas, Guanajuato. México
4Universidad de Guanajuato, Salamanca. México
5Universidad de Guadalajara, Guadalajara, Jalisco. México
6Instituto Tecnológico de Estudios Superiores de Guanajuato, Guanajuato. México
Año:
Periodo: Ene-Mar
Volumen: 23
Número: 1
Paginación: 47-62
País: México
Idioma: Inglés
Tipo de documento: Artículo
Resumen en inglés 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.
Disciplinas: Ciencias de la computación
Palabras clave: Inteligencia artificial
Keyword: Image segmentation,
Neural network,
Self-organizing maps,
Artificial intelligence
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