Resumen

BARRON-ADAME, J. M. et al. Color Image Segmentation of Seed Images Based on Self-Organizing Maps (SOM) Neural Network. Comp. y Sist. [online]. 2019, vol.23, n.1, pp.47-62.  Epub 26-Feb-2019. ISSN 2007-9737.  https://doi.org/10.13053/cys-23-1-3141.

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.

Palabras llave : Image segmentation; neural network; self-organizing maps.

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