| Autor: |
Khudov, Hennadii, Makoveichuk, Oleksandr, Khizhnyak, Irina, Oleksenko, Oleksandr, Khazhanets, Yuriy, Solomonenko, Yuriy, Yuzova, Iryna, Dudar, Yevhen, Stetsiv, Stanislav, Khudov, Vladyslav |
| Předmět: |
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| Zdroj: |
Eastern-European Journal of Enterprise Technologies; 2022, Vol. 116 Issue 9, p6-13, 8p |
| Abstrakt: |
This paper considers the improved method for segmenting complex structured images acquired from space observation systems based on the particle swarm algorithm. Unlike known ones, the method for segmenting complex structured images based on the particle swarm algorithm involves the following: – highlighting brightness channels in the Red-Green-Blue color space; – using a particle swarm method in the image in each channel of brightness of the RGB color space; – image segmentation is reduced to calculating the objective function, moving speed, and a new location for each swarm particle in the image in each RGB color space brightness channel. Experimental studies have been conducted on the segmentation of a complex structured image by a method based on the particle swarm algorithm. It was established that the improved segmentation method based on the particle swarm algorithm makes it possible to segment complex structured images acquired from space surveillance systems. A comparison of the quality of segmenting a complex structured image was carried out. The comparative visual analysis of well-known and improved segmentation methods indicates the following: – the improved segmentation method based on the particle swarm algorithm highlights more objects of interest (objects of military equipment); – the well-known k-means method assigns some objects of interest (especially those partially covered with snow) to the snow cover (marked in blue); – the improved segmentation method also associates some objects of interest that are almost completely covered with snow with the snow cover (marked in blue). It has been established that the improved segmentation method based on the particle swarm algorithm reduces segmentation errors of the first kind by an average of 12 % and reduces segmentation errors of the second kind by an average of 8 %. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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