Modified Artificial Ecosystem-Based Optimization for Multilevel Thresholding Image Segmentation; Mathematics; Vol. 9, iss. 19

Modified Artificial Ecosystem-Based Optimization for Multilevel Thresholding Image Segmentation; Mathematics; Vol. 9, iss. 19

Bibliographic Details
Parent link:	Mathematics Vol. 9, iss. 19.— 2021.— [2363, 25 p.]
Corporate Author:	Национальный исследовательский Томский политехнический университет Инженерная школа информационных технологий и робототехники
Other Authors:	Ewees A. A. Ahmed, Abualigah L. Laith, Yousri D. Dalia, Sahlol A. T. Ahmed, Al-qaness M. A. A. Mohammed, Alshathri S. Samah, Mokhamed Elsaed (Mohamed Abd Elaziz) A. M. Akhmed Mokhamed
Summary:	Title screen Multilevel thresholding is one of the most effective image segmentation methods, due to its efficiency and easy implementation. This study presents a new multilevel thresholding method based on a modified artificial ecosystem-based optimization (AEO). The differential evolution (DE) is applied to overcome the shortcomings of the original AEO. The main idea of the proposed method, artificial ecosystem-based optimization differential evolution (AEODE), is to employ the operators of the DE as a local search of the AEO to improve the ecosystem of solutions. We used benchmark images to test the performance of the AEODE, and we compared it to several existing approaches. The proposed AEODE achieved a high performance when evaluated by the structural similarity index (SSIM), peak signal-to-noise ratio (PSNR), and fitness values. Moreover, the AEODE outperformed the basic version of the AEO concerning SSIM and PSNR by 78% and 82%, respectively, which reserves the best features for each of AEO and DE.
Language:	English
Published:	2021
Subjects:	электронный ресурс труды учёных ТПУ image segmentation multilevel thresholding artificial ecosystem-based optimization (AEO) differential evolution (DE) optimization algorithms сегментация изображения обработка оптимизация экосистемы эволюция алгоритмы
Online Access:	https://doi.org/10.3390/math9192363
Format:	Electronic Book Chapter
KOHA link:	https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=667751

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