An Optimizing Multilevel Thresholding for Image Segmentation Based on Hybrid Swarm Computation Optimization

Please use this identifier to cite or link to this item: https://lib.hpu.edu.vn/handle/123456789/34473

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DC Field	Value	Language
dc.contributor.author	Đào, Thị Kiên	en_US
dc.contributor.author	Wang, Hong Jiang	en_US
dc.contributor.author	Yu, Jie	en_US
dc.contributor.author	Nguyễn, Hữu Quỳnh	en_US
dc.contributor.author	Ngô, Trường Giang	en_US
dc.contributor.author	Nguyễn, Trọng Thể	en_US
dc.date.accessioned	2021-11-26T02:42:21Z
dc.date.available	2021-11-26T02:42:21Z
dc.date.issued	2021	en_US
dc.identifier.uri	https://lib.hpu.edu.vn/handle/123456789/34473
dc.description.abstract	This paper suggests a solution for the image segmentation(IS) problem with the multilevel thresholding based on one of the latest hybrid swarm computation optimization algorithms, particle swarms, and gravitational search (PSGA). The experimental results are comparable with other state-of-the-art algorithms that show that the PSGA on selected images is better than the competitors.	en_US
dc.format.extent	10 tr.	en_US
dc.format.mimetype	application/pdf
dc.language.iso	en	en_US
dc.publisher	Springer	en_US
dc.subject	Cross-entropy thresholding	en_US
dc.subject	Image segmentation	en_US
dc.subject	Particle warms	en_US
dc.title	An Optimizing Multilevel Thresholding for Image Segmentation Based on Hybrid Swarm Computation Optimization	en_US
dc.type	Article	en_US
dc.department	Bài báo khoa học	en_US
Appears in Collections:	Bài báo khoa học

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