Image Thresholding Improved by Global Optimization Methods

Autor:	Felipe Balabanian, Eduardo Sant'Ana da Silva, Helio Pedrini
Jazyk:	angličtina
Rok vydání:	2017
Předmět:	Electronic computers. Computer science QA75.5-76.95 Cybernetics Q300-390
Zdroj:	Applied Artificial Intelligence, Vol 31, Iss 3, Pp 197-208 (2017)
Druh dokumentu:	article
ISSN:	0883-9514 1087-6545 08839514
DOI:	10.1080/08839514.2017.1300050
Popis:	Image thresholding is a common segmentation technique with applications in various fields, such as computer vision, pattern recognition, microscopy, remote sensing, and biology. The selection of threshold values for segmenting pixels into foreground and background regions is usually based on subjective assumptions or user judgments under empirical rules or manually determined. This work describes and evaluates six effective threshold selection strategies for image segmentation based on global optimization methods: genetic algorithms, particle swarm, simulated annealing, and pattern search. Experiments are conducted on several images to demonstrate the effectiveness of the proposed methodology.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/bbb2f0af5da04f70aaad1879623c4dae Zobrazit plný text záznamu View record in DOAJ Plný text ve formátu PDF Plný text ve formátu HTML
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