2020
DOI: 10.1109/tim.2019.2910345
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A Fast Circle Detection Method Based on a Tri-Class Thresholding for High Detail FPC Images

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Cited by 12 publications
(1 citation statement)
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“…In recent years, optimization algorithms have been widely applied to the detection of circles. Proportional genetic algorithm [22], bacterial foraging algorithm [23], differential evolutionary algorithm [24], electromagnetic optimization [25], artificial immune algorithm [26], and artificial bee colony algorithm [27] have been successively applied to the detection of circular targets. These methods do not adopt the hypothesis verification method but set an objective function in advance and repeatedly iterate the feature information extracted from the image to the objective function to obtain the circle's center and radius.…”
Section: Introductionmentioning
confidence: 99%
“…In recent years, optimization algorithms have been widely applied to the detection of circles. Proportional genetic algorithm [22], bacterial foraging algorithm [23], differential evolutionary algorithm [24], electromagnetic optimization [25], artificial immune algorithm [26], and artificial bee colony algorithm [27] have been successively applied to the detection of circular targets. These methods do not adopt the hypothesis verification method but set an objective function in advance and repeatedly iterate the feature information extracted from the image to the objective function to obtain the circle's center and radius.…”
Section: Introductionmentioning
confidence: 99%