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• W3137648749 abstract "Image segmentation is a prime operation to understand the content of images. Multilevel thresholding is applied in image segmentation because of its speed and accuracy. In this paper, a novel multilevel thresholding algorithm based on Cuckoo search (CS) is introduced. One of the major drawbacks of metaheuristic algorithms is the stagnation phenomenon which leads to a fall into local optimums and premature convergence. To overcome this shortcoming, the idea of Darwinian theory is incorporated with CS algorithm to increase the diversity and quality of the individuals without decreasing the convergence speed of CS algorithm. A policy of encouragement and punishment is considered to lead searching agents in the search space and reduce the computational time. The algorithm is implemented based on dividing the population into specified groups and each group tries to find a better location. Ten test images are selected to verify the ability of our algorithm using the famous energy curve method. Two popular entropies criteria, Otsu and Kapur, are employed to evaluate the capability of the introduced algorithm. Eight different search algorithms are also implemented and compared with our method. Experimental results manifest that DCS is a powerful tool for multilevel thresholding and the obtained results outperform the CS algorithm and other heuristic search methods." @default.
• W3137648749 created "2021-03-29" @default.
• W3137648749 creator A5000173534 @default.
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• W3137648749 date "2021-03-17" @default.
• W3137648749 modified "2023-09-26" @default.
• W3137648749 title "A Multilevel Image Thresholding Method Using the Darwinian Cuckoo Search Algorithm" @default.
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• W3137648749 doi "https://doi.org/10.1142/s0219467821500522" @default.
• W3137648749 hasPublicationYear "2021" @default.
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