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• W2874132225 abstract "Image matting is a challenging task and has become the basis of various digital multimedia technologies. The aim of image matting is to extract the foreground from a given image with the user-provided information. This study focuses on sampling-based image matting methods. The key issue in sampling-based image matting methods is to search the best foreground-background (F-B) sample pair for each unknown pixel which is generally known as a large-scale “sample optimization problem’’. This study explores a new variant particle swarm optimization algorithm based on convergence speed controller, a premature-convergence-prevented strategy, to improve the performance of image matting. Particularly, we embed the convergence speed controller into particle swarm optimization and proposed a efficient variant algorithm of it for the sample optimization problem. We conducted extensive experiments to verify the efficiency of the proposed algorithm. The experimental results show that the proposed algorithm, compared to the existing algorithms, is competitive and can achieve higher-quality matting." @default.
• W2874132225 created "2018-07-19" @default.
• W2874132225 creator A5006334685 @default.
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• W2874132225 date "2018-01-01" @default.
• W2874132225 modified "2023-10-06" @default.
• W2874132225 title "Particle Swarm Optimization with Convergence Speed Controller for Sampling-Based Image Matting" @default.
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• W2874132225 doi "https://doi.org/10.1007/978-3-319-95933-7_75" @default.
• W2874132225 hasPublicationYear "2018" @default.
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