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• W4360865311 abstract "Low-rank and sparse decomposition (LRSD) plays a vital role in foreground–background separation. The existing LRSD methods have the drawback: imprecise surrogate functions of rank and sparsity. We propose the weighted Schatten p-norm (WSN) and Laplacian scale mixture (LSM) method based on LRSD for foreground–background separation, which introduces the WSN and LSM to improve this drawback. To demonstrate the performance of the proposed method, it is applied to foreground–background separation and gets the highest average F-measure score." @default.
• W4360865311 created "2023-03-25" @default.
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• W4360865311 date "2023-03-24" @default.
• W4360865311 modified "2023-09-23" @default.
• W4360865311 title "Weighted Schatten p-norm and Laplacian scale mixture-based low-rank and sparse decomposition for foreground–background separation" @default.
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• W4360865311 doi "https://doi.org/10.1117/1.jei.32.2.023021" @default.
• W4360865311 hasPublicationYear "2023" @default.
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