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• W2939848498 abstract "Foreground extraction has been a fundamental research topic in video analysis. Recently the non-convex penalty has been shown remarkable advantages in signal processing, and the fused sparsity is widely employed in image processing because it is able to preserve sharp edges that are usually the most important for clear object extraction. By decomposing video frames into low-rank background and sparse foreground in the tensor framework, a novel robust principal component analysis (RPCA) technique for foreground extraction is proposed in this paper. First, the tensor nuclear norm is employed to develop the spatio-temporal redundancy of background based on the circulant algebra. Secondly, the non-convex fused sparsity is utilized to describe foreground, which is able to enhance the smoothness of boundary. An efficient algorithm via using the alternating direction method of multipliers (ADMM) is proposed with convergence analysis. Extensive experiments on synthetic and real-world data sets show that our proposed technique outperforms the existing state-of-the-art techniques. In fact, to the best of our knowledge, this is the first time to integrate the nonconvex penalty and the fused sparsity into a tensor framework, which has demonstrated the superiority of performance." @default.
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• W2939848498 date "2018-07-01" @default.
• W2939848498 modified "2023-09-23" @default.
• W2939848498 title "Foreground Extraction via the Tensor-Based RPCA with Non-Convex Fused Sparsity" @default.
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• W2939848498 doi "https://doi.org/10.1109/fskd.2018.8686893" @default.
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