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• W2507476065 abstract "Intrinsic image decomposition is an important topic in computer vision and computer graphics applications. However, this is a challenging problem by adopting the information of a single image. Therefore, additional priors or supplementary information such as multiply images or user interactions are necessary to address this problem. In this paper, we propose a novel scheme to use multiple images for intrinsic image decomposition, based on a similar strategy in robust principal component analysis (RPCA). RPCA utilizes the fact that the reflectance layer is common in multiple lights of a scene, and attempts to decompose the data matrix constructed from input images into a low-rank matrix and a sparse matrix. This is possible if the sparse matrix is sufficiently sparse, which is often not the case in computer vision applications. Moreover, the weighting parameter between the low-rank and sparse matrices greatly affects the accuracy of the results, and tuning this parameter can be tricky. This paper proposes a rank-constrained PCA algorithm (RCPCA) for solving background recovering problems. Fixing the rank of the low-rank matrix to be 1 allows RCPCA to better recover the low-rank matrix from the data matrix. Comprehensive tests show that RCPCA produces more stable and accurate results than RP-CA." @default.
• W2507476065 created "2016-09-16" @default.
• W2507476065 creator A5029351151 @default.
• W2507476065 date "2016-09-01" @default.
• W2507476065 modified "2023-09-30" @default.
• W2507476065 title "Rank-constrained PCA for intrinsic images decomposition" @default.
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• W2507476065 doi "https://doi.org/10.1109/icip.2016.7533026" @default.
• W2507476065 hasPublicationYear "2016" @default.
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