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- W2086799992 abstract "In this paper, two dimensional principal components of natural images (2D-PCs) are proposed. Similar to principal components of natural images (1D-PCs), 2D-PCs can also be viewed as fundamental components of human's receptive field because they contain edge-like, bar-like and grating-like patterns. However, compared to 1D-PCs, 2D-PCs are of surprising symmetry, stable regularity, good interpretability, and have little computational complexity in real applications. Then, based on 1D-PCs and 2D-PCs, we design two kinds of statistical texture features (STF(1D) and STF(2D)), and apply them to multi-class facial expression recognition. Numerous experimental results demonstrate that our statistical texture features are better or not worse than other popular features for facial expression recognition." @default.
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- W2086799992 date "2011-10-01" @default.
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- W2086799992 title "Two dimensional principal components of natural images and its application" @default.
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- W2086799992 doi "https://doi.org/10.1016/j.neucom.2011.03.047" @default.
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