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• W2077492785 startingPage "013038" @default.
• W2077492785 abstract "An anisotropic optical flow estimation method based on self-adaptive cellular neural networks (CNN) is proposed. First, a novel optical flow energy function which contains a robust data term and an anisotropic smoothing term is projected. Next, the CNN model which has the self-adaptive feedback operator and threshold is presented according to the Euler–Lagrange partial differential equations of the proposed optical flow energy function. Finally, the elaborate evaluation experiments indicate the significant effects of the various proposed strategies for optical flow estimation, and the comparison results with the other methods show that the proposed algorithm has better performance in computing accuracy and efficiency." @default.
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• W2077492785 date "2013-03-28" @default.
• W2077492785 modified "2023-09-24" @default.
• W2077492785 title "Anisotropic optical flow algorithm based on self-adaptive cellular neural network" @default.
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• W2077492785 doi "https://doi.org/10.1117/1.jei.22.1.013038" @default.
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