28 March 2013 Anisotropic optical flow algorithm based on self-adaptive cellular neural network
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J. of Electronic Imaging, 22(1), 013038 (2013). doi:10.1117/1.JEI.22.1.013038
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.
© 2013 SPIE and IS&T
Congxuan Zhang, Zhen Chen, Ming Li, Kaiqiong Sun, "Anisotropic optical flow algorithm based on self-adaptive cellular neural network," Journal of Electronic Imaging 22(1), 013038 (28 March 2013). http://dx.doi.org/10.1117/1.JEI.22.1.013038
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KEYWORDS
Optical flow

Neural networks

Diffusion

Evolutionary algorithms

Data modeling

Sun

Partial differential equations

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