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Anisotropic optical flow algorithm based on self-adaptive cellular neural network

[+] Author Affiliations
Congxuan Zhang

Nanjing University of Aeronautics and Astronautics, College of Automation, Nanjing 210016, China

Zhen Chen

Nanchang Hangkong University, School of Measuring and Optical Engineering, Nanchang 330063, China

Ming Li

Nanchang Hangkong University, School of Measuring and Optical Engineering, Nanchang 330063, China

Kaiqiong Sun

Nanchang Hangkong University, School of Measuring and Optical Engineering, Nanchang 330063, China

J. Electron. Imaging. 22(1), 013038 (Mar 28, 2013). doi:10.1117/1.JEI.22.1.013038
History: Received June 28, 2012; Revised January 7, 2013; Accepted January 18, 2013
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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.

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Citation

Congxuan Zhang ; Zhen Chen ; Ming Li and Kaiqiong Sun
"Anisotropic optical flow algorithm based on self-adaptive cellular neural network", J. Electron. Imaging. 22(1), 013038 (Mar 28, 2013). ; http://dx.doi.org/10.1117/1.JEI.22.1.013038


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