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Journal of Electronic Imaging

Anisotropic optical flow algorithm based on self-adaptive cellular neural network
Author(s): Congxuan Zhang; Zhen Chen; Ming Li; Kaiqiong Sun
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Paper 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.

Paper Details

Date Published: 28 March 2013
PDF: 10 pages
J. Electron. Imag. 22(1) 013038 doi: 10.1117/1.JEI.22.1.013038
Published in: Journal of Electronic Imaging Volume 22, Issue 1
Show Author Affiliations
Congxuan Zhang, Nanjing Univ. of Aeronautics and Astronautics (China)
Zhen Chen, Nanchang Hangkong Univ. (China)
Ming Li, Nanchang Hangkong Univ. (China)
Kaiqiong Sun, Nanchang Hangkong Univ. (China)

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