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

Congxuan Zhang; Zhen Chen; Ming Li; Kaiqiong Sun

doi:10.1117/1.JEI.22.1.013038

28 March 2013 Anisotropic optical flow algorithm based on self-adaptive cellular neural network

Congxuan Zhang, Zhen Chen, Ming Li, Kaiqiong Sun

Author Affiliations +

Journal of Electronic Imaging, Vol. 22, Issue 1, 013038 (March 2013). https://doi.org/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.

Citation Download Citation

Congxuan Zhang, Zhen Chen, Ming Li, and Kaiqiong Sun "Anisotropic optical flow algorithm based on self-adaptive cellular neural network," Journal of Electronic Imaging 22(1), 013038 (28 March 2013). https://doi.org/10.1117/1.JEI.22.1.013038

Published: 28 March 2013

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