Name
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. (C) 2013 SPIE and IS&T |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1117/1.JEI.22.1.013038 | JOURNAL OF ELECTRONIC IMAGING |
Field | DocType | Volume |
Anisotropy,Computer science,Algorithm,Robust statistics,Smoothing,Operator (computer programming),Artificial neural network,Cellular neural network,Partial differential equation,Optical flow | Journal | 22 |
Issue | ISSN | Citations |
1 | 1017-9909 | 0 |
PageRank | References | Authors |
0.34 | 9 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Congxuan Zhang | 1 | 0 | 0.34 |
| Zhen Chen | 2 | 30 | 5.97 |
| Ming Li | 3 | 0 | 1.01 |
| Kaiqiong Sun | 4 | 0 | 0.34 |