Name
Abstract | ||
|---|---|---|
This correspondence paper proposes a fast orientation estimation approach of natural images without the help of semantic information. Different from traditional low-level features, our low-level features are extracted inspired by the biological simple cells of the visual cortex. Two approximated receptive fields to mimic the biological cells are presented, and a local rotation operator is introduced to determine the optimal output and local orientation corresponding to an image position, which serve as the low-level feature employed in this paper. To generate the low-level features, a bisection method is applied to the first derivative of the model of receptive fields. Moreover, the feature screener is introduced to eliminate too much useless low-level features, which will speed up the processing time. After all the valuable low-level features are combined, the overall image orientation is estimated. The proposed approach possesses several features suitable for real-time applications. First, it avoids the tedious training procedure of some conventional methods. Second, no specific reference such as the horizon is assumed and no a priori knowledge of image is required. The proposed approach achieves a real-time orientation estimation of natural images using only low-level features with a satisfactory resolution. The effectiveness of our proposed approach is verified on real images with complex scenes and strong noises. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/TSMC.2015.2497253 | IEEE Trans. Systems, Man, and Cybernetics: Systems |
Keywords | Field | DocType |
Feature extraction,Estimation,Semantics,Cells (biology),Biological system modeling,Real-time systems | Computer vision,Bisection method,Computer science,A priori and a posteriori,Feature extraction,Artificial intelligence,Orientation (computer vision),Rotation operator,Real image,Machine learning,Semantics,Speedup | Journal |
Volume | Issue | ISSN |
46 | 11 | 2168-2216 |
Citations | PageRank | References |
7 | 0.48 | 12 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhiqiang Cao | 1 | 169 | 29.46 |
| Xilong Liu | 2 | 25 | 6.09 |
| Nong Gu | 3 | 77 | 9.58 |
| Saeid Nahavandi | 4 | 1545 | 219.71 |
| De Xu | 5 | 142 | 25.04 |
| Chao Zhou | 6 | 82 | 13.49 |
| Min Tan | 7 | 2342 | 201.12 |