Name
Title | ||
|---|---|---|
Real-time adaptive visible and infrared image registration based on morphological gradient and C_SIFT |
Abstract | ||
|---|---|---|
Since the visible and infrared images have different imaging mechanisms, the difficulty of image registration has greatly increased. The grayscale difference between visible and infrared images is very disadvantageous for extracting feature points in homogenous region, but they both retain the obvious contour edge in the scene. After using the morphological gradient method, the grayscale edge of visible and infrared images can be obtained and their similarity is greatly improved, and their difference may be seen as the difference in brightness or grayscale. Therefore, we proposed a novel algorithm to realise real-time adaptive registration of visible and infrared images using morphological gradient and C_SIFT. Firstly, the morphological gradient method is used to extract the rough edges of visible and infrared images for aligning their visual features as a single similar type. Secondly, the C_SIFT feature detection operator is used to detect and extract feature points from the extracted edges. The C_SIFT uses the centroid method to describe the main direction of feature points, makes rotation invariance feasible. Finally, to verify the effectiveness of the proposed algorithm, we carried out a series of experiments in eight various scenarios. The experimental results show that the proposed algorithm has achieved good experimental results. The registration of visible and infrared images can be completed quickly by the proposed algorithm, and the registration accuracy is satisfactory. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1007/s11554-019-00858-x | Journal of Real-Time Image Processing |
Keywords | DocType | Volume |
Visible image, Infrared image, Registration, Morphological gradient, SIFT, Real-time | Journal | 17 |
Issue | ISSN | Citations |
5 | 1861-8200 | 2 |
PageRank | References | Authors |
0.36 | 14 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zeng Qiang | 1 | 34 | 10.73 |
| Jianhua Adu | 2 | 7 | 1.43 |
| Jiexin Liu | 3 | 2 | 0.36 |
| Jianxing Yang | 4 | 2 | 0.36 |
| Yuanping Xu | 5 | 8 | 5.20 |
| Mei Gong | 6 | 2 | 0.36 |