| 摘要: |
| 针对目前图像拼接过程中特征点提取速度慢和特征点匹配精度不高的问题，提出了一种图像拼接的优化算法，即首先对待拼接图像进行降采样处理、然后根据半图像区域提取特征点并采用SSDA(Sequential Similarity Detection Algorithm)算法进行特征点提纯，最后进行图像拼接;拼接结果表明：与传统的图像拼接方法相比，新的优化算法大大地降低了计算数据量，在图像拼接时间方面具有明显的优势. |
| 关键词: 图像拼接 图像配准 降采样 SURF算法 SSDA算法 |
| DOI： |
| 分类号: |
| 基金项目: |
|
| An Optimized Image Mosaic Method |
|
ZHANG Sheng, HAN Chao
|
| Abstract: |
| In view of the fact that feature point extraction speed is slow and that its matching accuracy is not high in the current image stitching process,an optimized image stitching algorithm is proposed.Firstly,the images to be spliced are carried by the down sampling process; then extract the feature points on the half image area and use the Sequential Similarity Detection Algorithm (SSDA) to purify the feature points; finally,stitch the images.Stitching results show that compared with the traditional image matching method,the new optimization algorithm greatly reduces calculated data and has obvious advantages in image stitching time. |
| Key words: image stitching image registration down sampling SURF algorithm SSDA algorithm |
