目的 传统的基于平面拼接算法生成的全景图像存在严重的失真问题，很难保证良好的视觉一致性；而普通柱面拼接算法无法较好地满足实时性要求。为此，提出一种基于改进SIFT（scale-invariant feature transform）特征描述子的柱面全景图像拼接算法。方法 首先将待拼接的图像序列进行柱面投影，利用改进的SIFT特征检测器获取图像中的特征点，生成64维SIFT特征描述子；然后根据特征描述子之间的欧氏距离提取初始特征点对，利用RANSAC（random sample consensus）方法进一步剔除伪匹配特征点对并建立待拼接图像之间的空间变换矩阵；最后根据图像之间的空间变换矩阵进行图像配准，采用加权平均融合的方法完成图像的无缝拼接。结果 本文全景图拼接算法，可以有效地克服平面拼接算法存在的失真问题，保证了全景图像的视觉一致性。同时，相比普通柱面拼接算法，本文算法的拼接速度提高了近一倍。结论 通过对不同尺寸和数量的图像序列构建全景图，相对于平面拼接算法和普通柱面拼接算法，本文算法可以有效实现图像之间的拼接，生成宽视野、高分辨率的全景图像，且能够应用于对实时性要求比较高的图像拼接场合。

Objective Using planar stitching algorithm causes serious distortions in the panorama image, thereby resulting in difficulty ensuring good visual consistency. Meanwhile, the traditional cylindrical stitching algorithm cannot meet real-time requirements. To overcome these shortcomings, this paper proposes a cylindrical panorama stitching algorithm based on the improved SIFT (scale-invariant feature transform) feature descriptor. Method First, the image sequences to be stitched are transformed using cylindrical projection, and then the improved SIFT feature detector is adopted to extract the feature points. Accordingly, 64 dimensional SIFT feature descriptors are generated. Based on the Euclidean distance of feature descriptors, the initial feature points are determined. By using RANSAC (random sample consensus) method, false matching feature points can be eliminated further and the space transformation matrix between the images to be stitched can be constructed. Finally, the image registration can be completed successfully according to the above-mentioned space transformation matrix, where the weighted average fusion method is used to realize the seamless splicing of images. Result This paper presents a new cylindrical image-stitching algorithm, which can effectively avoid distortion problems in planar stitching algorithm. The proposed algorithm achieves good visual consistency of panoramic image. The speed of the proposed algorithm is nearly two times of that of the traditional cylindrical image-stitching algorithm. Conclusion As obtained from the experiment on panorama images using image sequences with different numbers and sizes, the proposed algorithm can stitch images more quickly and more efficiently than the planar stitching algorithm and the traditional cylindrical stitching algorithm. The generated panorama image has wide vision and high resolution, and is suitable for image-stitching applications of real-time requirements.