In this paper, a new method for image mosaicing (image stitching) is introduced based on Scale Invariant Feature transform (SIFT). One of the main drawbacks of SIFT is the redundancy of the extracted keypoints, which leads to lower image mosaicing quality. Recently, a new method called Redundant Keypoint Elimination (RKEM) was presented to remove these redundant features, and enhance image registration performance. Despite the applicability of RKEM, its threshold value is considered the same in all parts of the image. This characteristic leads to inappropriate removal of keypoints due to the fact that distribution of keypoints in the high-detailed region is denser than the low-detailed ones. This paper proposes a new method to improve RKEM called Clustered RKEM (CRKEM) which is based on keypoints distribution. Moreover, in this paper a new blending algorithm is proposed based on a Gaussian-weighted function. In the proposed blending method, the Gaussian function is proposed based on the mean and variance of the pixels in the overlapped region of images to be mosiaced. In comparison with the classical methods, the experimental results confirm the superiority of the proposed method in image mosaicing as well as to image registration and matching.

本文介绍了一种基于尺度不变特征变换（SIFT）的图像拼接（图像拼接）新方法。SIFT 的主要缺点之一是提取的关键点的冗余性，这导致图像拼接质量较低。最近，提出了一种称为冗余关键点消除（RKEM）的新方法来去除这些冗余特征，并提高图像配准性能。尽管 RKEM 具有适用性，但其阈值在图像的所有部分都被认为是相同的。由于高细节区域的关键点分布比低细节区域的关键点分布更密集，因此该特性导致关键点的不适当去除。本文提出了一种改进 RKEM 的新方法，称为集群 RKEM（CRKEM），它基于关键点分布。而且，在本文中，提出了一种基于高斯加权函数的新混合算法。在所提出的混合方法中，基于待拼接图像重叠区域中像素的均值和方差提出高斯函数。与经典方法相比，实验结果证实了该方法在图像拼接以及图像配准匹配方面的优越性。