Scale invariant feature transform (SIFT) is one of the most effective techniques in image matching applications. However, it has a main drawback: existing numerous redundant keypoints located very close to each other in the image. These redundant keypoints increase the computational complexity while they decrease the image matching performance. Redundant keypoint elimination method (RKEM)–SIFT are incorporated to eliminate these points by comparing their distances with a fixed experimental threshold value. However, this value has a great impact on the matching results. In this paper, an adaptive RKEM is presented which considers type of the images and distortion thereof, while adjusting the threshold value. Moreover, this value is found separately for the reference and sensed images. In an image, the adaptive RKEM finds the histogram of the keypoints distances, for which the number and the width of the bins are determined based on the number of keypoints and the distances distribution metrics. Then, a maximum value for searching the optimal threshold value is determined. Finally, for each integer value smaller than the mentioned maximum, a set containing distances smaller than that value is created and the one with the smallest variance is selected. The integer value corresponding to that set is chosen as the adaptive threshold for that image. This approach can improve the efficiency of the RKEM-SIFT in eliminating redundant keypoints. Simulation results validated that the proposed method outperforms the SIFT, A² SIFT and RKEM-SIFT in terms of the matching performance indices.

尺度不变特征变换（SIFT）是图像匹配应用程序中最有效的技术之一。但是，它有一个主要缺点：现有的大量冗余关键点在图像中彼此非常靠近。这些冗余关键点增加了计算复杂度，同时降低了图像匹配性能。通过将冗余点消除方法（RKEM）–SIFT与固定实验阈值进行比较，可以消除这些点。但是，此值对匹配结果有很大影响。在本文中，提出了一种自适应RKEM，它在调整阈值的同时考虑图像的类型及其失真。此外，针对参考图像和感测图像分别找到该值。在图像中 自适应RKEM会找到关键点距离的直方图，并根据关键点的数量和距离分布指标确定垃圾箱的数量和宽度。然后，确定用于搜索最佳阈值的最大值。最后，对于每个小于所述最大值的整数值，将创建一个包含小于该值的距离的集合，并选择方差最小的集合。选择与该集合相对应的整数值作为该图像的自适应阈值。这种方法可以提高RKEM-SIFT消除冗余关键点的效率。仿真结果验证了该方法优于SIFT，A 为此，将根据关键点的数量和距离分布指标确定垃圾箱的数量和宽度。然后，确定用于搜索最佳阈值的最大值。最后，对于每个小于所述最大值的整数值，将创建一个包含小于该值的距离的集合，并选择方差最小的集合。选择与该集合相对应的整数值作为该图像的自适应阈值。这种方法可以提高RKEM-SIFT消除冗余关键点的效率。仿真结果验证了该方法优于SIFT，A 为此，将根据关键点的数量和距离分布指标确定垃圾箱的数量和宽度。然后，确定用于搜索最佳阈值的最大值。最后，对于每个小于所述最大值的整数值，将创建一个包含小于该值的距离的集合，并选择方差最小的集合。选择与该集合相对应的整数值作为该图像的自适应阈值。这种方法可以提高RKEM-SIFT消除冗余关键点的效率。仿真结果验证了该方法优于SIFT，A 将创建一个包含小于该值的距离的集合，并选择方差最小的集合。选择与该集合相对应的整数值作为该图像的自适应阈值。这种方法可以提高RKEM-SIFT消除冗余关键点的效率。仿真结果验证了该方法优于SIFT，A 将创建一个包含小于该值的距离的集合，并选择方差最小的集合。选择与该集合相对应的整数值作为该图像的自适应阈值。这种方法可以提高RKEM-SIFT消除冗余关键点的效率。仿真结果验证了该方法优于SIFT，A² SIFT和RKEM-SIFT在匹配性能指标方面。