基于改进的SIFT特征的图像双向匹配算法

图像匹配是计算机视觉中许多领域的基础,特征提取则是图像匹配的基础,其中不变量特征是一个重要的理论。SIFT是最有效的尺度、旋转、亮度不变量局部特征之一,但算法复杂、计算时间长。分析了SIFT的计算时间分配,通过计算关键点的邻域梯度直方图时动态修改采样步长,大大提高了SIFT的计算速度。分析了基于SIFT特征的图像匹配算法,提出了双向匹配算法,提高了图像匹配的准确率。实验结果表明所提出的方法是有效的。

Improved SIFt-based Bidirectional Image Matching Algorithm

Scale invariant feature transform(SIFT) is one of the most effective local feature of scale,rotation and illumination invariant,but its algorithm is complicated and computation time is long.The computation time distribution is analyzed,and the computation speed is greatly improved by dynamically modifying sampling step when computing the gradient histogram of the region around the key point location.SIFT-based image matching algorithm is analyzed,and a bidirectional matching algorithm is proposed to improve the accuracy of image matching.The experimental results show that the proposed algorithms are effective.