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引用次数: 2
摘要
图像配准是医学、计算机视觉和遥感等领域常用的图像配准方法。与现有方法相比,本文提出了一种有效的方法,可以提供更快的速度和更高的精度。本文重点研究了基于Harris检测器的特征检测方法,该方法相对于其他特征检测算法,在尺度、旋转角度、照度和噪声等方面具有较强的不变性,在性能上获得了最好的检测结果。利用梯度直方图对每个检测到的兴趣点生成特征向量。使用Best bin first (BBF)对生成的特征向量进行匹配,生成准确的兴趣点对并对齐感测图像。采用附加步骤去除异常值,提高配准精度。实验结果表明,该方法采用Harris检测器、Hog描述子、BBF和RANSAC,具有较高的精度和鲁棒性。
An efficient technique for subpixel accuracy using integrated feature based image registration
Image registration which is frequently required in Medical, Computer vision and remote sensing field is used to align two images geometrically. This paper presents efficient method for providing speedup and more accuracy in compared to current state of the art existing methods. This paper focuses on Feature detection using Harris detector which gives best result based on performance and has firm invariance to scale, rotation angle, illuminance and noise compare to other feature detection algorithms. Also Feature vector is generated for each detected interest point using Histogram of gradient. Generated feature vector are matched in both image using Best bin first (BBF) to generate accurate Interest point pair and to align the sense image. An additive step is applied to remove outliers and to improve registration accuracy. An experimental result shows that the present method using Harris detector, Hog descriptor, BBF and RANSAC provides higher precision and also it is more robust.