基于Harris多尺度角点检测的图像配准新算法

为改进角点检测算子的检测性能,提高基于角点的图像配准算法的配准精度,把多分辨分析的思想引入到经典的Harris角点检测中,构造了基于小波变换的灰度强度变化公式,并得到了具有尺度变换特性的自相关矩阵,从而构建了一种新的Harris多尺度角点检测算法。这样,使得新的角点检测可以在不同的尺度下获取角点,并克服了单一尺度的Harris角点检测可能存在的角点信息丢失、位置偏移和易受噪而提取出伪角点等问题。然后根据角度直方图得到的旋转角度,和提取的以角点为中心的特征子图,定义了角点点对的对齐度。最后,运用最大化对齐度准则来精确地确定角点匹配点对。实验表明,该配准算法具有精确性、有效性和抗噪性,实现了良好的配准效果。

Novel Image Registration Based on Harris Multi-Scale Corner Detection Algorithm

The performance of feature detector determines the precision of registration directly.In this paper,the multi-resolution idea is introduced into the classical Harris algorithm,and the wavelet-based formula for measuring the image intensity variation is developed,meanwhile,the auto-correlation matrix is obtained that reflected the scale variation information.Then,a novel Harris multi-scale corner detection algorithm is presented,which might overcome the drawback that the single-scale Harris detector usually leads to either missing significant corners or detecting false corners due to noise.In order to compensate for the orientation difference between two target images,a so-called "angle histogram" is calculated.Based on the rotation angle and feature sub-images,the Corner Pair Alignment Metric(CPAM) is defined.Finally,each corner matching pair is determined accurately using maximization of the AM.Experiments demonstrate that this algorithm has a good performance of accuracy,efficiency and robustness.