基于图像分块的多尺度Harris角点检测方法

Multi-scale Harris corner detection based on image block

Harris角点检测是一种经典的角点检测算法,在现实中应用广泛,但不具有尺度变化特性。为了改变其单一尺度的特性,使得角点提取更加精确和有效,将多尺度的概念和图像分块方法引入到Harris算法中,在多个尺度下对角点进行提取。将每个尺度上的角点响应值的本地最大值作为该尺度上的候选角点,并同时对图像进行分块;最后,沿小尺度到大尺度方向判断候选角点是否是真实角点,剔除伪角点,使得角点检测更加精确。通过对比实验,新算法明显地提高了图像角点的检测性能。

Harris corner detection is a classical algorithm and is widely used nowadays,but it does not have the invariant property.For modifying its single-scale and making corner detection more accurate and valid,the conception of multi-scale-space and image block method were introduced into the Harris algorithm in this paper.Corner detection was used to corner extraction by multi-scale-space.Under each scale,the maximum response of local corner was regarded as a candidate corner point,while the image was segmented.Finally,along the direction of small-scale to large-scale to judge whether the candidate corner was true,the false corner was eliminated,thus the corner detection was more accurate.By comparison test,the new algorithm significantly improves the image corner detection performance.