基于小波的多尺度SUSAN角点检测

SUSAN角点检测是一种经典的角点检测算法,但不具有尺变化特性.利用小波变换的多尺度特性,即在小尺度下的定位准确和大尺度下的抗噪性强,该文把多分辨分析的思想引入到该算法中,从而构建了一种新的基于小波变换的SUSAN多尺度角点检测算法.新的角点检测可以在不同的尺度下获取角点,克服了单一尺度的SUSAN角点检测可能存在的角点位置偏移和易受噪声干扰而提取出伪角点等问题.为了综合利用各个尺度下的角点信息,该文提出了由粗到细的角点筛选方案.通过对比实验,新算法明显地提高了图像角点检测性能.

SUSAN Multi-scale Corner Detection Based on Wavelet Transform

SUSAN comer detection is a classical algorithm, but has not the property of scale change. Using multi-scale characteristics of wavelet Transform, positioning in small-scale is accurate and anti-noise in large-scale is strong. The multi-resolution idea is introduced into the SUSAN algorithm in this paper. Then, a novel SUSAN multi-scale corner detection algorithm based on wavelet transform is presented, which might detect the comers in different scales and overcome the drawback that the single-scale SUSAN detector usually leads to detecting false comers due to noise. For comprehensive utilization of various scales comer information, comer screening programs from coarseness to fine is presented. Compared with SUSAN algorithm, the presented algorithm is more efficient in detecting the comers with accurate location and is robust to noise.