摘要
为了能在强噪声条件下实现对弱目标的亚像素定位,提出了一种基于改进小波变换和Zernike矩的亚像素边缘检测算法,该方法首先结合小波变换和小波模极大值原理,将边缘点由粗到细地搜索出来,实现边缘的准确、有效定位,然后再用改进后的Zernike矩算子对图像进行亚像素边缘提取。对工程应用中带有噪声图像的处理结果表明,该方法抗噪能力比较强,且定位精度比较高,在提取效果上明显优于经典边缘检测算法,是一种稳定可靠的图像边缘检测算法。
In order to locate weak target with sub-pixel precision under strong noise condition, a sub-pixel edge detection algorithm was proposed based on improved wavelet transform and Zernike moments. Firstly, wavelet transform and modulus maxima were combined to search the edge points ＂from coarse to fine＂ to achieve accurate and effective locating, then sub-pixel edge points were detected by the improved Zernike moments. The method was used for dealing with images with noise in actual engineering application. The experimental result showed that the algorithm has stronger anti-noise capability and higher locating accuracy, and it is a stable and reliable algorithm with better performance over that of the traditional edge detection algorithms.
出处
《电光与控制》
北大核心
2015年第9期50-54,共5页
Electronics Optics & Control
基金
统计数据驱动的剩余寿命预测若干关键问题研究(61174030)
关键词
边缘检测
图像处理
多尺度分析
小波变换
模极大值
edge detection
image processing
multi-scale analysis
wavelet transform
modulus maxima
