摘要
为能在噪声图像中准确地找到边缘的亚像素坐标,提出了一种基于小波多尺度积和改进Zernike算法的边缘亚像素坐标提取方法。首先,用多尺度小波变换处理图像,充分发挥大、小尺度各自的优势,结合多尺度乘积的特性,提取噪声图像的像素级坐标。在此基础上,使用改进的Zernike方法提取亚像素坐标。2组对比试验表明,该方法能准确地提取噪声图像的亚像素边缘坐标,较Zernike算法抗噪能力强,提取的边缘坐标准确度高。
In order to find subpixel edge coordinate accurately in noise image, a subpixel coordinates extraction method is proposed based on wavelet muhiscale edge algorithms and improved Zernike. Firstly, the image is processed by the method of muhiscale wavelet, making full use of large and small-scale advantages, combining with characteristics of the multiscale product and extracting pixel coordinates of edge. Secondly, subpixel edge points are detected by the mothod of Zernike. Two sets of comparative test shows that this method can accurately extract subpixel edge coordinates of noise image. Compared with the Zernik algorithm, it has higher measurement accuracy and stronger anti-noise performance.
出处
《北京信息科技大学学报（自然科学版）》
2013年第4期29-34,共6页
Journal of Beijing Information Science and Technology University
基金
国家自然科学基金资助项目(11172047
11072038)
北京市属高等学校人才强教深化计划资助项目(PHR201106131)
关键词
小波变换
多尺度积
亚像素
边缘提取
抗噪性
wavelet transform
muhiscale product
subpixel
edge detection
noise immunity
