摘要
常规的角点检测方法通常只考虑曲率极大值的局部特性,且在单一尺度下进行,易受噪声的影响,造成角点的漏检。为了克服这些缺陷,本文采用将多尺度分析的非线性复扩散处理方法与边缘点曲率极值的局部和全局特性相结合进行角点检测的方法。首先对图像进行保护边缘的非线性复扩散,以获取不同尺度的图像信息;然后针对不同尺度下图像的实部和虚部,进行基于全局和局部特性的角点检测,除考虑角点曲率极值的局部特性外,还将其与邻近点曲率的关系作为全局特性加以比较,最终确定角点。实验结果表明,本文方法可以有效地去除噪声的干扰,提取的角点数目多,避免漏检,位置准确。
The common corner detecting methods only consider the local properties such as extremum curvature value,and are performed with single-scale images,Which often leads to missing true corners, and they are apt to be interfered by noise. In order to overcome these drawbacks, a new synthetical corner detecting＇method combing multi-scale analysis method based on nonlinear complex diffusion and global and local curvature properties is proposed. In this method, multi-scale scales images can be obtained by applying nonlinear complex diffusion which can preserve the edges very well. Then comers are detected according to not only its local curvature properties, but also the relationship between neighboring points in the contours. The experiments show that this method can get rid of noise disturbance,and detect more and accurate corners than original methods, and can be widely used in image registration and segmentation.
出处
《光电子．激光》
EI
CAS
CSCD
北大核心
2012年第2期396-401,共6页
Journal of Optoelectronics·Laser
基金
国家自然科学基金(30870666)
山东省科技发展计划(2010GF10243)
山东大学创新基金(2009JC004)资助项目
关键词
角点检测
多尺度分析
非线性扩散
复扩散
corner detecting
multi-scale analysis
nonlinear diffusion
complex diffusion
