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基于曲率尺度空间的轮廓角点检测 被引量:15

Contour Corner Detection Based on Curvature Scale Space
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摘要 在曲率尺度空间和多尺度曲率积的基础上,提出了一种基于多尺度曲率多项式的角点检测算法。首先基于曲率尺度空间检测不同尺度下的角点,然后利用多尺度曲率积或曲率和对检测到的角点进行增强处理,该方法可有效抑制噪声影响,防止高尺度下对一些角点平滑;另外,根据多尺度曲率多项式结果的符号还可有效的判别所检测角点的凹凸性。采用不同的评价准则及实例图像进行测试,实验结果证明该角点检测器是非常有效的,优于文中其它两种检测算法。 A novel algorithm for detecting corners is presented based on Curvature Scale Space (CSS) and Multi-scale Curvature Product (MSCP). Firstly, the comers of an image are detected at different curvature scale space. Then, a multi-scale curvature polynomial is defined as the sum or multiplication of the curvature of the contour at each scale. The new method can not only enhance curvature extreme peaks effectively, but also suppress noise and prevent smoothing some corners with augment of the scale. In addition, the concavity and convexity of detected comers can be judged by the result sign of the curvature polynomial. Experiment results show that the new method is more effective in corner detection than the other algorithms mentioned in the paper.
出处 《光电工程》 CAS CSCD 北大核心 2009年第7期78-82,共5页 Opto-Electronic Engineering
基金 河南省教育厅自然科学基础研究基金(2007520019 2008B520012 2009B520013) 河南省国际合作项目(084300510065) 苏州大学江苏省计算机信息处理技术重点实验室开放基金(KJS0715) 河南理工大学博士基金(B050901) 河南理工大学骨干教师资助基金
关键词 角点检测 曲率尺度空间 多尺度曲率多项式 计算机视觉 模式识别 corner detection curvature scale space multi-scale curvature polynomial computer vision pattern recognition
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