摘要
为提高角点检测算法的定位精度和对噪声的鲁棒性,提出了基于多尺度弦角尖锐度累积的自适应角点检测算子。首先,利用Canny算法快速提取图像边缘轮廓;然后,划分轮廓支撑域并将其作为尺度,分别计算3个尺度下的弦角尖锐度均值,并将其累积作为角点响应函数;最后,根据每条轮廓各自的自适应阈值标记角点。实验结果表明,与现有的角点检测算法相比,该算法提高了噪声图像和模糊图像上角点的定位精度和抗噪声能力,并具有自适应性。
We propose a new adaptive corner detector based on multi-scale chord-angle sharpness accu-mulation, which can reduce location error and detects fine accuracy on noisy images. Firstly, we use the canny detector to detect edges at low computational cost. Secondly, we devise support regions of the contour into three sections as scales and computes the chord-angel sharpness respectively, then ac- cumulate the three scale sharpness as corner response function. Finally, we use an dynamic adaptive corner threshold to label corners. The results on fine and low quality images show that the proposed algorithm performs better than the other three algorithms in terms of both detection accuracy and lo- cation error.
出处
《武汉大学学报（信息科学版）》
EI
CSCD
北大核心
2015年第5期617-622,627,共7页
Geomatics and Information Science of Wuhan University
基金
湖北省自然科学基金资助项目(2015CFB602)
冶金工业过程系统科学湖北省重点实验室资助项目(Y201413)
国家973计划资助项目(2011CB707904)~~
关键词
角点检测
噪声图像
弦角尖锐度累积
自适应阈值
corner detector
noisy image
chord-angel sharpness accumulation
adaptive threshold
