利用局部方向微分向量一致性的角点检测

王富平; 水鹏朗

doi:10.3788/OPE.20152312.3509

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利用局部方向微分向量一致性的角点检测

信息科学 | 更新时间：2020-08-13

- 利用局部方向微分向量一致性的角点检测
- Corner detection via consistency of local directional differential vectors
- 光学精密工程 2015年23卷第12期页码：3509-3518
- 作者机构：
  
  西安电子科技大学, 雷达信号处理国家重点实验室,陕西西安,710071
- 作者简介：
- 基金信息：
  
  国家自然科学基金资助项目(No. 61271295).()
- DOI：10.3788/OPE.20152312.3509
  中图分类号： TN973.3;TP391
- 修回日期：2015-12-01，
  
  纸质出版日期：2015-12-25
- 稿件说明：
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王富平, 水鹏朗,. 利用局部方向微分向量一致性的角点检测[J]. 光学精密工程, 2015,23(12): 3509-3518

WANG Fu-ping, SHUI Peng-lang,. Corner detection via consistency of local directional differential vectors[J]. Editorial Office of Optics and Precision Engineering, 2015,23(12): 3509-3518
王富平, 水鹏朗,. 利用局部方向微分向量一致性的角点检测[J]. 光学精密工程, 2015,23(12): 3509-3518 DOI： 10.3788/OPE.20152312.3509.

WANG Fu-ping, SHUI Peng-lang,. Corner detection via consistency of local directional differential vectors[J]. Editorial Office of Optics and Precision Engineering, 2015,23(12): 3509-3518 DOI： 10.3788/OPE.20152312.3509.

摘要

在边缘轮廓提取的基础上

提出了一种利用局部方向微分向量一致性的角点检测算法以消除边缘噪声对角点检测产生的不利影响。该算法提取图像的边缘轮廓来降低算法计算量;利用各向异性高斯方向导数(ANDD)滤波器提取每个像素处的方向微分向量并进行幂次变换

以增强向量的各向异性;进而利用相邻像素的方向微分向量构建一致性测度。最后

对同一轮廓上的一致性测度进行均值归一化

得到最终角点测度。实验显示

提出算法的平均角点定位误差为1.52 pixel

与对比算法接近;检测准确率分别比点到弦距离累积(CPDA)法、相对局部曲率(He&Yung)法提高了58%和5.5%

与归一化残余面积(RA)算法相等

同时角点错检率比He&Yung和RA少25.5%和21.6%。提出的算法能准确地检测出真实角点

并具有更小的错误检测率

更高的角点重复率

而且对边缘噪声十分鲁棒。

Abstract

On the basis of edge contour extraction

a new corner detection algorithm via the consistency of local directional differential vectors of pixels was proposed to eliminate the adverse effects of the edge noise on the corner detection. In the algorithm

the edge contour of an image was extracted to reduce the computation. Then

the Anisotropic Gaussian Directional Derivative( ANDD) filters were used to extract the directional differential vectors at each pixel on contours and the power transformation was employed to the vectors to enhance their anisotropy. Furthermore

the consistency was constructed from the directional differential vectors of adjacent pixels. Finally

the consistency was normalized by the average consistency on the same contour to produce the final corner measure. The experimental results show that the proposed algorithm achieves the average localization error of 1.52 pixel approximately to comparison algorithms. Also the detection accuracy is improved by 58% and 5.5% as compared with those of the Chord-to-point Distance Accumulation (CPDA) algorithm and relative local curvature (He&Yung ) algorithm and it is equal to that of the Residual Area (RA) algorithm. Meanwhile the false detection ratio is 25.5%

21.6% lower than those of the He & Yung and RA algorithms

respectively. The proposed algorithm accurately detects the true corners in the image and holds a smaller false detection ratio and higher corner repeatability

and it is very robust to the edge noise.

关键词

Keywords

references

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