| Harris角点检测的优化算法 |
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| 引用本文： | 洪改艳,芮廷先,俞伟广,何士产,王天召.Harris角点检测的优化算法[J].计算机系统应用,2017,26(4):169-172. |
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| 作者姓名： | 洪改艳 芮廷先 俞伟广 何士产 王天召 |
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| 作者单位： | 上海财经大学 浙江学院经济与信息管理系, 金华 321000,上海财经大学 信息管理与工程学院, 上海 200000,上海财经大学 浙江学院经济与信息管理系, 金华 321000,上海财经大学 浙江学院经济与信息管理系, 金华 321000,解放军73051部队, 金华 321000 |
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| 摘 要： | 针对Harris角点检测算法中提取出较多的伪角点和计算量大的问题，提出了一种基于Harris角点检测的改进算法. 为抑制Harris角点检测中的伪角点数目并且提高算法的效率，首先加入预筛选得到候选角点，在计算水平和垂直方向梯度时，对于梯度较小的像素点进行预处理，在进行非极大值抑制时采用自适应阈值，提高算法自适应性，最后利用USAN对角点进行进一步选择. 实验结果表明，改进的Harris角点检测算法不仅提高了检测精度和效率，而且对噪声具有一定的鲁棒性.
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| 关 键 词： | Harris角点 角点预选 自适应阈值 USAN |
| 收稿时间： | 2016/7/18 0:00:00 |
| 修稿时间： | 2016/8/8 0:00:00 |
Improved Algorithm Based on Harris Corner Detection |
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| HONG Gai-Yan,RUI Ting-Xian,YU Wei-Guang,HE Shi-Chan and WANG Tian-Zhao.Improved Algorithm Based on Harris Corner Detection[J].Computer Systems& Applications,2017,26(4):169-172. |
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| Authors: | HONG Gai-Yan RUI Ting-Xian YU Wei-Guang HE Shi-Chan and WANG Tian-Zhao |
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| Affiliation: | Department of Economics and Information Management, Shanghai University of Finance and Economics Zhejiang College, Jinhua 321000, China,School of Information Management and Engineering, Shanghai University of Finance and Economics, Shanghai 200000, China,Department of Economics and Information Management, Shanghai University of Finance and Economics Zhejiang College, Jinhua 321000, China,Department of Economics and Information Management, Shanghai University of Finance and Economics Zhejiang College, Jinhua 321000, China and No.73051 of PLA, Jinhua 321000, China |
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| Abstract: | According to the problems of extracting more false corners and large computation problems in harris corner detection, this paper proposes an improved algorithm based on harris corner detection. In order to reduce the number of false corners and improve the algorithm efficiency, a pre-selection strategy is embedded to pick out potential corners before the normal routine. When calculate the horizontal and vertical gradients, the pixels with smaller horizontal and vertical gradients are pre-processed. In order to improve the auto-adaptive of algorithm, an auto-adaptive threshold is adopted, finally using USAN to make further selection. The result shows that the improved algorithm not only can improve the precision and efficiency of corner detection, but also is robust to noise to some extent. |
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| Keywords: | Harris corner corner pre-selection auto-adaptive threshold USAN |
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