计算机工程

• 图形图像处理 • 上一篇    下一篇

用于图像匹配的改进Harris特征点检测算法

扈立超1,史再峰1,庞科1,刘江明1,曹清洁1,2   

  1. (1.天津大学电子信息工程学院,天津 300072; 2.天津师范大学数学科学学院,天津 300387)
  • 收稿日期:2014-11-03 出版日期:2015-10-15 发布日期:2015-10-15
  • 作者简介:扈立超(1988-),男,硕士研究生,主研方向:数字图像处理;史再峰,讲师;庞科,博士研究生;刘江明,硕士研究生;曹清洁,博士研究生。
  • 基金项目:
    国家“863”计划基金资助项目(2012AA012705);国家国际科技合作专项基金资助项目(2012DFB10170)。

Improved Harris Feature Point Detection Algorithm for Image Matching

HU Lichao  1,SHI Zaifeng  1,PANG Ke  1,LIU Jiangming  1,CAO Qingjie  1,2   

  1. (1.School of Electronic and Information Engineering,Tianjin University,Tianjin 300072,China; 2.School of Mathematical Sciences,Tianjin Normal University,Tianjin 300387,China)
  • Received:2014-11-03 Online:2015-10-15 Published:2015-10-15

摘要: 原始Harris特征点检测算法采用高斯滤波进行平滑处理,增强了其鲁棒性,但是也提高了该算法的复杂度,导致其不能应用到许多图像匹配系统中,还存在对T型和斜T型特征点定位不准确的问题。为此,提出一种新的特征点检测算法。使用加速分割测试特征的特征点检测原理排除大量的非特征点,利用邻域像素比较法消除部分强干扰点,采用改进的高效非极大值抑制算法获得结果特征点。实验结果表明,该算法具有较好的匹配精度和较快的检测速度,检测时间仅为原始Harris算法的13.9%,适用于实时图像匹配系统。

关键词: 机器视觉, 图像匹配, 特征点检测, Harris算法, 非极大值抑制

Abstract: By using Gaussian filtering for smooth processing,the original Harris feature point detection algorithm enhances its robustness.But it also increases the complexity of the algorithm which can not be applied to many image matching systems.Its positioning accuracy of T-type and diagonal T-type feature points is low.In order to solve the above problems,a new feature point detection algorithm is proposed.Amounts of non-feature points are excluded by using the principle of Features from Accelerated Segment Test(FAST) feature point detection.Some strong interference points are ruled out by using neighborhood pixels comparison method.The resulting feature points are obtained by using the improved efficient non-maximum suppression algorithm.Experimental results demonstrate that the improved algorithm has better matching accuracy and higher detection speed,its detection time is only approximately 13.9% that of the original Harris algorithm and it is quite suitable for real-time image matching systems.

Key words: machine vision, image matching, feature point detection, Harris algorithm, non-maximum suppression

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