• 中国计算机学会会刊
  • 中国科技核心期刊
  • 中文核心期刊

计算机工程与科学

• 图形与图像 • 上一篇    下一篇

混合特征下最优阈值预测的图像匹配

严春满,郝有菲,张迪,陈佳辉   

  1. (西北师范大学物理与电子工程学院,甘肃 兰州 730070)
  • 收稿日期:2018-11-22 修回日期:2019-02-27 出版日期:2019-10-25 发布日期:2019-10-25
  • 基金资助:

    国家自然科学基金(61741119);甘肃省自然科学基金(17JR5RA074,17JR5RA078)

An image matching method  based on
optimal threshold prediction under hybrid features

YAN Chun-man,HAO You-fei,ZHANG Di,CHEN Jia-hui   

  1. (College of Physics and Electronic Engineering,Northwest Normal University,Lanzhou 730070,China)
  • Received:2018-11-22 Revised:2019-02-27 Online:2019-10-25 Published:2019-10-25

摘要:

针对单一特征条件下图像匹配率较低,以及SIFT算法由于固定对比度阈值造成特征点数目提取不均的问题,提出一种混合特征下最优阈值预测的图像匹配算法。该算法首先采用SIFT算法提取图像特征点,然后利用纹理参数二阶矩自适应法得到最优阈值,并用描述性较强的纹理特征向量对SIFT匹配过程进行约束实现图像的匹配。实验结果表明,提出的算法根据图像灰度分布自适应选取对比度阈值,能够增强图像细节信息且使提取的特征点数量稳定,在匹配过程中引入纹理向量作为约束准则,避免了相似区域的误匹配,对光照和模糊图像有较好的鲁棒性。

关键词: 图像匹配, SIFT, 纹理特征, 对比度阈值

Abstract:

Aiming at the problem of low image matching rate under single feature condition, and the uneven extraction of feature points of the scale-invariant feature transform (SIFT) algorithm due to fixed contrast threshold, we propose a novel image matching method based on adaptive threshold prediction under hybrid features. Firstly, the algorithm uses the SIFT to extract image feature points. Then, we employ the texture parameter second moment method to adaptively calculate the optimal threshold, and the descriptive texture feature vector to constrain the SIFT matching process. Experimental results demonstrate that the proposed method can adaptively select the contrast threshold according to the gray level distribution of the image, enhance image detail information and stabilize the number of extracted feature points. The texture vectors constrain the matching process to avoid the mismatch of similar regions. The method is robust to illumination and blurred images.
 

Key words: image matching, SIFT, texture feature, contrast threshold