韩超,方露,章盛.一种优化的图像配准算法[J].电子测量与仪器学报,2017,31(2):178-184
一种优化的图像配准算法
An optimized image registration algorithm
  
DOI:10.13382/j.jemi.2017.02.003
中文关键词:  SIFT算法  Trajkovic算法  稀疏降维  图像配准  双向匹配
英文关键词:SIFT algorithm  Trajkovic algorithm  sparse dimension reduction  image registration  bidirectional matching
基金项目:安徽省自然科学基金(1508085MF121)、安徽省高校自然科学研究项目(KJ2016A056)、省级专业综合改革项目(2016zy013)、安徽检测技术与节能装置省级重点实验室开放基金(1506c085002)资助项目
作者单位
韩超 安徽工程大学电气工程学院芜湖241000 
方露 安徽工程大学电气工程学院芜湖241000 
章盛 安徽工程大学电气工程学院芜湖241000 
AuthorInstitution
Han Chao College of Electrical Engineering, Anhui Polytechnic University, Wuhu 241000,China 
Fang Lu College of Electrical Engineering, Anhui Polytechnic University, Wuhu 241000,China 
Zhang Sheng College of Electrical Engineering, Anhui Polytechnic University, Wuhu 241000,China 
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中文摘要:
      为了降低传统尺度不变特征变换(SIFT)算法的特征点检测与匹配的时间复杂度,提出一种优化的图像配准算法,即采用Trajkovic算法检测特征点,并采用SIFT算法的分配描述符方法分配特征点描述符参数,再用稀疏降维原理对特征点描述符参数进行降维处理,最后,采用基于双向匹配的相似性度量算法进行特征点匹配。模拟实验选择检测图像的特征点数、匹配对数、正确匹配对数、匹配正确率、配准时间与配准时间下降率6个指标作为评估标准,结果表明,优化算法在特征点配准正确率方面与传统SIFT算法相当,但在特征点配准速度方面有明显提升。
英文摘要:
      To reduce the time complexity of the traditional SIFT algorithm related to the feature points detection and matching, an optimized image registration algorithm is proposed. Namely the feature points are extracted by Trajkovic algorithm, and the SIFT algorithm distribution descriptor method is adopted to allocate feature points descriptor parameters. Then sparse dimension reduction principle is used to reduce the feature points descriptor dimensions. Finally, the similarity measure algorithm based on bidirectional matching is used to match them. Six evaluation criteria including the feature points detection of image, matching pair, correct matching pair, matching accuracy, registration time and registration time drop rate are adopted in the simulation experiments. The simulation results show that the optimization algorithm in comparison with traditional SIFT algorithm is equivalent in the accuracy of feature point matching. However, it has obvious improvement in terms of feature points matching speed.
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