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图学学报

• 视觉与图像 • 上一篇    下一篇

基于分数阶梯度算子的图像匹配算法

  

  1. 1. 东南大学自动化学院，江苏 南京 210096；
    2. 东南大学复杂工程系统测量与控制教育部重点实验室，江苏 南京 210096
  • 出版日期:2017-06-30 发布日期:2017-07-06
  • 基金资助:
    国家自然科学基金项目(61405034，51475092，51175081，61462072)

Image Matching Algorithm Based on Fractional-Order Gradient Operator

  1. 1. School of Automation, Southeast University, Nanjing Jiangsu 210096, China;
    2. Key Laboratory of Measurement and Control of CSE, Ministry of Education, Southeast University, Nanjing Jiangsu 210096, China
  • Online:2017-06-30 Published:2017-07-06

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摘要： 为保护更多的图像细节特征，提出一种基于分数阶梯度算子构建非线性尺度空间的
图像匹配算法。在非线性扩散滤波方程的传导函数中，引入分数阶梯度算子，利用快速显式扩散
(FED)算法构建非线性尺度空间，采用Leja 排序法对一个FED 周期内的时间步长重新排序，采用
一种新的基于Hessian 矩阵的关键点检测算子检测关键点，利用局部灰度序模式(LIOP)描述子
对关键点进行描述。实验结果表明，与其他经典算法相比，该算法在各种图像变换下具有更好
的性能。

关键词: 图像匹配, 分数阶梯度算子, 快速显式扩散, Leja 排序, 局部灰度序模式

Abstract: In order to protect more image detail features, an image matching algorithm based on
fractional-order gradient operator to construct nonlinear scale space was proposed. The
fractional-order gradient operator was introduced in conductivity function of classic nonlinear
diffusion equation. In this process, the recent numerical schemes called fast explicit diffusion (FED)
was used to build the nonlinear scale space. And the sequence of the FED time step sizes was
rearranged by Leja ordering. In addition, a new interest point operator from the Hessian matrix was
exploited to detect the keypoints and local intensity order pattern (LIOP) descriptor was applied in
feature description. The experimental results show that compared with the state-of-the-art algorithm,
the proposed algorithm has better performance in many geometric and photometric transformations.

Key words: image matching, fractional-order gradient operator, fast explicit diffusion, Leja ordering;
local intensity order pattern