吉林大学学报(信息科学版)

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基于概率密度分割的特征约束角点匹配方法

刘元琳, 段锦, 祝勇, 张茂峰, 张海洋   

  1. 长春理工大学 电子信息工程学院, 长春 130022
  • 收稿日期:2014-01-06 出版日期:2014-07-24 发布日期:2014-12-18
  • 作者简介:刘元琳(1989—), 女, 吉林白山人, 长春理工大学硕士研究生, 主要从事数字视频与图像处理技术研究, (Tel)86-13504422089(E-mail)yiran891001@163.com;通讯作者: 段锦(1971—), 男, 长春人, 长春理工大学教授, 博士生导师, 主要从事图像处理、 目标识别及视景仿真研究, (Tel)86-13514494485(E-mail)duanjin@vip.sina.com。

Characteristics Constrained Corner Matching Method Based on Probability Density Segmentation

LIU Yuanlin, DUAN Jin, ZHU Yong, ZHANG Maofeng, ZHANG Haiyang   

  1. College of Electrical and Information Engineering, Changchun University of Science and Technology, Changchun 130022, China
  • Received:2014-01-06 Online:2014-07-24 Published:2014-12-18

摘要:

为克服Harris算子特征点匹配的角点群聚现象, 提出了一种基于概率密度的角点匹配算法。该方法将角点间的图像距离作为基本区域划分的主要参考系数, 利用划分区域的角点概率密度减少匹配区域, 然后将区域外的特征点判定为伪角点并将其去除。实验表明, 该改进算法的匹配结果有效地减少了干扰点, 从而提高了算法的实时性和准确性。

关键词: 均匀化, 区域划分, 目标定位, 角点检测, Harris算子, 概率密度

Abstract:

To overcome corner clustering phenomenon of Harris operator feature point matching, we proposed a probability density\|based corner matching algorithm. This method sets the image distance between the corner as a basic reference coefficient of the main regional division, using the corner probability density of regional division to reduce the matching area, and judging the feature points outside the region as false corners and removing them. Experiments show that the matching results of improved algorithm effectively reduce interference points, improving the timeliness and accuracy of the algorithm.

Key words: corner detection, Harris operator, probability density, uniform, regional segmentation, target location

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