Image Match Method Based on Improved SIFT Algorithm

LIU Hui; SHEN Hai-long

Chinese

您当前的位置：

首页 >

文章列表页 >

Image Match Method Based on Improved SIFT Algorithm

更新时间：2026-09-04

- Image Match Method Based on Improved SIFT Algorithm
- Vol. 31, Issue 1, Pages: 38-42(2014)
- 作者机构：
  
  1. 1 重庆邮电大学机构通信新技术应用研究所,重庆,400065
  2. 2 重庆信科设计有限公司,重庆,400065
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2014
- 稿件说明：
移动端阅览
刘辉, 申海龙. 一种基于改进SIFT算法的图像配准方法[J]. 微电子学与计算机, 2014, 31(1): 38-42.

LIU Hui, SHEN Hai-long. Image Match Method Based on Improved SIFT Algorithm[J]. 2014, 31(1): 38-42.
刘辉, 申海龙. 一种基于改进SIFT算法的图像配准方法[J]. 微电子学与计算机, 2014, 31(1): 38-42. DOI：

LIU Hui, SHEN Hai-long. Image Match Method Based on Improved SIFT Algorithm[J]. 2014, 31(1): 38-42. DOI：

摘要

给出一种改进的SIFT算法，并将该算法应用到图像配准中.首先，该改进算法结合Canny算子，去除影响匹配的边缘点.然后采用最近邻与次近邻之比来对96维描述子进行匹配.最后，采用随机抽样一致性（RANSAC）方法消除误匹配.实验结果表明，与原SIFT算法相比，该算法能够有效提高图像配准确度，并减少了10%左右的运算时间.

Abstract

This paper presented a improved SIFT algorithm

and applied this algorithm in image matching.Firstly

integrated the Canny algorithm into the improved algorithm

excluding the edge points.Then

ratio between the first and the second closest distance is used to match the 96-dimensional vectors.Finally

eliminating the inaccurate matching by using RANSAC method.The experimental results show that

compared with the original SIFT algorithm

the modified algorithm can effectively improve the precision of image matching

and the matching time is decreased about 15%.

关键词

Keywords

references

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Microservices anomaly detection based on the correlation of monitoring metrics

Aerial small object detection algorithm based on improved RT-DETR

Steganography model guided by attention mechanism and improved dense convolution

Research on assembly action recognition based on MSS-YOLO-MPHands fusion architecture

Research on the design of blockchain-based AI-generated content traceability framework

Related Author

No data

Related Institution

No data

AI问答

Postal code：100079
Tel：（010）53879206 Email：tmw@bjxintong.com.cn
Technical support is provided by Beijing Founder electronics co., LTD 京ICP备09082226号-64 京公网安备11010602201714号
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰