Rejecting Mismatches of IR/Visual Image by Support Vector Regression

ZHANG Jiao; LI Jun-shan; ZHU Ying-hong; ZHU Qiu-xu

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Rejecting Mismatches of IR/Visual Image by Support Vector Regression

更新时间：2026-09-04

- Rejecting Mismatches of IR/Visual Image by Support Vector Regression
- Vol. 30, Issue 2, Pages: 38-41(2013)
- 作者机构：
  
  1. 1 第二炮兵工程大学,陕西,西安,710025
  2. 2 武警福建总队厦门支队,福建,厦门,361000
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2013
- 稿件说明：
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张姣, 李俊山, 朱英宏, 朱秋旭. SVR在异源图像误匹配点对剔除中的应用[J]. 微电子学与计算机, 2013, 30(2): 38-41.

ZHANG Jiao, LI Jun-shan, ZHU Ying-hong, et al. Rejecting Mismatches of IR/Visual Image by Support Vector Regression[J]. 2013, 30(2): 38-41.
张姣, 李俊山, 朱英宏, 朱秋旭. SVR在异源图像误匹配点对剔除中的应用[J]. 微电子学与计算机, 2013, 30(2): 38-41. DOI：

ZHANG Jiao, LI Jun-shan, ZHU Ying-hong, et al. Rejecting Mismatches of IR/Visual Image by Support Vector Regression[J]. 2013, 30(2): 38-41. DOI：

摘要

针对红外和可见光图像匹配算法中普遍存在正确匹配率低的问题

提出了一种基于支持向量回归(sup-port vector regression

SVR)的误匹配点剔除算法.算法在已知特征匹配点对的基础上

将点坐标作为SVR的训练样本;通过SVR建立回归模型

拟合匹配点对的坐标映射函数;最后根据映射函数判定匹配点对的正确性

实现误匹配点对的剔除.实验表明

本文算法对于误匹配点的判定与剔除具有明显的效果.与随机抽样一致性算法相比

能够在不损失正确匹配的前提下有效减少误匹配对

具有较高的正确率.

Abstract

To resolve the problem of low correct matching rate in IR/visual image matching

an improved approach to reject mismatches based on support vector regression(SVR) is proposed.Supposing that the pairs of control points were known

the method applied SVR to estimate the point correspondence function;then based on the function to judge whether matches are right or not.Experiment results indicate that the proposed method is a feasible approach for rejecting mismatches.Compared with RANSAC

it can improve correct matching rate under the condition of remaining the original right matching.

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