Scale invariant feature transform mismatches removal based on canonical correlation analysis

doi:10.11772/j.issn.1001-9081.2015.11.3308

Journal of Computer Applications ›› 2015, Vol. 35 ›› Issue (11): 3308-3311.DOI: 10.11772/j.issn.1001-9081.2015.11.3308

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Scale invariant feature transform mismatches removal based on canonical correlation analysis

ZHAO Wei¹, TIAN Zheng^1,2, YANG Lijuan¹, YAN Weidong¹, WEN Jinhuan¹

1. School of Science, Northwestern Polytechnical University, Xi'an Shaanxi 710129, China;
2. State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China

Received:2015-05-18 Revised:2015-07-22 Online:2015-11-13

基于典型相关分析方法的尺度不变特征变换误匹配剔除

赵伟¹, 田铮^1,2, 杨丽娟¹, 延伟东¹, 温金环¹

1. 西北工业大学理学院, 西安 710129;
2. 中国科学院遥感与数字地球研究所遥感科学国家重点实验室, 北京 100101

通讯作者: 赵伟(1987-),男,山东聊城人,博士研究生,主要研究方向:遥感图像处理.
作者简介:田铮(1946-),女,陕西西安人,教授,主要研究方向:遥感图像处理、时间序列分析; 杨丽娟(1987-),女,河南开封人,博士研究生,主要研究方向:遥感图像处理; 延伟东(1979-),男,河北唐山人,副教授,博士,主要研究方向:遥感图像处理; 温金环(1974-),女,陕西西安人,讲师,博士,主要研究方向:遥感图像处理.
基金资助:
国家自然科学基金资助项目(60972150,61201323,61301196);陕西省自然科学基础研究计划项目(2014JQ5189);遥感科学国家重点实验室开放基金资助项目(OFSLRSS201206).

Abstract

Abstract: A method to remove Scale Invariant Feature Transform (SIFT) mismatches based on Canonical Correlation Analysis (CCA) was presented to improve the quality of feature matching when the feature points locate in some similar structures of one image. At first, SIFT matching algorithm was used to get the initial matching pairs. Then, a line was fitted based on the linear relation between the canonical correlation components. The mismatches were removed by thresholding the distances from the points to the line. Furthermore, the influence of each remained match on the collineartiy degree was analyzed to indicate false matches. The experimental results show that the proposed algorithm can remove mismatches efficiently and keep more correct matches.

Key words: mismatch, image registration, Scale Invariant Feature Transform (SIFT), RANdom Sample Consensus (RANSAC) algorithm, canonical correlation analysis

摘要： 针对尺度不变特征变换(SIFT)描述子仅利用特征点的局部邻域灰度信息而对图像内具有相似灰度分布的特征点易产生误匹配的问题,提出一种基于典型相关分析(CCA)的SIFT误匹配剔除方法.该方法首先利用SIFT算法进行匹配,得到初始匹配对; 然后根据典型相关成分的线性关系拟合直线,利用点到直线的距离剔除大部分误匹配点对; 对剩余的匹配点对,逐一分析其对典型相关成分的共线性的影响,剔除影响程度大的特征点对.实验结果表明,该方法能够在剔除误匹配的同时保留更多的正确匹配,提高了图像配准的精度.

关键词: 误匹配, 图像配准, 尺度不变特征变换, 随机采样一致性算法, 典型相关分析

CLC Number:

TP751.1

ZHAO Wei, TIAN Zheng, YANG Lijuan, YAN Weidong, WEN Jinhuan. Scale invariant feature transform mismatches removal based on canonical correlation analysis[J]. Journal of Computer Applications, 2015, 35(11): 3308-3311.

赵伟, 田铮, 杨丽娟, 延伟东, 温金环. 基于典型相关分析方法的尺度不变特征变换误匹配剔除[J]. 计算机应用, 2015, 35(11): 3308-3311.

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Scale invariant feature transform mismatches removal based on canonical correlation analysis

基于典型相关分析方法的尺度不变特征变换误匹配剔除

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