摘要
为解决尺度不变特征变换(SIFT)算法在图像发生旋转和尺度变化时产生的错误匹配问题,提出一种新的算法。根据SIFT提取的关键点信息,利用正确匹配点对间的旋转不变因子和尺度不变因子来剔除SIFT误匹配点,然后对保留下来的特征点进行聚类分析,对目标图像进行识别判断,并通过实验将该算法与双向匹配算法和随机抽样一致性算法(RANSAC)进行比较。实验结果表明,该算法能够有效地剔除误匹配点,且误剔除率低。剔除误匹配点后再进行图像检索,图像的漏检率和误检率都大大地降低了。
A new algorithm was put forward to solve SIFT mismatching problem that was caused by the image rotation and scale changes. It used the image rotation-invariant and scale-invariant factors of the right matching points to eliminate the mismatching points with the key information of SIFT extraction. Then it analyzed the reserved feature points by clustering to recognize the target images and was compared with the bidirectional matching algorithm and RANSAC algorithm through the experiments. The experiment results showed that this algorithm can effectively eliminate the false matching points and the false rejection rate is low. Images are retrieved after the mismatching points excluded, and the miss rate and the false detection rate of the images are greatly reduced.
出处
《红外技术》
CSCD
北大核心
2015年第7期560-565,共6页
Infrared Technology
基金
文件检验鉴定公安部重点实验室(中国刑事警察学院)资助项目
编号:11KFKT002
关键词
尺度不变特征变换
转不变因子
尺度不变因子
误剔除率
漏检率
误检率
SIFT, rotation-invariant factor, scale-invariant factor, false rejection rate, miss rate, false detection rate
