摘要
图像匹配技术被广泛用于人脸识别、全景图像生成等领域。该文利用变比不变特征点(Scale Invariance FeatureTransform-SIFT)提取方法提取特征点,并对SIFT方法提取出的特征点用最近邻算法(Nearest Neighbor-NN)进行匹配,在搜索最近邻特征点和次近邻特征点时使用了在K-D树搜索算法基础上进行改进的搜索算法BBF(Best Bin First)算法。实验证明该匹配算法具有匹配精度高,鲁棒性好的特点。
Image matching technology has been widely used for face recognition, building panorama. This paper uses SIFT （Scale Invariance Feature Transform） as feature extraction method. After that, the paper uses NN （Nearest Neighbor） for feature matching. In the period of searching nearest and second nearest feature point, the paper uses BBF （Best Bin First） as searching method which is modified from K-D tree searching method. This experiment proved that it is of high accuracy and robustness.
出处
《国外电子测量技术》
2008年第1期3-4,15,共3页
Foreign Electronic Measurement Technology
基金
许昌学院青年资金项目
关键词
图像
特征点
匹配
最近邻算法
image
feature
registration
nearest neighbor
