摘要
基于图像特征点的匹配算法广泛应用于图像检索,目标检测、识别等图像处理领域。针对特征匹配算法召回率较低的问题,提出了一种基于归一化互相关函数网格的统计优化特征匹配算法,将匹配主方向差和匹配尺度比引入特征匹配过程中,综合利用SIFT(scale invariant feature transform)特征点的主方向、尺度和位置等约束在网格框架下加速匹配位置的求解,优化特征匹配结果,提高匹配召回率和综合匹配性能。首先在目标图中寻找原图每个特征点的最近邻匹配特征点,得到初匹配结果;其次利用匹配主方向差剔除初匹配中的大部分误匹配,然后基于匹配尺度比信息对匹配图像划分网格,统计匹配特征点的位置信息在网格间的分布情况,最后计算原图中每个网格的归一化互相关函数以判断该网格内的匹配是否正确,得到优化后的特征匹配结果。实验结果表明,新算法的匹配准确率在与经典特征匹配算法相当的基础上将匹配召回率平均提高了10%以上,获得了更好的综合匹配性能。
The matching algorithm based on image feature points is widely used in image retrieval, target detection, identification and other image processing fields. Aiming at the problem that the feature matching algorithm has low recall rate, a statistical optimization feature based on grid of the normalized cross correlation function is proposed. The matching main direction difference and scale ratio are introduced to feature matching process, for comprehensively utilizing SIFT feature points' information, such as the main direction, scale and position constrains, to accelerate the solution of matching position constraint under the grid framework, which optimizes the feature matching results and improves the recall rate and comprehensive match performance. Firstly, the nearest neighbor matching feature points of each feature point in the original image are found in the target image, and the initial matching results are obtained. Secondly, the matching main direction difference is used to eliminate most mismatches of the initial matching. Thirdly, the matching images are meshed based on the matching scale ratio information, and the position information of the matching feature points distributed among the grids is gathered statistics. Finally, the normalized cross correlation function of each small grid in the original image is calculated to determine whether the matching in the small grid is correct, and the optimized feature matching results are obtained. The experimental results show that the matching accuracy of the new algorithm is similar to that of classical feature matching algorithms, meanwhile the matching recall rate is increased by more than 10%, and a better comprehensive matching performance is obtained.
作者
赵春晖
樊斌
胡劲文
张志远
潘泉
ZHAO Chunhui;FAN Bin;HU Jinwen;ZHANG Zhiyuan;PAN Quan(School of Automation, Northwestern Polytechnical University, Xi′an 710072, China)
出处
《西北工业大学学报》
EI
CAS
CSCD
北大核心
2019年第2期249-257,共9页
Journal of Northwestern Polytechnical University
基金
国家自然科学基金(61473230
61603303
61790552
61790554)
陕西省自然科学基金(2017JM6027
2017JQ6005)资助
关键词
特征匹配
网格法
匹配召回率
SIFT
归一化互相关
feature matching
grid method
matching recall rate
SIFT
normalized cross correlation
