TY - JOUR
T1 - 基于网格的统计优化特征匹配算法
AU - Zhao, Chunhui
AU - Fan, Bin
AU - Hu, Jinwen
AU - Zhang, Zhiyuan
AU - Pan, Quan
N1 - Publisher Copyright:
© 2019 Journal of Northwestern Polytechnical University.
PY - 2019/4/1
Y1 - 2019/4/1
N2 - 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.
AB - 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.
KW - Feature matching
KW - Grid method
KW - Matching recall rate
KW - Normalized cross correlation
KW - SIFT
UR - http://www.scopus.com/inward/record.url?scp=85065838309&partnerID=8YFLogxK
U2 - 10.1051/jnwpu/20193720249
DO - 10.1051/jnwpu/20193720249
M3 - 文章
AN - SCOPUS:85065838309
SN - 1000-2758
VL - 37
SP - 249
EP - 257
JO - Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University
JF - Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University
IS - 2
ER -