Spectral matching algorithm based on nonsubsampled contourlet transform and scale-invariant feature transform  

作　　者：Dong Liang Pu Yan Ming Zhu Yizheng Fan Kui Wang 

机构地区：[1]Key Lab of Intelligent Computing ＆ Signal Processing, Ministry of Education, Anhui University, Hefei 230039, E R. China [2]School of Electronics and Information Engineering, Anhui University, Hefei 230039, P. R. China

出　　处：《Journal of Systems Engineering and Electronics》2012年第3期453-459,共7页系统工程与电子技术（英文版）

基　　金：supported by the National Natural Science Foundation of China (61172127;11071002);the Specialized Research Fund for the Doctoral Program of Higher Education (20113401110006);the Innovative Research Team of 211 Project in Anhui University (KJTD007A)

摘　　要：A new spectral matching algorithm is proposed by us- ing nonsubsampled contourlet transform and scale-invariant fea- ture transform. The nonsubsampled contourlet transform is used to decompose an image into a low frequency image and several high frequency images, and the scale-invariant feature transform is employed to extract feature points from the low frequency im- age. A proximity matrix is constructed for the feature points of two related images. By singular value decomposition of the proximity matrix, a matching matrix （or matching result） reflecting the match- ing degree among feature points is obtained. Experimental results indicate that the proposed algorithm can reduce time complexity and possess a higher accuracy.A new spectral matching algorithm is proposed by us- ing nonsubsampled contourlet transform and scale-invariant fea- ture transform. The nonsubsampled contourlet transform is used to decompose an image into a low frequency image and several high frequency images, and the scale-invariant feature transform is employed to extract feature points from the low frequency im- age. A proximity matrix is constructed for the feature points of two related images. By singular value decomposition of the proximity matrix, a matching matrix （or matching result） reflecting the match- ing degree among feature points is obtained. Experimental results indicate that the proposed algorithm can reduce time complexity and possess a higher accuracy.

关 键 词：point pattern matching nonsubsampled contourlet transform scale-invariant feature transform spectral algorithm. 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] TN911[自动化与计算机技术—计算机科学与技术]