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 freq...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.展开更多
基金supported by the National Natural Science Foundation of China (6117212711071002)+1 种基金the Specialized Research Fund for the Doctoral Program of Higher Education (20113401110006)the Innovative Research Team of 211 Project in Anhui University (KJTD007A)
文摘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.
基金国家自然科学基金(the National Natural Science Foundation of China under Grant No.61040054No.60573724)+2 种基金辽宁省自然科学基金(No.20072197)中央高校基本科研基金(No.DC10020111)国家民委基金(No.GM200966)
文摘针对油田遥感图像在灰度有明显差异的情况下,联合位置、尺度和方向的尺度不变特征变换(PSO-SIFT)算法很难为其找到足够多的正确对应关系,且花费时间较长的问题,提出一种基于改进PSO-SIFT算法的图像匹配算法.首先采用“回”字型分块思想构建特征描述符,降低特征描述子的维度;然后使用基于全局运动建模的双边函数(BF)算法与快速样本共识(FSC)算法相结合的匹配策略,对所得的匹配对进行误匹配剔除,以增加正确匹配的数量;最后将该算法与4种同类算法及原PSO-SIFT算法进行对比.实验结果表明,该算法比同类算法精度更高,与原算法相比不仅保证了图像匹配的精度,正确匹配对数量也增加了约3倍,且匹配时间约缩短20 s.
