Contourlet-SIFT特征匹配算法
doi: 10.3724/SP.J.1146.2012.01132
Contourlet-SIFT Feature Matching Algorithm
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摘要: 基于局部特征的匹配算法中SIFT(Scale Invariant Feature Transform)算法性能好,应用广泛,但其描述子的维度高、匹配耗时大,对局部相似区域的匹配鲁棒性差。为此,该文提出一种Contourlet-SIFT特征匹配算法。在尺度空间下提取旋转不变特征,对特征及其邻域进行Contourlet变换,由各方向子带分解系数的均值和标准差构建全局纹理描述向量,根据向量间欧氏距离的大小进行特征点排序,选取距离较小的前1%的特征再进行SIFT最近邻比值匹配。实验结果表明该算法对亮度差异大、相似区域多的图像的匹配性能优于SIFT,在保证尺度、旋转、视角等不变性与SIFT相当的同时,匹配速度大为提升。
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关键词:
- 图像处理 /
- 特征匹配 /
- 尺度不变特征变换 /
- Contourlet变换 /
- 全局纹理信息
Abstract: The Scale Invariant Feature Transform (SIFT) has a fine algorithm performance and an extensive application to the matching algorithm of local features, but its descriptor is characterized by a high dimension and huge time consumption also gives rise to a low matching robustness when tackling similar areas. Therefore this paper puts forward an innovative Contourlet-SIFT feature matching algorithm. The SIFT key points are first extracted to conduct Contourlet transformation on peripheral areas in order to calculate the mean and standard deviation of the decomposition coefficient in each direction. Then the vector of overall texture description is constructed and the Euclidean distance of this low-dimensional vector provides references for prioritizing the matching pairs. The first 1% key points will be subject to the nearest ratio matching by the SIFT vector. The result proves that the new algorithm surpasses SIFT especially when addressing the images with great brightness difference and many similar areas. It can lift the matching speed while it parallels SIFT in its invariability of scale, rotation and visual angle.
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