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中文摘要：
为了提高谱匹配算法对噪声和出格点的鲁棒性，提出一种基于谱图理论的结构描述子，并在此基础上结合几何相容性给出了匹配目标函数的定义及相应求解算法．首先给出一种利用特征谱与谱隙序列的统计量构造的结构描述子，以获得定长的特征点属性表示；然后结合邻近关系表示的几何相容性定义了求解匹配问题的目标函数，将匹配问题转化为一对一约束下的优化问题；最后介绍了利用概率松弛对匹配目标函数的求解方法．在模拟数据与真实图像上的比较实验结果均表明该算法具有相对较高的准确性．
英文摘要：
In order to improve the robustness of spectral correspondence algorithm for noise and outlier, a structural descriptor based on spectral graph theory is proposed, and the matching objective function combined with geometric consistency is given as well as its solving algorithm. Firstly, a structural descriptor is proposed by utilizing the statistic of graph spectra and spectral gap, consequently the attribute representation of feature point with fixed length is obtained. Secondly, an objective function is defined by combining geometric consistency represented by neighborhood relationship, and then the matching problem is formulated as an optimization problem with one-to-one correspondence constraints. Finally, the solution to the defined objective function is given by using probabilistic relaxation. Comparative experiments applied to both synthetic data and real-world images validate that our method can achieve higher matching accuracy.
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