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dc.contributor.author卢清薇
dc.contributor.author罗旌钰
dc.contributor.author王云峰
dc.date.accessioned2018-11-26T08:55:24Z
dc.date.available2018-11-26T08:55:24Z
dc.date.issued2017
dc.identifier.citation软件,2017,(12)
dc.identifier.issn1003-6970
dc.identifier.other10.3969/j.issn.1003-6970.2017.12.028
dc.identifier.urihttps://dspace.xmu.edu.cn/handle/2288/165730
dc.description.abstractSURF(Speed-up robust features)算法进行图像特征点匹配时需要循环遍历待匹配图像所有特征点,计算特征点之间的SURF64描述距离,耗时大。本文对SURF算法进行了16维与4维的降维研究。实验结果表明,16维SURF算法性能与64维SURF算法基本相当,但能大幅度降低运算时间;4维运算性能降低较大,不能用于特征点匹配,但4维SUFR描述算法可以扩展到图像的各个像素点,用于ICP算法及图像的稠密匹配。
dc.description.abstractMatching image feature point by SURF(Speed-up robust features) algorithm needs to loop through all feature points on the image to be matched, and compute the sixty-four dimensional distance of SURF between fea-ture points, which will take a long computation time. The sixteen dimensional SURF and four SURF are imple-mented. The experimental results show that the performance of the sixteen dimensional SURF is almost the same as that of the sixty-four dimensional SURF, moreover the sixteen dimensional SURF takes less time than sixty-four dimensional SURF. The performance of four dimensional SURF is much inferior to that of sixty-four dimensional SURF, so it cannot used to match feature point. However the four dimensional description of SURF can be ex-panded to all feature points, which can be applied in ICP algorithm and dense matching algorithm.
dc.language.isozh_CN
dc.subject图像匹配
dc.subject特征点
dc.subjectSURF算法
dc.subject降维
dc.subjectDimensional reduction
dc.titleSURF算法的降维研究
dc.title.alternativeResearch on Dimensional Reduction for SURF Algorithm
dc.typeArticle


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