[1]唐坤,韩斌.一种自适应搜索范围的SIFT特征点快速匹配算法[J].智能系统学报,2014,9(6):723-728.[doi:10.3969/j.issn.1673-4785.201309037]
TANG Kun,HAN Bin.A new fast algorithm of self-adaptive search scope for SIFT matching[J].CAAI Transactions on Intelligent Systems,2014,9(6):723-728.[doi:10.3969/j.issn.1673-4785.201309037]
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TANG Kun,HAN Bin.A new fast algorithm of self-adaptive search scope for SIFT matching[J].CAAI Transactions on Intelligent Systems,2014,9(6):723-728.[doi:10.3969/j.issn.1673-4785.201309037]
一种自适应搜索范围的SIFT特征点快速匹配算法
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
9
期数:
2014年第6期
页码:
723-728
栏目:
学术论文—机器学习
出版日期:
2014-12-25
- Title:
- A new fast algorithm of self-adaptive search scope for SIFT matching
- Keywords:
- SIFT; Auto ARV&; DP; feature points matching; search scope; BBF
- 分类号:
- TP319
- 文献标志码:
- A
- 摘要:
- 针对SIFT特征向量匹配时间成本高的问题,提出了一种自适应搜索范围的快速匹配算法-AutoARV&DP。该算法首先根据特征向量集合计算一个合适的参考向量,然后自适应确定一个搜索范围,最后在一个通过距离过滤后的较小搜索空间中进行特征向量匹配。实验结果表明,与经典的BBF算法相比较,AutoARV&DP在获得满意匹配效果的同时,能够有效地降低SIFT特征点匹配的时间成本。
- Abstract:
- Aiming at the high time cost of feature vectors matching in scale invariant feature transform(SIFT), a fast algorithm, called auto angle of reference vector & distance of point(Auto ARV&DP), of self-adaptive search scope for SIFT matching is put forward. Firstly, an appropriate reference vector is computed based on the feature vectors set. Secondly, a self-adaptive search scope is determined by this reference vector. Finally, the matching of SIFT is performed in a small feature vectors set, which is filtered by Norm. Experimental results showed that compared with the classical best bin first(BBF) algorithm, Auto ARV&DP can effectively decrease the time cost of feature vector matching of SIFT with no loss of matching performance when the size of feature vector set is large.
- 参考文献/References:
-
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[12] 吴伟交,王敏等. 基于向量夹角的SIFT特征点匹配算法[J]. 模式识别与人工智能, 2013, 26(1): 123-127.WU Weijiao, WANG Min. SIFT feature matching algorithm based on vector angle[J]. Pattern Recognition and Artificial Intelligence(PR&AI), 2013, 26(1): 123-127.
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备注/Memo
收稿日期:2013-9-11;改回日期:。
作者简介:唐坤,男,1988年生,硕士研究生,主要研究方向为数字图像处理,人工智能;韩斌,男,1968年生,教授,博士,主要研究方向为数字图像处理、智能检测、并行计算。
通讯作者:唐坤.E-mail:tkpaperzc@sina.cn.
更新日期/Last Update:
2015-06-16