摘要
提出基于特征场景的快速图像匹配方法,一定程度上解决了基于主流的局部特征匹配算法无法描述全局特征的问题。通过采集场景图像,使用主成分分析(PCA)重构特征场景,进而用于匹配范围划分;在划分后的匹配范围中使用SURF算法进行快速局部特征匹配。实验结果表明,此方法结合大尺度全局特征和尺度不变局部特征,使近似目标的区分能力得到了加强。在鲁棒性和时效性上,此方法达到了较好的平衡,拓展了主流局部特征匹配方法的应用范围。最后提出了对本方法的改进方向,表明了此方法的可拓展性。
In this paper,a new method based on eigen-scene for fast image matching was presented,and it could solve the problem that mainstream matching algorithm with local features cannot describe the global features.By extracting scene images,the eigen-scene using Principal Component Analysis（PCA） could be reconstructed,and then it could be used for matching scope division.In the divided matching scope,SURF algorithm was used to match fast local features.The experimental results indicate this method combines the large scale global features and scale invariant local features so that the discrimination ability is enhanced for similar object.The robustness and timeliness of this method achieve good balance so that it extends the application fields of mainstream matching method with local features.In the end,improvement direction is suggested for this paper,which shows the scalability of this method.
出处
《计算机应用》
CSCD
北大核心
2011年第7期1850-1852,共3页
journal of Computer Applications
基金
住房和城乡建设部软科学研究项目(2010-K9-43)
吉林省教育厅"十一五"科学技术研究项目(吉教科合字[2010]第303号)
关键词
特征场景
主成分分析
全局特征
SURF
匹配范围划分
eigen-scene
Principal Component Analysis（PCA）
global feature
Speeded-Up Robust Features（SURF）
matching scope division
