期刊文献+

基于多尺度FAST-9的图像快速匹配算法 被引量:7

Fast Image Matching Algorithm Based on Multi-scale FAST-9
下载PDF
导出
摘要 FAST-9检测子不具备尺度不变性,为此,提出一种基于多尺度FAST-9的图像快速匹配算法。对图像建立高斯尺度空间,在各图层上应用FAST-9检测子分别提取特征点,在其周围建立圆形区域并分配主方向,同时建立方形区域构造SURF描述子,利用基于最近邻匹配方法进行匹配。实验结果表明,与SURF、SIFT算法相比,该算法具有较高的匹配速度。 FAST-9 detector can not be invariant to scale changes.Aiming at this problem,this paper proposes a fast image matching algorithm based on multi-scale FAST-9.Feature points are extracted from the constructed Gaussian scale space by the FAST-9 detector,and main orientation is assigned based on information from a circular region around the interest point.SURF descriptor is extracted by a constructed square region aligned to the selected orientation.The matching pairs are determined with the nearest neighbor distance matching method.Experimental results show that the proposed algorithm outperforms better than SURF and SIFT algorithm in speed.
出处 《计算机工程》 CAS CSCD 2012年第12期208-210,217,共4页 Computer Engineering
基金 国家自然科学基金资助项目(60772151 61075025)
关键词 FAST-9检测子 SURF算法 局部不变特征 图像匹配 SIFT算法 高斯尺度空间 FAST-9 detector SURF algorithm local invariant feature image matching SIFT algorithm Gaussian scale space
  • 相关文献

参考文献8

  • 1Lowe D G.Object Recognition from Local Scale-invariant Fea-ture[C]//Proceedings of ICCV’99.Kerkyra,Greece:IEEEComputer Society,1999:1150-1157.
  • 2程德志,李言俊,余瑞星.基于改进SIFT算法的图像匹配方法[J].计算机仿真,2011,28(7):285-289. 被引量:50
  • 3Bay H,Tuytelaars T,van Gool L.SURF:Speeded Up RobustFeatures[C]//Proceedings of the 9th European Conference onComputer Vision.Graz,Austria:[s.n.],2006:404-417.
  • 4Smith S M,Brady J M,SUSAN——A New Approach to LowLevel Image Processing[J].International Journal of ComputerVision,1997,23(34):45-78.
  • 5Rosten E,Drummond T.Fusing Points and Lines for HighPerformance Tracking[C]//Proceedings of ICCV’05.Beijing,China:[s.n.],2005:1508-1511.
  • 6Rosten E,Drummond T.Machine Learning for High-speed CornerDetection[C]//Proceedings of the 9th European Conference onComputer Vision.Graz,Austria:[s.n.],2006:430-443.
  • 7Quinlan J R.Induction of Decision Trees[J].Machine Learning,1986,1(1):81-106.
  • 8Lindeberg T.Scale-space for Discrete Signals[J].IEEE Trans.onPattern Analysis and Machine Intelligence,1990,12(3):234-254.

二级参考文献3

  • 1D G Lowe. Distinctive image features from scale invariant keypoints [ J]. International Journal of Computer Vision, 2004,60(2) : 91- 110.
  • 2Cordeliaschmid, Rogermohr. Loeal Gray value Invaiiants for Image Retrieval [ J ]. Pattern Analysis and Maehine Intelligenee, IEEE Transactions, VOlumel9, 15-sues May 1997. 530-535.
  • 3王曼,王魁生.一种改进的遗传算法在图像匹配技术中的应用[J].计算机仿真,2008,25(3):86-89. 被引量:4

共引文献49

同被引文献77

  • 1袁远松,赵小敏,杨东勇.DataMatrix条码的畸变校正[J].计算机系统应用,2008,17(10):47-50. 被引量:5
  • 2徐亦斌,王敬东,李鹏.基于圆投影向量的景象匹配方法研究[J].系统工程与电子技术,2005,27(10):1725-1728. 被引量:17
  • 3赵文彬,张艳宁.角点检测技术综述[J].计算机应用研究,2006,23(10):17-19. 被引量:85
  • 4张小洪,李博,杨丹.一种新的Harris多尺度角点检测[J].电子与信息学报,2007,29(7):1735-1738. 被引量:79
  • 5Bay H,Tuytelaars T,Van Gool L.SURF:Speeded up Robust Features[J].Computer Vision and Image Understanding,2008,110(3):346-359.
  • 6Zhang Huijuan,Hu Qiong.Fast Image Matching Based-on Improved SURF Algorithm [C]// International Conference on Elec-tronics,Communications and Control(ICECC),2011:1460-1463.
  • 7Chen L,Chua T S.A match and tiling approach to content-basedvideo retrieval[C]∥Int.Conf.on Multimedia and Expo.2001:417-420.
  • 8Kashino K,Kurozumi T,Murase H.A quick search method for audio and video signals based on histogram pruning[J].IEEE Transactions on Multimedia,2003,5(3):348-357.
  • 9Chiu C,Wang H.A novel video matching framework for copy detection[C]∥Proc.of the 21th IPPR Conference on Computer Vision,Graphics and Image Processing (CVGIP’2008).2008.
  • 10Myers C S,Rabiner L R.A comparative study of several dynamictime-warping algorithms for connected word recognition[J].The Bell System Technical Journal,1981,60(7):1389-1409.

引证文献7

二级引证文献47

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部