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基于SIFT的特征点提取的算法改进 被引量:2

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摘要 SIFT作为当今图像处理领域特征点描述相当热门的算法之一,被学者专家普遍研究并改进,已诞生了一系列的变种算法。然而随着计算机视觉、人工智能等领域的不断发展,实时性的需求日益增强,在保证准确度的同时提高算法效率成为研究的热点之一。文章主要研究并实现SIFT算法及其改进算法PCA-SIFT、SURF,并比较各算法的优劣。
出处 《信息通信》 2016年第2期66-67,共2页 Information & Communications
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参考文献5

  • 1Ke Y,Sukthankar R.PCA-SIFT:A more distinctive representation for local image descriptors. Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition . 2004
  • 2Mikolajczyk K,Schmid C.A performance evaluation of local descriptors. IEEE Transactions on Pattern Analysis and Machine Intelligence . 2005
  • 3David G. Lowe.Distinctive Image Features from Scale-Invariant Keypoints[J]. International Journal of Computer Vision . 2004 (2)
  • 4张羽,朱丹,王玉良.一种改进的快速SIFT特征匹配算法[J].微计算机信息,2008,24(33):220-222. 被引量:17
  • 5李忠海,李申,崔建国,刘罗曼.基于快速SIFT特征提取的模板匹配算法[J].计算机工程,2011,37(24):222-224. 被引量:9

二级参考文献13

  • 1David G Lowe. Distinctive Image Features from Scale - Invariant Interest Points.International Journal of Computer Vision, 2004, 60 (2), 91-110.
  • 2Michael Grabner, Helmut Grabner, and Horst Bischof. Fast approximated SIFT. Asian Conference on Computer Vision,Hyderabad ,India, 2006, 918-927.
  • 3Paul Viola , Michael Jones. Rapid object detection using a boosted cascade of simple features. Computer Vision and Pattern Recognition.2001, Volume Ⅰ, 511 ┝518.
  • 4Fatih Porikli. Integral histogram: A fast way to extract histograms in cartesian spaces. Computer Vision and Pattern Recognition,2005, Volume 1,829-836.
  • 5Martin A.Fishchler, Robert C.Bolles. Random Sample Consensus: a paradigm for model fitting with application to image analysis and automated cartography.Communication Association Machine, 1981,24(6), 381-395
  • 6Lowe D. Distinctive Image Features from Scale-invariant Key- points[J]. International Journal on Computer Vision, 2004, 60(2): 91-110.
  • 7Mikolajczyk K, Schmid C. A Performance Evaluation of Local Descriptors[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 27(10): 1615-1630.
  • 8Yan Ke, Sukthankar R. PCA-SIFT: A More Distinctive Representation for Local Imagedescriptors[C] //Proc. of IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Washington D. C., USA: [s. n.] , 2004.
  • 9宰小涛,赵宇明.基于SIFT特征描述子的立体匹配算法[J].微计算机信息,2007(24):285-287. 被引量:25
  • 10刘立,彭复员,赵坤,万亚平.采用简化SIFT算法实现快速图像匹配[J].红外与激光工程,2008,37(1):181-184. 被引量:92

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