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)
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.