期刊文献+

基于ORB的快速完全仿射不变图像匹配 被引量:15

Fast fully affine invariant image matching based on ORB
在线阅读 下载PDF
导出
摘要 ASIFT具有完全仿射不变性,但计算耗时;ORB实时性好,但仿射不变性差。为了在图像匹配中同时兼顾仿射不变性和实时性,利用模拟相机在不同视点下成像的手段使得ORB具备完全仿射不变性,进而提出了一种基于ORB的快速完全仿射不变图像匹配新算法(AORB)。首先通过模拟相机在不同视点下成像以获取模拟的图像,然后用快速的ORB算法对所有模拟的图像对进行匹配,最终取得完全仿射不变性。实验结果表明,该算法能够满足完全仿射不变图像匹配需求,并且相比基于OpenMP的ASIFT计算速度提高了约6倍。 Affine-SIFT is fully affine invariant but its computation is time-consuming.Oriented FAST and Rotated BRIEF(ORB)is extremely fast but is not affine invariant.In order to solve the problem that it is difficult to balance the good affine invariance and the real-time performance in image matching,a new fast method(AORB,Affine-ORB)for fully affine invariant image matching based on ORB,which applies the ASIFT method simulated in all views to obtain the full affine invariance of ORB,is proposed.Firstly,it simulates enough image views obtainable by varying the viewpoints of camera.Secondly,all simulated image pairs are matched using fast ORB.Thus,the full affine invariance is obtained.Experimental results show that proposed method is efficient in fully affine invariant image matching and it is 6times faster than ASIFT.
出处 《计算机工程与科学》 CSCD 北大核心 2014年第2期303-310,共8页 Computer Engineering & Science
基金 国家自然科学基金资助项目(61105031)
关键词 ORB ASIFT Affine-SIFT 仿射不变 图像匹配 ORB ASIFT affine-SIFT affine invariant image matching
  • 相关文献

参考文献13

  • 1Mikolajczyk K,Schmid C.Scale and affine invariant interest point detectors[J].International Journal of Computer Vision,2004,60(1):63-86.
  • 2Matas J,Chum O,Urban M,et al.Robust wide baseline stereo from maximally stable extremal regions[C]// Proc of the British Machine Vision Conference,2002:384-393.
  • 3Mikolajczyk K,Tuytelaars T,Schmid C,et al.A comparison of affine region detectors[J].International Journal of Computer Vision,2005,65(1):43-72.
  • 4Morel J M,Yu G.ASIFT:A new framework for fully affine invariant image comparison[J].SIAM Journal on Imaging Sciences,2009,2(2):438-469.
  • 5Lowe D.Distinctive image features from scale-invariant keypoints[J].International Journal of Computer Vision,2004,60(2):91-110.
  • 6Rublee E,Rabaud V,Konolige K,et al.ORB:An efficient alternative to SIFT or SURF[C]//Proc of 2011 IEEE International Conference on Computer Vision (ICCV 2011),2011:2564-2571.
  • 7Rosten E,Porter R,Drummond T.Faster and better:A machine learning approach to corner detection[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2010,32(1):105-119.
  • 8Calonder M,Lepetit V,Strecha C,et al.BRIEF:Binary robust independent elementary features[C]//Proc of European Conference on Computer Vision,2010:1.
  • 9Calonder M,Lepetit V,Ozuysal M,et al.BRIEF:Computing a local binary descriptor very fast[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2012,34 (7):1281-1298.
  • 10Rosin P L.Measuring corner properties[J].Computer Vision and Image Understanding,1999,73(2):291-307.

同被引文献76

  • 1童宇,蔡自兴.基于特征匹配的全景图的生成[J].华中科技大学学报(自然科学版),2004,32(S1):77-79. 被引量:2
  • 2Rosin P L. Measuring comer properties [ J ]. Computer Vision and Image Understanding, 1999,73 ( 2 ) : 291 - 307.
  • 3Sattler T, Leibe B, Kobbelt L. SCRAMSAC: improving RANSAC's efficiency with a spatial consistency filter [ C]///Proceedings of the IEEE International Conference on Computer Vision. Piscataway: IEEE, 2009: 2090- 2097.
  • 4Rublee E, Rabaud V, Konolige K, et al. ORB: an effi- cient alternative to SIFT or SURF [ C ] JJ Proceedings of the IEEE International Conference on Computer Vision. Piscataway : IEEE, 2011:2564 - 2571.
  • 5Mair E, Hager G D, Burschka D, et al. Adaptive and generic corner detection based on the accelerated segment test [ C ] //J Proceedings of European Conference on Computer Vision, Crete, Greece,Sep 5-11, 2010,63 (12) : 183-196.
  • 6Calonder M, Lepetit V, Fua P. BRIEF: Binary Robust Independent Elementary Features [ C ]// Proceedings of European Conference on Computer Vision, Crete, Greece, Sep 5 - 11, 2010, 63(14) :778-792.
  • 7Rosten E,Drummond T. Machine learning for high-speed corner detection[ M]. Springer, 2006:25-36.
  • 8智金波.基于局部特征点的图像配准算法及应用研究[D].北京:北京印刷学院,2015.
  • 9LOWE D G. Object recognition from local scale-invariant tatures [ C]// Proceedings of the 1999 IEEE International Conference on Computer Vision. Piscataway, N J: IEEE, 1999:1150-1157.
  • 10BAY H, ESS A, TUYTELAARS T, et al. SURF: speeded up ro- bust feature [ J]. Computer Vision and Image Undeltanding, 2008, 110(3): 346-359.

引证文献15

二级引证文献116

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