期刊文献+

基于模糊控制的ASIFT图像特征优化算法 被引量:2

Affine SIFT Feature Optimization Algorithm Based on Fuzzy Control
原文传递
导出
摘要 ASIFT算法是图像特征匹配的有效工具,具有很强的仿射不变性。在ASIFT算法的基础上,利用Nelder-Mead单纯形方法优化采样点,并通过模糊控制策略实现单纯形参数的自适应调整。针对一组低空遥感图像的实验结果表明,本文方法保持了ASIFT算法对仿射变换的鲁棒性,并且能获得比ASIFT和SIFT算法更多的特征匹配对。 Affine SIFT is an efficient tool for image matching, which has been proven to be invariant to affine distortion. To acquire more matches between reference image and input image, this paper used Nelder-Mead Simplex to optimize the best affine transformation based on transforms acquired by ASIFT. Moreover, fuzzy control has been employed to construct an adaptive parameter tuning strategy of Simplex. Experiments conducted remote sensing images show that the proposed algorithm is also invariant to affine distortion. Furthermore, the proposed method achieved promising results since we acquired more correct matches than ASIFT and SIFT.
出处 《模糊系统与数学》 CSCD 北大核心 2012年第5期147-153,共7页 Fuzzy Systems and Mathematics
基金 国家自然科学基金资助项目(61202143 61103052) 高等学校博士学科点基金资助项目(20090121110032) 深圳市科技项目(JC200903180630A ZYB200907110169A) 福建省产学重大项目(2011H6020) 福建省自然科学基金资助项目(2011J01013) 福建省教育厅项目(JA10196)
关键词 SIFT特征 仿射变换 Nelder-Mead单纯形法 模糊控制 SIFT Feature Affine Transform Nelder-Mead Simplex Fuzzy Control
  • 相关文献

参考文献17

  • 1Faugeras O. Three-dimensional computer vision : A geometric viewpoint [M]. MIT Press,1993.
  • 2Bardera A,Feixas M,Boada 1,Sbert M. Image registration by compression[J]. Information Sciences,2010,180(7):1121-1133.
  • 3Rajwade A,Banerjee A,Rangarajan A. Probability density estimation using isocontours and isosurfaces: Applicationto information-theoretic image registration [J]. IEEE Transactions on PAMI,2009,31(3) :475-492.
  • 4周海芳,杜云飞,杨学军,李思昆.基于互信息的遥感图像区域配准并行算法的研究与实现[J].中国图象图形学报,2010,15(1):174-180. 被引量:15
  • 5Le Moigne J. An automated parallel image registration technique based on the correlation of wavelet features [J].IEEE Transaction on Geoscience and Remote Sensing,2002,40(8) : 1849-1864.
  • 6Chen H* Varshney P K,M. Arora M K. Performance of mutualinformation similarity measure for registration ofmulti-temporal remote sensing images [J]. IEEE Transaction on Geoscience and Remote Sensing, 2003 .40(11): 2445-2454.
  • 7Hong G,Yun H. Combination of feature-based and area-based image registration technique for high resolution remotesensing image[Z]. IEEE International Symposium on Geoscience and Remote Sensing,2007:377^380.
  • 8Moravec H P. Towards automatic visual obstacle avoidance [C]// Proceedings of the 5th International JointConference on Artificial Intelligence,1977:584-590.
  • 9Harris C,Stephens M A. Combined corner and edge detector [C]//Proceedings of Alvey Vision Conference, 1988 :189 -192.
  • 10Forstner W. A feature based correspondence algorithm for image matching[Z]. ISP Comm. Ill, 1986 : 1251--1256.

二级参考文献13

  • 1Viola P A. Alignment by Maximization of Mutual Information[ D ]. Boston, MA, USA: Massachusetts Institute of Technology, 1995.
  • 2Thevenaz P, Unser M. Optimization of mutual information for muhiresnlution image registration [J]. IEEE Transactions on Image Processing, 2000, 9 ( 12 ) : 2083-2099.
  • 3Fumihiko I, Kanrou O, Osaka H. A data distributed parallel algorithm for nonrigid image registration [ J]. Parallel Computing, 2005, 31(1 ): 19-43.
  • 4Christensen Gary E. MIMD vs. SIMD parallel processing: A case study in 3D medical image registration [ J]. Parallel Computing, 1998, 24(9-10): 1369-1383.
  • 5Warfield S K, Ferrant M, Gallez X, et al. Real-time biomechanical simulation of volumetric brain deformation for image guided neurosurgery [ C ]//Proceedings of the High Performance Networking and Computer Conference. Washington DC, USA: IEEE Computer Society,2000,230: 1-16.
  • 6Wachowiak M P, Peters T M. High performance medical image registration using new optimization techniques [ J ]. IEEE Transactions on hrformation Technology in Biomedicine, 2006, 10(2) : 344-353.
  • 7Kybic J, Unser M. Fast parametric elastic image registration [ J]. IEEE Transactions on Image Processing, 2003, 12 ( 11 ) : 1427- 1442.
  • 8Heldmann S, Mahnke O, Ports D, et al. Fast computation of Mutual Information in a variational image registration approach [C]//Tolxdorff T, et al. Bidverarbeitung far die Medizin 2004. Berlin: Springer,2004,116: 448-452.
  • 9Rueckert D, Clarkson M J, Hill D L G, et al. Non-rigid registration using higher-order mutual information [ J ]. Proceeding of SPIE, 2000, 3979 (2) : 438-447.
  • 10Rohlfing T, Maurer C R. Nonrigid image registration in sharedmemory multiprocessor environments with application to brains, breasts, and bees [ J]. IEEE Transactions on Information technology in Biomedicine, 2003, 7( 1 ) : 16-25.

共引文献14

同被引文献14

引证文献2

二级引证文献22

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