期刊文献+

高维图像数据的最优表达 被引量:2

The Greatest Expression of High Dimensional Image
下载PDF
导出
摘要 对于M×N维图像数据,提出了一种用M维和N维向量表达的方式,这种方式使得图像处理可以在较低维数的空间中进行,便于计算。同时在一定意义下,这种表达是最优的。证明了在图像采样点数趋于无穷时,就相当于文献的结果。给出了这一方法的应用实例。 A novel method is proposed by which a M×N dimensional image can be expressed with Mand N dimensional vectors. Therefore, the image can be processed in the lower dimension space and it is easy to calculate. In certain sense this expression is optimum. We prove that it is identical to the result in the literature when the hits go to infinite. At last the applications verify the theoretical result.
出处 《国防科技大学学报》 EI CAS CSCD 北大核心 2003年第5期85-89,共5页 Journal of National University of Defense Technology
基金 国家863基金资助项目(2001AA35040) 国家自然科学基金资助项目(60003013)
关键词 高维图像数据 最优表达 M维 N维 向量表达 high dimensional image greatest expression
  • 相关文献

参考文献4

  • 1宋健.高维函数和流形在低维可视空间中的最优表达[J].科学通报,2001,46(12):977-984. 被引量:16
  • 2Bracewell R N.The Fourier Transform and its Applications[J]. McGraw - Hill, New York, 1986.
  • 3Ingrid Daubechies.Ten Lectures On Wavelets[M].The Society for Industrial and Applies Mathematics,1992.
  • 4Castleman K R. Digital Image Processing[ M]. Prentice - Hall, 1996.

二级参考文献2

  • 1Lang S,Real and Functional Analysis,1990年,70页
  • 2关肇直,泛函分析讲义,1958年,143-149,274-293页

共引文献15

同被引文献18

  • 1谭璐,易东云,吴翊,袁伟.基于非线性降维的图像识别[J].计算机工程,2005,31(13):54-55. 被引量:4
  • 2孟爱国,章登勇,陈志坚,李峰.基于小波包变换和支持向量机的虹膜识别方法[J].计算机工程与设计,2006,27(10):1769-1771. 被引量:2
  • 3胡永刚,吴翊,卜江.基于加权PCA的声音指纹降维技术[J].计算机应用,2006,26(9):2250-2254. 被引量:5
  • 4John Daugman,How iris recognition works[J].IEEE Transactions on Circuits and Systems for Video Technology,2004,14:21-30.
  • 5Daugman J.The importance of being random:Statistical principles of iris recognition[J].Pattem Recognition,2003,36(2):279- 291.
  • 6MaLi,Tan Tieniu,Wang Yunhong,et al.Efficient iris recognition by characterizing key local variation[J].IEEE Transaction on Image Processing,2004,13:739-750.
  • 7Wildes P.Iris recognition emerging biometric technology[J].Proceeding of IEEE, 1997(5): 1347-1363.
  • 8Mikhail Belkin,Partha Niyogi.Laplacian eigenmaps for dimensionality reduction and data representation[J].Neural Computation,2003,15(6): 1373-1396.
  • 9Mikhail Belkin,Partha Niyogi.Laplacian eigenmaps and spectral techniques for embedding and clustering[C].Vancouver, Bdtish Columbia, Canada:Advances in Neural Information Processing Systems,2001.
  • 10中科院虹膜图像库CASIA iris image database(version2.O)[EB/OL].http://www.sinobiometrics.com.

引证文献2

二级引证文献2

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