期刊文献+

基于邻域信息的红外与可见光图像互信息配准 被引量:4

Infrared and Visible Light Image Mutual Information Registration Based on Neighborhood Information
下载PDF
导出
摘要 基于互信息的配准方法以其自动化程度高和配准精度高的优点被广泛应用在图像配准中。文中针对红外与可见光图像配准中采用传统的互信息,仅考虑图像像素的灰度信息,而没有考虑图像像素之间空间信息的情况,提出了一种基于邻域信息的互信息配准算法。该算法充分利用图像像素之间的空间信息,在互信息计算中图像中每个像素的灰度值由其邻域像素的灰度值按照距离分配不同的权值共同得到。实验表明该算法使配准曲线更加光滑,配准过程中极值更易找出,提高了配准精度和抗噪能力。 Mutual information which as an effective measure used in image registration. Standard mutual information used in the infrared and visible light image registration only considers the pixels intensity value but ignores their spatial information. In this paper give a new algorithm based on neighborhood infarmation. Every pixel is determined by neighborhood pixel according to weight value by distance in process of the mutual information calculation. The experiment results show that the algorithm makes full use of spatial information, smoothes the registration curve, can easily find extreme value, achieves a high accuracy, and has good performance against noise.
出处 《计算机技术与发展》 2008年第10期151-154,共4页 Computer Technology and Development
关键词 图像配准 红外 邻域信息 互信息 image registration IR neighborhood information mutual information
  • 相关文献

参考文献5

  • 1Collignon A,Maes F, Delaere D, et al. Automated multimodality medical image registration using information theory [ C ]//Proc 14th Int Cord Information Processing ha Medical Imaging ( IPMI,95). lle de Be.rder, France: IEEE Press, 1995 : 263 - 274.
  • 2Josien P, Antoine J, Max V. Im- age registration by maximization of combined mutual information and gradient information[J ]. IEEE Trans on Medical Image, 2000,19 (8) : 809 - 814.
  • 3Rueckert D,Clarkson M J,HiU D L G,et al. Non- rigid registration using higher- order mutual information [ C]//Proe SPIE Medical Imaging 2000: Image Processing. San Diego, CA: [s. n. ] ,2000:438 - 447.
  • 4Studhokne C, Hill D L G, Hawkes D J. An overlap invariant entropy measures of 3D medical image alignment[J]. Pattern Recognition, 1999,32(1) :71 - 86.
  • 5姜晓彤,罗立民,赵正旭.一种改进的基于互信息和梯度特征的图像配准方法的研究[J].仪器仪表学报,2006,27(9):1141-1146. 被引量:20

二级参考文献16

  • 1JOSIEN P, ANTOINE J, MAX V. Image registration by maximization of combined mutual information and gradient information[J]. IEEE Trans on Medical Image, 2000,19 (8):809.
  • 2MAES F, COLLIGNON A, VANDERMEULEN D,et al. Multimodality image registration by maximization of mutual information[J]. IEEE Transactions on Medical Imaging, 1997,16(2): 187-198.
  • 3CIDECIYAN A V, JACOBSON S G, KEMP C M, et al. Registration of high resolution images of the retina[C]. In.. Proceedings of SPIE, Medical Imaging Ⅵ.Image Processing, 1992,1562: 310-312.
  • 4MEYER C, BOES J L, KIM B, et al. Demonstration of accuracy and clinical versatility of mutual information for automatic multimodality image fusion using affine and thin plate spline-warped geometric deformations[J]. Medical Image Analysis, 1997, 1 (3):195-206.
  • 5WELLS W, VIOLA P, ASTUMI H, et al. Multimodal volume registration by maximization of mutual information[J]. Medical Image Analysis, 1996,1 (1) :35-51.
  • 6RITTER N, OWENS R, COPPER J, et al. Registration of stereo and temporal images of the retina[J].IEEE Transactions on Medical Imaging, 1999,18 (5):404-418.
  • 7ROUET J M, JACQ J J, ROUX C. Genetic algorithms for a robust 3D MR CT registration[J]. IEEE Transactions on Information Technology in Biomedicine, 2000,4(2) : 126-136.
  • 8YAMANY S, AHMED M, FARAG A. A new genetic-based technique for matching 3D curves and surfaces[J]. Pattern Recognition, 1999,32 : 1817-1820.
  • 9WEST J, FITZPATRICK J M, WAND M Y, et al.Comparison and evaluation of retrospective intermodality brain image registration techniques[J]. Journal of Computer Assisted Tomography, 1997, 21 ( 4 ):554-566.
  • 10PLUIM J, MAINTZ J, VIERGEVER M. Image registration by maximization of combined mutual information and gradient information[J]. IEEE Transactions on medical imaging, 2000,19 (8):809-814.

共引文献19

同被引文献44

引证文献4

二级引证文献8

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