期刊文献+

各向异性小波收缩用于图像分割 被引量:5

Applying anisotropic wavelet shrinkage to image segmentation
原文传递
导出
摘要 已经证明2维情况下一般各向异性扩散与HAAR小波收缩在一定条件下是等价的,基于此等价性的各向异性小波收缩结合了小波收缩与各向异性扩散两种方法的优势。将各向异性小波收缩用于多尺度图像分割,提出一种对多尺度各向异性扩散分割方法的改进方法——多尺度各向异性小波收缩图像分割算法。该算法利用各向异性小波收缩对图像中像素灰度值进行扩散,在尽可能保持边缘的情况下,使同质区域内相邻像素灰度随尺度数增加趋于相同,构造基于尺度的空间栈,从而完成对目标的分割,是一种非监督图像分割方法。对比实验结果表明,该算法在有效处理区域内部不一致性的同时,能够准确地定位目标边缘,实现同质区域的融合,完成分割任务,且该算法收敛速度高于多尺度各向异性扩散分割方法。 The equivalence of the anisotropic diffusion and HAAR wavelet shrinkage in 2-dimension in a given condition has been proved.The anisotropic wavelet shrinkage based on the equivalence combines the merits of wavelet shrinkage and anisotropic diffusion.In this paper,applying the anisotropic wavelet shrinkage to multiresolution image segmentation is researched,one improved method to multi-resolution anisotropic diffusion image segmentation method is proposed,which is a multi-resolution anisotropic wavelet shrinkage image segmentation method.The anisotropic wavelet shrinkage is used to diffuse the pixels in the image,and the gray level of the neighbor pixels in homogenous fields tend to become the same value along with the scale' s increase while keeping the edge as far as possible.The scale space stack based on scale is built,and the segmentation to object is accomplished.It is an unsupervised method.Comparison experiments show that the method can locate the edge of the object exactly while processing the internal inconsistency of fields effectively,and the homogenous fields are merged perfectly to implement segmentation.The convergence speed of the method is faster than the multi-resolution anisotropic diffusion image segmentation method.
出处 《中国图象图形学报》 CSCD 北大核心 2010年第10期1485-1490,共6页 Journal of Image and Graphics
关键词 图像分割 尺度空间 各向异性小波收缩 image segmentation scale space anisotropic wavelet shrinkage
  • 相关文献

参考文献10

  • 1Jain A K, Farrokhnia F. Unsupervised texture segmentation using gabor filters[ J]. Pattern Recognit, 1991, 24(12) : 1167-1186.
  • 2Unser M. Texture classification and segmentation using wavelet frames[ J]. IEEE Transactions on Image Processing, 1995,4 (11) : 1549-1560.
  • 3Soo Chang Kim, Tae Jin Kang. Texture classification and segmentation using wavelet packet frame and gaussian mixture model[ J]. Pattern Reeogni, 2007, 40(4) :1207-1221.
  • 4Mumford D, Shah J. Optimal approximations by piecewise smooth functions and associated variational problems [ J ]. Commun. Pure Appl. Math, 1989, 42: 577-684.
  • 5Tony F Chan, Luminita A Vese. Active contours without edges[J]. IEEE Transactions on Image Processing, 2001,10(2) :266-277.
  • 6Ana Petrovic, Oscar Divorra Escoda, Pierre Vandergheynst. Multiresolution segmentation of natural images: from linear to nonlinear scale-space representations [ J]. IEEE Transactions on Image Processing, 2004, 13(8) :1104-1114.
  • 7朱景福,黄凤岗.二维小波收缩与各向异性扩散等价性框架及在图像去噪中的应用[J].电子与信息学报,2008,30(3):524-528. 被引量:5
  • 8Rudin L, Osher S, Fatemi E. Nonlinear total variation based noise removal algorithms [ J ]. Phys D, 1992,60 ( 1-4 ) : 259-268.
  • 9罗希平,田捷.用最大熵原则作多阈值选择的条件迭代算法[J].软件学报,2000,11(3):379-385. 被引量:24
  • 10Donoho D L. Denosing by soft thresholding [ J ]. IEEE Transactions on Information Theory, 1995,41 (3) :613-627.

二级参考文献25

共引文献27

同被引文献41

  • 1费伦科,丁振凡,汤文亮.一种小波域隔点嵌入数字水印算法[J].微计算机信息,2007,23(26):111-112. 被引量:1
  • 2杨福生.小波变换的工程分析与应用[M].北京:科学出版社,2000..
  • 3飞思科技.小波分析理论与Matlab7实现[M].北京:电子工业出版社,2005.
  • 4崔屹.图像分析与处理-数学形态学方法及应用[M].科学出版社,北京:2000,4.
  • 5PERONA P,MALIK J. Scale -space and edge detection usinganisotropic diffusion[J].IEEK Transaction on Pattern Analysis andMachine Intelligence, 1990, 12(7): 629—639.
  • 6YU J H, WANG Y Y, SHKN Y Z. Noise reduction and edge dt'teo-tion via kernel anisotropic diffusion[J]. Pattern Recognition Letters,2008, 29(10): 1496—1503.
  • 7CATIE T, LIONS P, MORKL J, et al. Image selective smoothingand edge detection by nonlinear diffusion [J]. Siam Journal on Nu-merical Analysis, 1992, 29: 182—193.
  • 8GUY G,SNIK A S, YEHOSHUA Y Z. Image enhancement and de-noising by complex diffusion process[J]. IEKE Transaction on Pat-tern Analysis and Machine Intelligence, 2004, 29 ( 8) : 1021 —1036.
  • 9DUNNJ C. A fuzzy relative of the ISODATA process and its use indetecting compact well-separated cluster[J]. Journal of Cyberneticsand Systems, 1973, 3(3): 32—57.
  • 10WANG Y X, BU J. A fast and robust image segmentation using FCMwith spatial iuformation[J]. Digital Signal Processing, 2009, 11(7):1 — 10.

引证文献5

二级引证文献6

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