TEXTURE ANALYSIS BASED ON MAXIMUM POTENTIAL

Zhang Zhan; Hou Jiechang; Liao Mengyang

Volume 21 Issue 3

May 1999

Turn off MathJax

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 1999 > 21(3): 315-319

Zhang Zhan, Hou Jiechang, Liao Mengyang. TEXTURE ANALYSIS BASED ON MAXIMUM POTENTIAL[J]. Journal of Electronics & Information Technology, 1999, 21(3): 315-319.

Citation:

Zhang Zhan, Hou Jiechang, Liao Mengyang. TEXTURE ANALYSIS BASED ON MAXIMUM POTENTIAL[J]. Journal of Electronics & Information Technology, 1999, 21(3): 315-319.

Zhang Zhan, Hou Jiechang, Liao Mengyang. TEXTURE ANALYSIS BASED ON MAXIMUM POTENTIAL[J]. Journal of Electronics & Information Technology, 1999, 21(3): 315-319.

Citation:

Zhang Zhan, Hou Jiechang, Liao Mengyang. TEXTURE ANALYSIS BASED ON MAXIMUM POTENTIAL[J]. Journal of Electronics & Information Technology, 1999, 21(3): 315-319.

PDF( 981 KB)

TEXTURE ANALYSIS BASED ON MAXIMUM POTENTIAL

Received Date: 1997-08-11
Rev Recd Date: 1998-03-18
Publish Date: 1999-05-19

Abstract

Abstract

Potential is an important conception in Gibbs random field (GRF) model. In this paper, an approach for texture analysis based on maximum potential is presented. Experimental results prove that this approach can be an effective means for texture analysis.

FullText(HTML)

References(1)

References

Acuna C O. Texture modeling using Gibbs distributions[J].Graphic Models Image Processing.1992,54(3):210-222[2]Nguyen H H, Cohem P. Clustering and the unsupervised segmentation of texture image[J].Graphic ModelsImage Processing.1993, 55(1):1-19[3]Gurelli M I, Onural L. A parameter estimation method for Gibbs-Markov random field. IEEE Trans. on PAMI, 1994, PAMI-16(4): 424-430.[4]Levitan E, Chan M, Herman G T. Image modeling Gibbs priors[J].Graphic ModelsImage Processing.1995, 57(2):117-130

Relative Articles

Supplements(0)

Cited By

Proportional views