Image segmentation using maximum entropy method

Author

Leung, Chi-kin ; Lam, Fuel-Kit

Author_Institution

Dept. of Electron. Eng., Hong Kong Polytech., Kowloon, Hong Kong

fYear

1994

fDate

13-16 Apr 1994

Firstpage

29

Abstract

Segmentation of a composite image which contains two simple subimages is described. The a-priori knowledge about the two simple subimages is that they possess the maximum amount of entropy. The probability density functions (pdfs) of these image pixels are shown to be of the quasi-Gaussian form. Parameters for the pdf are estimated and then the maximum likelihood ratio test is applied to segmentation. An iterative algorithm is employed to improve the segmentation accuracy. Extension of this method to the segmentation of images with arbitrary pdfs is discussed

Keywords

entropy; image segmentation; iterative methods; maximum likelihood estimation; parameter estimation; composite image; image pixels; iterative algorithm; maximum entropy method; maximum likelihood ratio; probability density functions; quasiGaussian form; segmentation; segmentation accuracy; subimages; Entropy; Image processing; Image segmentation; Maximum likelihood estimation; Neural networks; Pixel; Probability density function; Random variables; Speech processing; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Speech, Image Processing and Neural Networks, 1994. Proceedings, ISSIPNN '94., 1994 International Symposium on

Print_ISBN

0-7803-1865-X

Type

conf

DOI

10.1109/SIPNN.1994.344973

Filename

344973