Title :
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
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;
Conference_Titel :
Speech, Image Processing and Neural Networks, 1994. Proceedings, ISSIPNN '94., 1994 International Symposium on
Print_ISBN :
0-7803-1865-X
DOI :
10.1109/SIPNN.1994.344973
